Invented by Catherine Allin, Scott P. Oppliger

The market for self-learning aisle generating systems has been steadily growing in recent years. These innovative systems are designed to generate electricity from the movement of people in public spaces such as shopping malls, airports, and train stations. They utilize advanced technology and algorithms to harness the kinetic energy created by individuals walking or moving through these areas, converting it into usable electrical power. The concept of self-learning aisle generating systems is rooted in the idea of sustainability and renewable energy. As the world becomes more conscious of the need to reduce carbon emissions and transition towards cleaner energy sources, these systems offer a practical solution to harness the energy that is often wasted in high-traffic public spaces. One of the key advantages of self-learning aisle generating systems is their ability to adapt and learn from the patterns of human movement. Through the use of sensors and machine learning algorithms, these systems can analyze the flow of people and optimize their energy generation accordingly. This means that over time, they become more efficient in capturing and converting energy, resulting in higher electricity production. The market for self-learning aisle generating systems is driven by several factors. Firstly, the increasing demand for renewable energy sources has created a favorable environment for the adoption of such systems. Governments and businesses are actively seeking ways to reduce their carbon footprint and meet sustainability goals, making self-learning aisle generating systems an attractive option. Secondly, the advancements in technology and the decreasing costs of sensors and energy conversion components have made these systems more affordable and accessible. This has opened up opportunities for businesses in various sectors to invest in self-learning aisle generating systems as a means of reducing their energy costs and demonstrating their commitment to sustainability. Furthermore, the potential applications of self-learning aisle generating systems are vast. Beyond public spaces, these systems can also be integrated into other environments such as gyms, stadiums, and even sidewalks. This versatility allows for a wide range of potential customers, further driving the growth of the market. In terms of the methods for making and using self-learning aisle generating systems, there are several key considerations. Firstly, the design and installation of the system should be carefully planned to ensure optimal energy capture. This involves strategically placing sensors and energy conversion components in areas with high foot traffic and ensuring that the system is seamlessly integrated into the existing infrastructure. Secondly, the algorithms used in self-learning aisle generating systems play a crucial role in their effectiveness. These algorithms need to be able to accurately analyze and predict human movement patterns, allowing the system to adjust its energy generation accordingly. Ongoing research and development in this area are essential to further improve the efficiency and performance of these systems. Lastly, the maintenance and monitoring of self-learning aisle generating systems are important for their long-term viability. Regular inspections and maintenance checks are necessary to ensure that the system is functioning optimally and to address any issues that may arise. Additionally, data monitoring and analysis can provide valuable insights into energy consumption patterns and help identify areas for further optimization. In conclusion, the market for self-learning aisle generating systems is poised for significant growth in the coming years. With the increasing demand for renewable energy sources and advancements in technology, these systems offer an innovative and sustainable solution for harnessing the energy generated by human movement. As businesses and governments continue to prioritize sustainability, self-learning aisle generating systems will play a crucial role in shaping the future of energy generation.

The Catherine Allin, Scott P. Oppliger invention works as follows

A computer-implemented, self-learning method for generating a personalized virtual aisle using a remote acquisition device associated with an entity includes the step of accessing an image database via an online structure to retrieve a number of images. Each image represents an item that is offered by the entity. The method involves using the online structure in order to scale each image according to the actual size of an item represented by the images. The method involves creating a number of aisle blocks using an online structure and set of rules. The method dynamically groups the plurality aisle blocks according to a history of the patron and the rules in order to create a virtual aisle. The method includes displaying the virtual avenue on an output that is accessible by the patron.

Background for Self-learning aisle generating systems and methods for making and using the same

Numerous entities have brick-and-mortar locations where customers can browse and/or purchase items. Patrons can also browse and/or purchase items remotely from brick-and-mortar locations. A patron can browse and/or purchase items over the network from his or her own home using a remote-acquisition mechanism. The provision of the option to purchase the one or two items from a remote location from the brick-and-mortar location is both viable and trendy. However, entities are not able to maximize the potential of remote acquisition mechanisms, in part because they present the items to patrons using formats (e.g. grids and list) that do not emulate the experience patrons have at brick and morter locations.

