Systems and methods for predicting and scoring future success of a business and / or product

Abstract

The system and method predicts the future success of companies and / or products based on past popularity and performance data. The system and method analyze, score, and rank companies or products based on how likely they are to succeed based on past and current popularity, consumer sentiment, sales, trends, ethicality, and financial health data. A computer-implemented method for replacing a selected product with a product having a higher attribute or sustainability score analyzes the selected product to determine its score, finds products similar to the selected product, determines a sustainability score for each similar product, and then ranks the similar products by similarity and corresponding sustainability score. One of the highest-ranked similar products may then be replaced with the selected product if its sustainability score is higher than the sustainability score of the selected product.

Description

[Technical Field]

[0001] Related Applications This patent claims priority to and benefit of U.S. Provisional Patent Application No. 63 / 436,465, filed December 30, 2022. All documents cited in this section are incorporated herein by reference in their entirety.

[0002] The present disclosure relates to predicting the future success of a business or product using past popularity and performance data, and more particularly to systems and methods for predicting the future success of a particular business or product using analysis, scores and rankings for likelihood of success in terms of future popularity and sales, and is particularly suited to online shopping and product recommendations, as well as retail, commerce and financial technologies. [Background technology]

[0003] Many shoppers are frequently motivated to purchase products made with certain characteristics and / or by companies / producers that align with the specific characteristics they wish to support. For example, shoppers frequently select products based on the perceived moral and / or ethical values ​​of the product and / or the product's producer. For example, some shoppers may choose to support women-owned businesses or ingredients grown on women-owned farms. Other shoppers may wish to support sustainable products and ingredients and therefore make purchasing decisions based on the level of sustainability of the product and / or ingredients and / or the manufacturer / company itself. In addition, businesses and consumers often make purchasing decisions based on the perceived or anticipated popularity of a product or company, which may also be related to the aforementioned characteristics.

[0004] However, determining the detailed characteristics of the ingredients in a product, and / or how that product is made, and / or who produces that product, as well as the performance, facility locations, product shipping distances, etc., can be extremely time-consuming and difficult for users to determine for each product or service they wish to purchase or company they wish to do business with. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] US Patent Application Publication No. 2018 / 0130072 [Patent Document 2] US Patent Application Publication No. 2013 / 0311216 Summary of the Invention

[0006] Systems and methods are disclosed for providing users with targeted information based on their own preferences, including at the point of sale. Systems and methods are disclosed for suggesting products and / or businesses that satisfy a user's own preferences. Systems and methods are also disclosed for online e-commerce shopping with product and business recommendations, and for suggesting alternatives, such as exchanges for the same, at checkout.

[0007] One embodiment of the present disclosure provides a method and / or system for determining the future popularity and likelihood of success of companies and products. Analysis and ranking may be based on past and current popularity, consumer sentiment, sales, trends, ethics, and financial health data. This includes gaining insights into past and future performance by collecting, inputting, and analyzing data related to predetermined metrics, including consumer sentiment, sales, industry and cultural trends, ethics, financial health, and other related data. Data may be collected through ancillary touchpoints, including those provided through social media, mobile applications, web applications, dynamic ads, and communications such as email. Based on the data and analysis related to a given product or company, users of the system are presented with a performance or "breakthrough" score that predicts and ranks how likely a given company or product is to be popular or financially successful in a specific geographic market, industry, or category in the future. The breakthrough score can also be used to provide a ranked list of the most desirable companies or products across a selected field.

[0008] Another embodiment of the present disclosure includes a method and / or system for exchanging a selected product for a more popular product or a product that is in line with a particular user's own preferences, e.g., a product with a higher sustainability score. The method includes: analyzing the selected product to determine its desirability, e.g., its sustainability score; finding a replacement product, where finding a replacement product includes identifying products similar to the selected product, where similarity is determined by factors such as characteristics, quality, and capabilities; determining the desirability of the replacement product by comparing attribute scores (e.g., sustainability scores) of those products similar to the selected product; ranking the products similar to the selected product for exchange by both similarity and sustainability score; selecting a replacement product from among the products similar to the selected product, where the replacement product has a high similarity to the selected product and a higher sustainability score than the selected product; presenting the replacement product; and exchanging the selected product for the replacement product.

