Description:BACKGROUND
Field of Invention
[001] Embodiments of the present invention generally relate to a marketing management system and particularly to a system for optimizing sales using artificial intelligence.
Description of Related Art
[002] In recent years, market environments have undergone substantial transformation due to technological advancement, digitization of commerce, and greater access to real-time data. Enterprises now encounter complex pricing structures, rapid demand shifts, and an ever-increasing volume of competitors offering similar products or services. The traditional manual methods of price evaluation and sales planning no longer deliver consistent results due to limited adaptability and a lack of precision in high-volume, fast-paced markets.
[003] Existing market price prediction tools and sales optimization systems, such as dynamic pricing engines, sales forecasting platforms, and AI-assisted analytics tools, provide partial assistance. These systems typically depend on historical data, consumer segmentation, and rule-based algorithms. While their integration into commercial practice has seen success in select sectors, their effectiveness diminishes under highly volatile market conditions or when faced with insufficient or skewed data inputs.
[004] Despite ongoing development in artificial intelligence and data science, limitations continue to exist in current solutions. These include data bias, lack of scalability, poor interoperability with legacy systems, and inadequate handling of customer behavior complexity.
[005] There is thus a need for an improved and advanced system for optimizing sales using artificial intelligence that can administer the aforementioned limitations in a more efficient manner.
SUMMARY
[006] Embodiments in accordance with the present invention provide a system for optimizing sales using artificial intelligence. The system comprising a user interface, established in a computing device, adapted to enable a user to input data of a marketable object. The system further comprising a processing unit communicatively connected to the computing device. The processing unit is configured to fetch external data related to the marketable object from social media platforms, financial datasets, web-based sources, or a combination thereof; preprocess the fetched external data using a preprocessing engine; evaluate a sentiment score by performing sentiment analysis on the preprocessed external data using a machine learning model selected from an Extreme Learning Machine (ELM), a Recurrent Neural Network (RNN), or a combination thereof; categorize the preprocessed external data into preset categories based on the evaluated sentiment score. The preset categories are selected from a positive category, a neutral category, or a negative category; generate a market trend for the marketable object based on the selected category and a historical trend data. The historical trend data is fetched from a training dataset of the machine learning model; predict a price of the marketable object using a prediction engine based on the generated market trend; and trigger an influence engine to improve the evaluated sentiment score of the marketable object to optimize the sales of the marketable object.
[007] Embodiments in accordance with the present invention further provide a method for optimizing sales using artificial intelligence. The method comprising steps of enabling a user to input data of a marketable object; fetching external data related to the marketable object from social media platforms, financial datasets, web-based sources, or a combination thereof; preprocessing the fetched external data using a preprocessing engine; evaluating a sentiment score by performing sentiment analysis on the preprocessed external data using a machine learning model selected from an Extreme Learning Machine (ELM), a Recurrent Neural Network (RNN), or a combination thereof; categorizing the preprocessed external data into preset categories based on the evaluated sentiment score. The preset categories are selected from a positive category, a neutral category, or a negative category; generating a market trend for the marketable object based on the selected category and a historical trend data. The historical trend data is fetched from a training dataset of the machine learning model; predicting a price of the marketable object using a prediction engine based on the generated market trend; and triggering an influence engine to improve the evaluated sentiment score of the marketable object to optimize the sales of the marketable object.
[008] Embodiments of the present invention may provide a number of advantages depending on their particular configuration. First, embodiments of the present application may provide a system for optimizing sales using artificial intelligence.
[009] Next, embodiments of the present application may provide a system for optimizing sales that offers instant analysis of market trends, competitor prices, and consumer patterns, enabling businesses to make timely and informed pricing decisions.
[0010] Next, embodiments of the present application may provide a system for optimizing sales that suggests optimized prices for products based on historical data, current demand, and competition, reducing manual effort and increasing accuracy.
[0011] Next, embodiments of the present application may provide a system for optimizing sales that improves forecasting precision, which leads to better inventory planning and revenue management.
[0012] Next, embodiments of the present application may provide a system for optimizing sales that incorporates consumer behavior analytics to help businesses tailor their sales and pricing strategies, thereby improving customer satisfaction and loyalty.
[0013] Next, embodiments of the present application may provide a system for optimizing sales that supports integration with existing enterprise systems and scales efficiently across various business sizes and industries, offering long-term adaptability.
[0014] These and other advantages will be apparent from the present application of the embodiments described herein.
[0015] The preceding is a simplified summary to provide an understanding of some embodiments of the present invention. This summary is neither an extensive nor an exhaustive overview of the present invention and its various embodiments. The summary presents selected concepts of the embodiments of the present invention in a simplified form as an introduction to the more detailed description presented below. As will be appreciated, other embodiments of the present invention are possible utilizing, alone or in combination, one or more of the features set forth above or described in detail below.