The following is a simplified overview of the disclosure to help you understand some of its basic aspects. This summary does not provide a comprehensive overview of the disclosure. This summary is not meant to highlight critical disclosure elements or define the scope of disclosure. Its only purpose is to simplify some concepts in the disclosure as a precursor to the detailed description which is provided elsewhere.

Accordingly, an embodiment of a computer-implemented, self-learning method for generating a personalized virtual aisle through a remote acquisition system associated with an entity includes the step of obtaining a plurality images from an image database by using an online structure. Each image represents a product offered by the entity. The method involves using the online structure in order to scale each image according to the actual size of an item represented by the images. The method involves creating a plurality aisle blocks using an online structure and rules that are specific to the entity. The method creates the virtual aisle by dynamically grouping a plurality of aisles blocks according to the historical records of patrons and the rules. The method includes displaying the virtual avenue on an output that is accessible by the patron. The set of rules contains at least one affinity rule. “The virtual aisle contains at least two physically incompatible items adjacent to each other.

Accordingly, another embodiment is a nontransitory computer-readable medium that contains computer-executable instructions executed by a processor digital to perform the method for generating a virtual aisle personalized via a remote acquistion mechanism associated with an organization. The instructions include instructions to access an image database in order to retrieve a number of images. Each image represents an item that is offered by the entity. The medium contains instructions for scaling the plurality images to match the actual size of each item represented by the images. The medium contains instructions on how to use at least one set of rules that are specific to an entity to create multiple aisle blocks. The medium includes instructions for grouping aisle blocks dynamically based on a patron’s history and the rules set to create a virtual aisle. The medium includes instructions for displaying a virtual aisle in a format that is accessible by the patron.

Accordingly, according to a further embodiment, a computer implemented self-learning method for generating a personalized virtual aisle through a remote acquisition device associated with an organization comprises the step of obtaining a number of images from an image database by using an online structure. Each image represents an item that is offered by the entity. The method involves using the online structure in order to scale each image according to the actual size of an item represented by the images. The method involves creating a number of aisle blocks using an online structure and set of rules. The method dynamically groups the plurality aisle blocks according to a history of the patron and the rules in order to create the virtual avenue. The method includes displaying the virtual avenue on an output that is accessible by the patron.

BRIEF DESCRIPTION ABOUT THE VIEWS FROM THE DRAWINGS

Illustrative embodiments” of the present disclosure will be described below in detail with reference to the drawing figures attached and wherein

FIG. “FIG.

FIG. “FIG.

FIG. “FIG.3 schematically illustrates a self-learning virtual aisle generation system according to an embodiment in the present disclosure.

FIG. “FIG. According to one embodiment, FIG.

FIG. “FIG. “3 according to an embodiment

FIG. FIG. “Detailed description of FIG. 5;

FIGS. “FIGS. 5;

FIG. “FIG. 3;

FIG. “FIG. 3;

FIG. “FIG. According to one embodiment, 3 is used to create a virtual aisle.

FIG. “FIG.

Many entities offer goods and/or service through electronic media. Target, Wal-Mart and Macy’s all offer remote acquisition mechanisms, which patrons can utilize to browse or purchase items remotely from their physical locations. The physical space of an entity is often limited and it may not be possible to display all the items that are available. Remote acquisition mechanisms enable entities to offer their customers more products, including those that aren’t available in the brick-and-mortar storefront.

Amazon is one of the largest entities in the world. Its remote acquisition mechanism can be accessed by customers via Amazon.com. As used herein the term entity refers to any institution, company, partnership or other organization that allows patrons of the entity to purchase one or more products in exchange for another thing (e.g. cash, bitcoins, etc.). As used herein the phrase remote acquisition mechanisms refers to an application, website or other virtual portal that is associated with an entity and allows patrons browse through or acquire one or multiple items from a remote location. Amazon’s remote acquisition tool allows patrons to find and purchase a wide variety of products. Amazon has a search box where the customer can enter the item or item type he is looking to purchase. The Amazon website displays the results as a grid. The patron must typically scroll down to see all of the items on the page. The grid can span hundreds of pages.

Click here to view the patent on Google Patents.