[0009] These aspects of the present disclosure are not intended to be exclusive, and other features, aspects, and advantages of the present disclosure will become readily apparent to those of ordinary skill in the art when read in conjunction with the following description, the appended claims, and the accompanying drawings. [Brief explanation of the drawings]

[0010] Various aspects of at least one embodiment are discussed below with reference to the accompanying drawings, which are not necessarily drawn to scale, with emphasis instead being placed on illustrating the principles disclosed herein. The drawings are included to provide illustration and a further understanding of the various aspects and embodiments, and are incorporated into and constitute a part of this specification, but are not intended as a definition of the scope of any particular embodiment. The drawings, together with the remainder of the specification, serve only to explain the principles and operation of the aspects and embodiments described and claimed, but should not be construed as limiting the embodiments. In the drawings, each identical or nearly identical component shown in various figures is represented by a like numeral. For purposes of clarity, not every component is shown in every figure.

[0011] [Figure 1] FIG. 1 illustrates a system according to one embodiment. [Figure 2] FIG. 10 illustrates attribute scores according to one embodiment. [Figure 3] FIG. 1 illustrates a breakthrough score according to one embodiment. [Figure 4] 1 is a flowchart of a method for recommending a replacement product. [Figure 5] FIG. 1 illustrates a method for a user to exchange a selected product for a replacement product, according to one embodiment. [Figure 6] FIG. 1 is an object diagram for reviewing and voting for a brand / product according to one embodiment. [Figure 7] 1 is a flowchart of a method for calculating a score according to one embodiment. [Figure 8] FIG. 1 is an object diagram for scoring according to one embodiment. [Figure 9] FIG. 10 is an object diagram for an alternative engine according to one embodiment. [Figure 10] FIG. 1 is an object diagram of a system for scoring. DETAILED DESCRIPTION OF THE INVENTION

[0012] In various embodiments, the system is a Software as a Service (SaaS) platform that provides systems and methods for delivering information about products or companies based on characteristics that shoppers or investors define as important in making a purchasing decision. For example, in some embodiments, the platform may include information about sustainability or the future success of a product or company. The term "sustainability" refers to a holistic approach that considers various impacts, including at least one of the social, environmental, and / or economic impacts of today's actions and decisions on the future. However, in other embodiments, these characteristics are not limited to sustainability and / or success, and may include one or more of: ethically sourced; ethically sourced; women- and / or minority-owned businesses; ingredients from preferred regions of the world; popularity; product availability; economic viability; and / or other desirable characteristics.

[0013] Various embodiments are described below with reference to block diagrams and flowchart illustrations of methods, apparatus (e.g., systems), and computer program products. Each block of the block diagrams and flowchart illustrations, and combinations of blocks in the block diagrams and flowchart illustrations, respectively, may be implemented by a computer executing computer program instructions. These computer program instructions may be loaded into a general-purpose computer, a special-purpose computer, or other programmable data processing apparatus, such that the instructions are executed on the computer or other programmable data processing apparatus to implement the functions specified in the flowchart blocks.

[0014] Therefore, the blocks in the block diagrams and flowchart diagrams support combinations of mechanisms for performing the specified functions, combinations of steps for performing the specified functions, and program instructions for performing the specified functions. It should also be understood that each block in the block diagrams and flowchart diagrams, and combinations of blocks in the block diagrams and flowchart diagrams, are not limited to the order in which they are presented.