BRIEF DESCRIPTION OF THE DRAWINGS
[0016] The above and still further features and advantages of embodiments of the present invention will become apparent upon consideration of the following detailed description of embodiments thereof, especially when taken in conjunction with the accompanying drawings, and wherein:
[0017] FIG. 1 illustrates a schematic of a system for optimizing sales using artificial intelligence, according to an embodiment of the present invention;
[0018] FIG. 2 illustrates a block diagram of a processing unit, according to an embodiment of the present invention; and
[0019] FIG. 3 depicts a flowchart of a method for optimizing sales using artificial intelligence, according to an embodiment of the present invention.
[0020] The headings used herein are for organizational purposes only and are not meant to be used to limit the scope of the description or the claims. As used throughout this application, the word "may" is used in a permissive sense (i.e., meaning having the potential to), rather than the mandatory sense (i.e., meaning must). Similarly, the words “include”, “including”, and “includes” mean including but not limited to. To facilitate understanding, like reference numerals have been used, where possible, to designate like elements common to the figures. Optional portions of the figures may be illustrated using dashed or dotted lines, unless the context of usage indicates otherwise.
DETAILED DESCRIPTION
[0021] The following description includes the preferred best mode of one embodiment of the present invention. It will be clear from this description of the invention that the invention is not limited to these illustrated embodiments but that the invention also includes a variety of modifications and embodiments thereto. Therefore, the present description should be seen as illustrative and not limiting. While the invention is susceptible to various modifications and alternative constructions, it should be understood, that there is no intention to limit the invention to the specific form disclosed, but, on the contrary, the invention is to cover all modifications, alternative constructions, and equivalents falling within the scope of the invention as defined in the claims.
[0022] In any embodiment described herein, the open-ended terms "comprising", "comprises”, and the like (which are synonymous with "including", "having” and "characterized by") may be replaced by the respective partially closed phrases "consisting essentially of", “consists essentially of", and the like or the respective closed phrases "consisting of", "consists of”, the like.
[0023] As used herein, the singular forms “a”, “an”, and “the” designate both the singular and the plural, unless expressly stated to designate the singular only.
[0024] FIG. 1 illustrates a schematic of a system 100 for optimizing sales using artificial intelligence, according to an embodiment of the present invention. In an embodiment of the present invention, the system 100 may be adapted to analyze a market trend of a marketable object. The marketable object may be, but not limited to, an article, merchandise, a service, a share, an initial public offering (IPO), a product, a commodity, a digital good, a cryptocurrency, a software license, a subscription, real estate, a rental item, a leasing agreement, a bond, a security, intellectual property, a patent, a copyright, a trademark, a data package, a ticket, an insurance policy, a financial instrument, a contract, a collectible item, a utility token, a non-fungible token (NFT), so forth. Embodiments of the present invention are intended to include or otherwise cover any marketable object, including known, related art, and/or later developed technologies.
[0025] The system 100 may further be adapted to analyze a marketability and/or a demographic response for the marketable object. The system 100 may further be adapted to predict a price of the marketable object based upon the conducted analysis. The system 100 may be adapted to source information for analysis from sources such as, but not limited to, a web, a brochure, a periodical sales report, a cohort of users, and so forth. Embodiments of the present invention are intended to include or otherwise cover any source of the information for analysis by the system 100, including known, related art, and/or later developed technologies.
[0026] According to the embodiments of the present invention, the system 100 may incorporate non-limiting hardware components to enhance the processing speed and efficiency such as the system 100 may comprise a computing device 102, a user interface 104, a processing unit 106, a preprocessing engine 108, a training dataset 110, a prediction engine 112, an influence engine 114, and a volatility engine 116. In an embodiment of the present invention, the hardware components of the system 100 may be integrated with computer-executable instructions for overcoming the challenges and limitations of the existing systems.
[0027] In an embodiment of the present invention, the computing device 102 may be an electronic device used by a user. The computing device 102 may comprise the user interface 104. The user interface 104 may enable the user to input data for the marketable object. The input data of the marketable object may be, but not limited to, stock price feeds, customer reviews, market announcements, real-time social media activity, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the input data, including known, related art, and/or later developed technologies. The computing device 102 may be, but not limited to, a laptop, a smartphone, a desktop, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the computing device 102, including known, related art, and/or later developed technologies.
[0028] In an embodiment of the present invention, the processing unit 106 may be connected to the computing device 102. The processing unit 106 may further be configured to execute computer-executable instructions to generate an output relating to the system 100. The processing unit 106 may be, but not limited to, a Programmable Logic Control (PLC) unit, a microprocessor, a development board, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the processing unit 106, including known, related art, and/or later developed technologies. In an embodiment of the present invention, the processing unit 106 may further be explained in conjunction with FIG. 2.