[0015] In various embodiments, the analysis and ranking of companies is based on past and current popularity, consumer sentiment, sales, trends, ethics, and financial health data. Past performance data for a given product or company is compared against key success indicators to predict its future popularity and likelihood of financial success. Popularity data includes, but is not limited to, consumer sentiment and brand awareness data, social media presence in terms of reach, engagement, content quality, and regularity. Sales and financial health data includes, but is not limited to, past revenue, financial records, and accounting. The platform may additionally provide a single source for product reviews, analysis, sourcing statements, and product and / or company showcases.

[0016] In various embodiments, the platform provides a single source for product reviews, analysis, sourcing statements, and product showcases. In various embodiments, the provided platform allows companies offering products to engage with users, respond to users, add more "color," and provide information at the point of engagement. Additionally, in various embodiments, the platform allows companies to add discount codes, links, etc., automate or send invitations to promotions, and provide one or more widgets that leverage user attention and feed comments into the platform. Also, in various embodiments, the platform allows companies to review benchmarking with competitors and show how the company is doing compared to competitors. In some embodiments, the platform provides the ability to publish statements, for example, to normalize sustainability strategies (or any strategies related to attributes that consumers have expressed interest in). For example, if a user is reviewing the ingredients of a product, in various embodiments, the platform will include the company's sustainability statement (i.e., where the ingredients are listed) at that time. This may be beneficial / desirable for many reasons, including, but not limited to, allowing a user to read about a product and also review a company's statements regarding the sustainability of its ingredients and / or product, thereby allowing the user to make an informed decision regarding purchasing the product based at least in part on the sustainability of the ingredients in the product and / or the sustainability of the product as a whole (or any attributes that are important to the user).

[0017] In various embodiments, the platform includes a product swap feature. In these embodiments, any product that the user indicates interest in, and in some embodiments, while reviewing items saved in the user's "shopping cart" or otherwise, suggested products that have high scores on attributes that the consumer cares about most (e.g., sustainability, minority-owned, small business, etc.), are displayed, and the user may "swap" items in the cart for the suggested products (i.e., products with high scores). In one embodiment, the system disclosed herein includes identifying and swapping high ecological impact products for low ecological impact products to quickly reach the user's goals. More details regarding the swap feature are provided below.

[0018] In various embodiments, the platform also includes a breakthrough score. The breakthrough score is a score indicative of the predicted success of any given product and / or company. The breakthrough score may be determined by the platform after analyzing data related to predetermined metrics including, for example, consumer sentiment, sales, industry and cultural trends, ethics, financial health, etc. The breakthrough score provides a product provider / company / investor with a predicted assessment of success. Additional details regarding the breakthrough score are discussed below.

[0019] In various embodiments, the disclosed system discovers and / or targets desired audiences through demographic and psychographic insights, helping companies / merchants / businesses / investors expand their reach, find new influencers, and become highly relevant in new verticals, as described in more detail below.

[0020] The system disclosed herein also centralizes, for example, billions of social, blog, news, follower, and business data points through integration. In various embodiments, this includes both real-time and historical data. The system normalizes into a single data model and unlocks by enriching and bringing context to human data. In various aspects, the system mines the most relevant insights, classifies and reveals hidden patterns, and delivers them to users via APIs or the WF online platform. This system is useful / desirable for many reasons, including, but not limited to, the fact that, when used, the system allows users to discover new products and companies and their potential for success on store shelves or in general, and / or the system connects hundreds of thousands of people on the platform, from consumers to brands and certification authorities.

[0021] In one exemplary embodiment disclosed herein, the platform, simply referred to as the "platform," is an example of a platform that provides systems and methods for delivering information about products or companies based on characteristics defined by shoppers or investors as important in making purchasing decisions. In one embodiment, the platform may include information about desirable characteristics / attributes, which may include sustainability or other desirable product attributes, such as geographic sourcing, minority ownership, innovative products, chemical-free, or any number of other desirable characteristics. In yet another embodiment, the platform may include information about the company and the future success of the product or the company as a whole.

[0022] The platform bridges the gap between people, brands, retailers, and certification organizations using the most comprehensive dataset on sustainability credentials, sustainability sentiment, and / or other pre-defined desirable product characteristics. In some embodiments, the platform is a sustainability review and education platform for consumers. In other embodiments, the platform is a sustainability communication platform for brands, and in some embodiments, a data intelligence platform for retailers. Similarly, in some embodiments, the platform is a review and education platform for consumers, investors, and others to share and obtain information about companies and their products (including their current and potential success). In other embodiments, the platform is a communication platform for companies and brands, and in some embodiments, a data intelligence platform for investors.

[0023] Similarly, the platform, in some embodiments, is a networking tool for accreditation bodies, certification bodies, and assessors to find new companies to partner with. In some embodiments, the platform includes many additional benefits and is desirable for many reasons, including, but not limited to, as a platform, method, and / or system, for businesses to collaboratively develop sustainability strategies with customers and / or selectively develop and share with users their expected future value and the value of their products.

[0024] In many embodiments, the platform is a tool for retailers, buyers, investors, and other stakeholders to discover which products and / or companies are most likely to be successful. The granularity of the data allows retailers to target specific types of shoppers, specific types of products, and gain deep insights into consumer trends and sentiment regarding sustainability and other desired topics.

[0025] This disclosure includes examples and references to specific characteristics that users may wish to prioritize when shopping, etc. These are intended as exemplary embodiments for purposes of disclosure and are not intended to limit the scope of the systems and methods disclosed herein. For purposes of disclosure, the systems and methods disclosed below with respect to attributes are disclosed with reference to sustainable or ethical product characteristics. However, the systems and methods disclosed herein may be used with respect to any desired characteristics of any product and / or company.

[0026] 1 , the system 100 includes a user interface 102 that provides an interface for a user to access a computer network 107 that includes one or more servers 804, 808 ( FIG. 8 ) that receive, analyze, store, and share data as described further herein. Data stored on the servers 804, 808 includes, but is not limited to, product attribute scores 104 (e.g., sustainability scores), breakthrough scores 106, product listings 108, and product reviews 110. The user interface 102 may include, for example, a monitor or screen and input devices such as a mouse, keyboard, or touch screen, as known to those skilled in the art for accessing the computer network 107.

[0027] 2 , the attribute score 104, in this embodiment, is a score that includes a numerical representation of several factors that contribute to the sustainability and / or ethicality of a product and / or the company / product manufacturer. The attribute score 104 is determined using a number of variables, including, but not limited to, the product's company / source score 200 and the product score 212 itself. The product's company / source 200 similarly includes a number of variables, including, but not limited to, company for good™ rating 202, transparency 204, social / environmental and / or economic impact 206, inventory 208, and source packaging 210, as well as other desired characteristics. The product score 212 includes a number of variables, including, but not limited to, ingredients 214, product packaging 216, health 218, and quality 220, as well as other desired characteristics.

[0028] The term sustainability is a multifaceted expression of many diverse factors that consider various impacts, including at least one of the social, environmental, and / or economic impacts, of today's actions and decisions on the future. Examples of these include, but are not limited to: 1) the ecological and environmental impacts and / or liabilities a business / enterprise generates in the course of its operations and the production and distribution of its products and services. This may include one or more of carbon emissions, energy usage, water usage, materials, and / or other related markets, and in various embodiments may be industry-specific; 2) how a company interacts with its employees and / or those operating within its supply chain, and the communities in which it operates; and 3) how a company governs and manages itself. This may include everything from how transparent a company is in its operations to the distribution of rights and responsibilities among various participants within the company, including the board of directors, management, shareholders, and stakeholders. The factors used to score the wherefrom or company score 104 may also be weighted as desired, with important criteria being given greater weight than other less important criteria. As will be appreciated, the factors considered to arrive at a product and / or company score can be tailored to particular circumstances and the weighting of criteria that are most important to those circumstances, for example, using Wherefrom or CompanyPro or other platforms.