[0029] FIG. 2 illustrates a block diagram of the processing unit 106, according to an embodiment of the present invention. In an embodiment of the present invention. The processing unit 106 may comprise the computer-executable instructions in form of programming modules such as a data fetching module 200, a data preprocessing module 202, a data evaluation module 204, a data generation module 206, and a data optimization module 208.
[0030] In an embodiment of the present invention, the data fetching module 200 may be configured to fetch external data related to the marketable object from social media platforms, financial datasets, web-based sources, and so forth. The data fetching module 200 may further be configured to transmit the fetched external data to the data preprocessing module 202.
[0031] The data preprocessing module 202 may be activated upon receipt of the fetched external data from the data fetching module 200. In an embodiment of the present invention, the data preprocessing module 202 may be configured to deploy the preprocessing engine 108. The preprocessing engine 108 may be configured to preprocess the fetched external data. The preprocessing may remove noise, remove spam content, remove irrelevant entries, and so forth from the fetched external data. The data preprocessing module 202 may further be configured to transmit the preprocessed external data to the data evaluation module 204.
[0032] The data evaluation module 204 may be activated upon receipt of the preprocessed external data from the data preprocessing module 202. In an embodiment of the present invention, the data evaluation module 204 may be configured to evaluate a sentiment score by performing sentiment analysis on the preprocessed external data using a machine learning model. The machine learning model may be, but not limited to, an Extreme Learning Machine (ELM), a Recurrent Neural Network (RNN), a Natural Language Processing (NLP), and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the machine learning model, including known, related art, and/or later developed technologies.
[0033] Further, the data evaluation module 204 may be configured to categorize the preprocessed external data into preset categories based on the evaluated sentiment score. The preset categories may be, but not limited to, a positive category, a neutral category, a negative category, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the preset categories, including known, related art, and/or later developed technologies.
[0034] The data evaluation module 204 may further be configured to transmit the categorized preprocessed external data to the data generation module 206.
[0035] The data generation module 206 may be activated upon receipt of the categorized preprocessed external data from the data evaluation module 204. In an embodiment of the present invention, the data generation module 206 may be configured to generate a market trend for the marketable object. The market trend may be generated using the selected category of the preprocessed external data and a historical trend data.
[0036] The historical trend data may be fetched from the training dataset 110 of the machine learning model. According to embodiments of the present invention, the trained dataset 110 may be for example, but not limited to, a distributed dataset, a personal dataset, an end-user dataset, a commercial dataset, a Structured Query Language (SQL) dataset, a non-SQL dataset, an operational dataset, a relational dataset, an object-oriented dataset, a graph dataset, and so forth. In a preferred embodiment of the present invention, the trained dataset 110 may be a cloud dataset. Embodiments of the present invention are intended to include or otherwise cover any type of the trained dataset 110, including known, related art, and/or later developed technologies. Further, the trained dataset 110 may be stored in a cloud server, in an embodiment of the present invention. The cloud server may be, but not limited to, a Microsoft Azure cloud server, an Amazon AWS cloud server, a Google Compute Engine (GCE) cloud server, an Amazon Elastic Compute Cloud (EC2) cloud server, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the cloud server, including known, related art, and/or later developed technologies.
[0037] Further, the data generation module 206 may be configured to deploy the prediction engine 112. The prediction engine 112 may be adapted to predict the price of the marketable object based on the generated market trend. The predicted price of the marketable object may further be transmitted to the data optimization module 208.
[0038] The data optimization module 208 may be activated upon receipt of the predicted price of the marketable object from the data generation module 206. In an embodiment of the present invention, the data optimization module 208 may be configured to trigger the influence engine 114. The influence engine 114 may be configured to improve the evaluated sentiment score of the marketable object to optimize the sales of the marketable object.
[0039] The data optimization module 208 may further be configured to trigger the influence engine 114. The influence engine 114 may be configured to generate actionable insights and recommendations. The generated actionable insights and the recommendations may optimize the sales of the marketable object. The actionable insights may be, but not limited to, price forecasts, demand patterns, and suggested inventory levels, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the actionable insights, including known, related art, and/or later developed technologies.
[0040] The data optimization module 208 may further be configured to trigger the volatility engine 116. The volatility engine 116 may be configured to visualize a market volatility of the marketable object. The market volatility may be visualized using infographics such as, but not limited to, graphs, tables, charts, snippets, and so forth. Embodiments of the present invention are intended to include or otherwise cover any type of the infographics, including known, related art, and/or later developed technologies.
[0041] FIG. 3 depicts a flowchart of a method 300 for optimizing sales using the artificial intelligence using the system 100, according to an embodiment of the present invention.
[0042] At step 302, the system 100 may enable the user to input data of the marketable object.