[0029] In various embodiments, the attribute score 104 is based on crowdsourced reviews. In this sense, it is a sentiment score, i.e., a score representing the opinions of multiple customers / shoppers (also called "consumers") when asked how they rate a given company or product. It is thus an expression of the general sentiment or impression of customers / shoppers in the marketplace regarding the level of sustainability (or other criteria) of a particular product or company. In various embodiments, the product score 212 may use the company score 200 as one factor to create the product score 212, while the company score 200 may also take into account the average scores of the products 212 manufactured by that company. In various embodiments, this may be achieved by using a ratio of each. Thus, in some embodiments, the company score may be composed of a blend of 40% company votes and 60% product votes. The product score, in various embodiments, may be composed of 50% company votes and 50% product votes. Again, the weights or percentages are determined by the attributes that are most important to the situation and / or use of the data.

[0030] Company score 200 may be determined using several factors, including, but not limited to, company for good™ rating 202, transparency 204, social 206, availability 208, and source packaging 210. In various embodiments, company for good™ 202 is a measure of the extent to which consumers / shoppers trust that the company in question has a clear social and environmental mission and is attempting to make a positive impact on people and the planet. Transparency 204, in various embodiments, refers to a measure of how transparent a customer / shopper believes a brand / company is in its sustainability and / or other reporting. Social 206, in various embodiments, refers to a measure of the extent to which a consumer trusts a brand to ensure good working rights, conditions and benefits for employees, interaction with the community, etc. Availability 208, in various embodiments, refers to the general level of sustainability a user / customer / shopper believes a particular company typically stocks and offers. In some embodiments, source packaging 210 refers to how sustainable a user / customer / shopper generally considers a brand or company's packaging to be.

[0031] With respect to product score 212, this may include factors such as ingredients 214, product packaging 216, health benefits 218, and product quality 220, as well as other factors as desired, such as pricing / value. Ingredients 214 in one example refers to a measure of how much a user / customer / shopper trusts a product's ingredients and materials are sustainably sourced and / or the quality of the ingredients. Product packaging 216 in one example refers to the user / customer / shopper's perceived level of sustainability of the product's packaging 216 and / or a measure of the overall quality of the packaging and branding. Quality 220 refers to a measure of the overall quality of the product to the user / customer / shopper, and pricing refers to the actual price of the product as well as its value.

[0032] In various embodiments, sustainability / ethical opinions / ratings for a company 200 or product 212 are collected from multiple sources. These sources may include, but are not limited to, partnerships with data licensees, including accreditation bodies, certifications, and associations; the companies themselves (i.e., data provided by the companies being rated); web scraping (i.e., information from various data pipelines on the internet); and people (i.e., directly from users / customers / consumers / shoppers themselves, and even users of attribute scores 104). In various embodiments, these sources are updated periodically, and in some embodiments, are continuously updated, aggregated, and normalized.

[0033] Referring now also to FIG. 3 , in various embodiments, a breakthrough score 300 is determined for each product. The breakthrough score is an indicator of a product's predicted success. The breakthrough score 300 is determined, for example, by using proprietary data about the product provided by the company or product source and blending it with third-party analytics data. In various embodiments, the third-party analytics data is focused on a product's attribute scores 104, popularity 302, and trends 304 to create a single data source that provides a predictive analysis of any given product's success. The popularity indicators may include those from any source that represent popularity, such as Instagram® follower count or monthly web traffic to a product, among other variables. However, the popularity indicators include factors that are accepted as indicators of popularity in the field at the time the breakthrough score 300 is calculated. These are weighted to create an overall popularity score out of 100. In various embodiments, the popularity score 302 comprises 33% of the overall score out of 100, with the remaining 67% coming from the attribute scores 104 (determined as discussed above) and trend 306 data (weighted similarly to the attribute scores 104). As known to those skilled in the art, other weightings may be utilized at different percentages.

[0034] In various embodiments, popularity data 302 may be scraped and collected from online sources. These online sources may include, but are not limited to, one or more of social media reach and engagement, search term popularity and analytics, and / or website traffic statistics. In various embodiments, this is collected and analyzed historically to provide trend data 304, such as, for example, that search term X is being searched Y% more this month compared to last month. The exact weighting of each social media or other source of trend or popularity data may vary based on the various social media sources that are considered reliable indicators of the product and / or popularity in question.

[0035] Determining the breakthrough score 106 is beneficial / desirable for many reasons, including, but not limited to, the ability to determine the most likely successful products, which may also impact the company score 200 because, as discussed above, it may also include the average score of the products 212 manufactured by the company. The granularity of the data used to determine the breakthrough score 106 allows retailers / companies to target specific types of customers / users / shoppers, specific types of products, and gain deeper insights into consumer trends and sentiment regarding, for example, sustainability. In other embodiments, the targeted sentiment may include other / different characteristics, but in each embodiment, the method may be similar or the same as that disclosed herein for sustainability.

[0036] Referring now also to FIG. 4 , in various embodiments, the platform includes an exchange function that suggests "exchanging" a product in which a user / consumer has expressed interest, e.g., a product added to a cart, for a product that has a higher score related to a characteristic (e.g., sustainability) that the user / consumer has expressed an interest in prioritizing. Thus, as described above, a sustainability score (or other characteristic score) is determined for each product and / or each company that offers the product, resulting in an attribute score 104. The attribute score 104 can be used to suggest alternative products to the user / consumer, so that the user / consumer can consider the alternative product along with the attribute score 104 / sustainability score and exchange the product in their shopping cart for another similar product with a higher attribute score 104.

[0037] Accordingly, disclosed herein are search engines, platforms, and / or methods that allow online shoppers and / or users / consumers to be recommended sustainable or ethical product alternatives to products they have expressed an intent to purchase (i.e., products they have added to their shopping cart or are reviewing product information for a particular product category) based on the crowdsourcing data described above. In various embodiments, users / consumers may express interest in products and / or product categories by viewing items, adding items to their shopping cart, or otherwise saving products. The platforms / engines / methods and / or systems disclosed herein can recommend alternative products of interest based on similarity scores and sustainability or other scores (also broadly referred to herein as attribute scores 104), where applicable and available. Attribute scores are scores that calculate the value of desirable product characteristics, such as sustainability.

[0038] Accordingly, one embodiment disclosed herein is an online e-commerce shopping system and method that provides recommendations during / in real time during the shopping experience and through ancillary touchpoints, e.g., mobile apps, web apps, dynamic ads, and communications, e.g., email. In various embodiments, a user / consumer indicates intent to purchase a product and is then presented with the most similar and relevant alternative products for potential exchange, i.e., sustainable and / or ethical products. In various embodiments, the suggested alternatives for exchange may be based on what is the most relevant and direct exchange possibility, e.g., based on products that can be simultaneously sustainable and / or ethical. The suggested alternatives, i.e., exchange candidates, may be ranked and presented based on sustainability and / or ethicality scores; alternatively, product alternatives that exceed a given sustainability rating may be further ranked / sorted based on similarity.

[0039] Still referring to FIG. 4 , the platform / engine / method for recommending exchanges 400 includes a first step of data collection 402. To suggest exchanges, data 402 must be collected and stored to create a database / index / lookup that can be accessed by computer network 107 (FIG. 1). There are two types of data that the system uses to improve and recommend options to users: explicit data 404 and implicit data 406. Explicit data 404 includes quantifiable product attributes, such as ingredients, nutrients, allergens, manufacturer, packaging, etc. These attributes may be provided by the retailer / company that produces the product, or by a data aggregator or other trusted source that works closely with the retailer. The second type of data is implicit data 406, which is provided / collected from the behavior a user has when interacting with a product (e.g., on a website, page views, clicks, items in a cart, search logs, etc.), and may also include data from other sources with which the user interacts, such as advertising, social media, etc. The more a system / engine ingests these two types of data 404, 406, the more robust it becomes over time.