[0043] At step 304, the system 100 may fetch the external data related to the marketable object from the social media platforms, the financial datasets, the web-based sources, and so forth.
[0044] At step 306, the system 100 may preprocess the fetched external data using the preprocessing engine 108.
[0045] At step 308, the system 100 may evaluate the sentiment score by performing the sentiment analysis on the preprocessed external data using the machine learning model.
[0046] At step 310, the system 100 may categorize the preprocessed external data into the preset categories based on the evaluated sentiment score.
[0047] At step 312, the system 100 may generate the market trend for the marketable object based on the selected category and the historical trend data.
[0048] At step 314, the system 100 may predict the price of the marketable object using the prediction engine 112 based on the generated market trend.
[0049] At step 316, the system 100 may triggering the influence engine 114 to improve the evaluated sentiment score of the marketable object to optimize the sales of the marketable object.
[0050] While the invention has been described in connection with what is presently considered to be the most practical and various embodiments, it is to be understood that the invention is not to be limited to the disclosed embodiments, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the scope of the appended claims.
[0051] This written description uses examples to disclose the invention, including the best mode, and also to enable any person skilled in the art to practice the invention, including making and using any devices or systems and performing any incorporated methods. The patentable scope of the invention is defined in the claims, and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if they include equivalent structural elements within substantial differences from the literal languages of the claims. , Claims:CLAIMS
I/We Claim:
1. A system (100) for optimizing sales using artificial intelligence, the system (100) comprising:
a user interface (104), established in a computing device (102) adapted to enable a user to input data of a marketable object; and
a processing unit (106) communicatively connected to the computing device (102), characterized in that the processing unit (106) is configured to:
fetch external data related to the marketable object from social media platforms, financial datasets, web-based sources, or a combination thereof;
preprocess the fetched external data using a preprocessing engine (108);
evaluate a sentiment score by performing sentiment analysis on the preprocessed external data using a machine learning model selected from an Extreme Learning Machine (ELM), a Recurrent Neural Network (RNN), or a combination thereof;
categorize the preprocessed external data into preset categories based on the evaluated sentiment score, wherein the preset categories are selected from a positive category, a neutral category, or a negative category;
generate a market trend for the marketable object based on the selected category and a historical trend data, wherein the historical trend data is fetched from a training dataset (110) of the machine learning model;
predict a price of the marketable object using a prediction engine (112) based on the generated market trend; and
trigger an influence engine (114) to improve the evaluated sentiment score of the marketable object to optimize the sales of the marketable object.
2. The system (100) as claimed in claim 1, wherein the influence engine (114) is configured to generate actionable insights and recommendations for optimizing the sales of the marketable object.
3. The system (100) as claimed in claim 1, wherein the processing unit (106) is configured to visualize market volatility of the marketable object using a volatility engine (116).
4. The system (100) as claimed in claim 1, wherein the sentiment analysis is performed using a Natural Language Processing (NLP).
5. The system (100) as claimed in claim 1, wherein the input data is selected from stock price feeds, customer reviews, market announcements, real-time social media activity, or combination thereof.
6. The system (100) as claimed in claim 1, wherein the preprocessing engine (108) is adapted to remove noise, remove spam content, remove irrelevant entries, or a combination thereof from the fetched external data.
7. A method (300) for optimizing sales using artificial intelligence, the method (300) is characterized by steps of:
enabling a user to input data of a marketable object;
fetching external data related to the marketable object from social media platforms, financial datasets, web-based sources, or a combination thereof;
preprocessing the fetched external data using a preprocessing engine (108);
evaluating a sentiment score by performing sentiment analysis on the preprocessed external data using a machine learning model selected from an Extreme Learning Machine (ELM), a Recurrent Neural Network (RNN), or a combination thereof;
categorizing the preprocessed external data into preset categories based on the evaluated sentiment score, wherein the preset categories are selected from a positive category, a neutral category, or a negative category;
generating a market trend for the marketable object based on the selected category and a historical trend data, wherein the historical trend data is fetched from a training dataset (110) of the machine learning model;
predicting a price of the marketable object using a prediction engine (112) based on the generated market trend; and
triggering an influence engine (114) to improve the evaluated sentiment score of the marketable object to optimize the sales of the marketable object.
8. The method (300) as claimed in claim 7, wherein the influence engine (114) is configured to generate actionable insights and recommendations for optimizing the sales of the marketable object.
9. The method (300) as claimed in claim 7, wherein the input data is selected from stock price feeds, customer reviews, market announcements, real-time social media activity, or a combination thereof.
10. The method (300) as claimed in claim 7, wherein the sentiment analysis is performed using natural language processing (NLP) techniques.
Date: June 03, 2025
Place: Noida

Nainsi Rastogi
Patent Agent (IN/PA-2372)
Agent for the Applicant