[0040] Once data 402 is collected and stored, it can be accessed in step 408. Storing and accessing data 402 through an efficient and robust engine / platform allows for large amounts of data to be stored and accessed in a short amount of time, preferably in near real-time. This step 408, in some embodiments, uses a NoSQL database 410, and information can be stored on a remote or local server 804 (FIG. 8). In one embodiment, database 410 is combined with in-memory storage 412, which in various embodiments is, for example, Redis®, for rapid access to the most accessed data. This step 408 will be beneficial to the long-term viability of the engine / platform as the collected and stored data becomes more robust.

[0041] Still referring to FIG. 4 , once the data is collected and stored, it is analyzed in step 414. Analyzing the data 414 is a task that can be divided into two types of processes: near real-time processing 416 and batch analysis 418. The largest set of data is analyzed 414 by the batch analysis 418 process. The batch analysis 418 process incorporates all existing data about products and / or companies. This process can be useful / desirable for many reasons, including, but not limited to, because it is convenient and feasible due to the fact that it produces good results. The second process, real-time processing 416, evaluates the stream of new product or company information and, in various embodiments, is faster than the batch analysis 418 process, with results that can be consumed immediately. This process is used to generate recommendations in an efficient and much more rapid manner, i.e., before the batch analysis 418 processing system can produce results. The underlying technology that powers these processes are complex machine learning algorithms that can cluster and rank products based on implicit and explicit labels. The concepts of term frequency (TF) and inverse document frequency (IDF) are used in information retrieval systems and content-based filtering mechanisms (such as content-based recommendation systems). They may be used to determine the relative importance of products. TF is the frequency of a word in a document. IDF is the inverse of the document frequency within the entire corpus of documents. In various embodiments, TF-IDF is used for many reasons, such as the need to weight common words and prepositions (e.g., "the," "a," etc.) that do not add value to finding similarities. To determine which products are close to each other, the engine / method / system / platform uses a vector space model to calculate proximity based on the angle between vectors. In this model, each item is stored in an n-dimensional space as a vector of its attributes (which is also a vector), and the angle between the vectors is calculated to determine the similarity of these vectors.

[0042] The next step in the disclosed method is data filtering 420 (FIG. 4). Data filtering 420 involves filtering the results to provide relevant recommendations for each particular user, taking into account multiple factors, including but not limited to, the user's previous activity, product attributes, and calculated sustainability / attribute scores 104.

[0043] 5, in various embodiments, a method and system is disclosed for recommending to a user / consumer / shopper products that may be considered similar to a user-selected product 502, but that have higher scores in sentiment categories, e.g., sustainability, ethics, etc. The method includes the user selecting a product 502, then the engine / platform using the methods / systems disclosed herein presenting replacement candidates to the user, the user selecting the replacement candidate, and the engine / platform replacing the previously selected product with a sustainable product, i.e., sustainable replacement 504.

[0044] 6, determining the attribute scores 104 (i.e., sustainability and ethical scores in this embodiment) for each product score 212 and brand / company / product source score 200, in various embodiments, includes collecting crowdsourced reviews for each product and company / product source as described herein. This method of determining the attribute scores 104 (i.e., sustainability and ethical scores in this embodiment) includes a method / system for product and brand reviews.

[0045] Referring now also to FIG. 7 , the product and brand review method 700 involves the user 105 navigating a product review page 702, then presenting the user 105 with questions, and the user selecting 706 an answer to each question. All of this may be performed via the user interface 102 ( FIG. 1 ), including saving 708 the answers to a web server 804 ( FIG. 8 ). The engine / method then recalculates 710 each question group score by averaging all received reviews along with any new reviews (NR), and then calculates 712 an attribute score 104 (sustainability in this embodiment) by averaging the question group scores. The terms “question” and “question group” are explained in more detail elsewhere in this disclosure. Whether the user is reviewing a product or a brand affects the next steps. If the user is reviewing a product, the engine / method / system retrieves 716 the calculated pure score for the brand and calculates 718 a final aggregate score. If the user is reviewing a brand, the engine / method averages 720 the pure scores for each product and calculates 722 a final aggregate brand score.

[0046] Referring again to FIG. 6 , a process diagram for rating a product or brand is shown. The rating process is the same regardless of whether a product or brand is being reviewed. While the method / process is described herein with reference to a product, in other embodiments, the same steps may be used to collect ratings for a brand. The method includes the user 105 navigating 602 / 702 to a product review page, preparing 604 questions for the user to answer, displaying 606 the questions, collecting 608 the user's answers, saving 610 the answers to a database, and displaying 612 the results to the user 105. These steps 602-612 are included / presented through the loading of a web interface / review wizard 803, which can be accessed and completed via the user interface 102 ( FIG. 1 ).

[0047] The following steps 614-624 are involved in the asynchronous rating / voting service. These steps include retrieving 614 previous score data stored in the score processing server 808 (FIG. 8), adding 616 new scores, calculating 618 new pure scores, retrieving 620 scores for associated data entries, calculating 622 new final scores, and determining 624 permanent scores. The scores are then processed and stored in the score processing server 808 and can be made available to systems and alternative engines (see FIG. 9 and its description below). The process diagram for rating / voting products shown in FIG. 6 is the same process that can be used to review businesses. For either products and / or businesses, scores are processed, stored on a server, and made available over a computer network.

[0048] 8 , in various embodiments, a method of scoring process 800 is disclosed. The scoring process 800 begins when a user 105 accesses a product review page 802, which loads a review wizard 803 accessible from a computer network 105. The user interacts with the product review page 802 via a user interface 102, as described above. The computer network 105 (which may include, without limitation, systems / engines / platforms) and the product review page 802 may be accessed via a direct connection or via a transmission medium, such as a local area network or a wide area network like the Internet. A web server 804 communicates with a product catalog 806, which may be stored on the same or a different server as the web server 804, which retrieves 812 product data and stores 814 user responses from the review wizard 803. The score processing server 808 retrieves 816 existing reviews from a review repository 810, which may be stored on the same or a different server as the score processing server 808, retrieves 818 existing scores for related products, and stores 820 newly generated computer scores for future retrieval by the score processing server 808.

[0049] Referring now also to FIG. 9 , once an online user / shopper / customer finds a product of interest, the engine / method / system disclosed herein finds the most relevant replacement alternatives using the following process for substitution: For any given product that the user is interested in purchasing, i.e., input data, a list of alternative products (e.g., sustainable products) is generated. Output data is generated, including a list of products ranked by attribute scores 104 and similarity scores 904. The characteristics of the given or selected product are obtained, and a filter is generated to search for these characteristics in other products. The alternative products are searched in a database, and the characteristics of the given or selected product are compared with the other products. Each record is then assigned a similarity score (a floating point number) between 0 and 1, where 0 = no similarity and 1 = identical. Only products with a similarity score greater than a predetermined threshold (e.g., 0.7) are considered valid alternatives. In various embodiments, the predetermined threshold may be different. The resulting list is ranked based on the attribute score 104, which in this embodiment is sustainability.

[0050] FIG. 10 shows an object diagram of the system for scoring. Once each required product and / or company has the necessary input data analysis and scoring, a breakthrough score can be generated. For breakthrough score generation, the data is normalized and a subset of the data for the selected space is created. That is, the subset can be region-specific, category-specific, or any subset depending on the desired requirements, and can be as detailed as the data allows. Once the subset is selected, weights for each variable are calculated to achieve maximum accuracy. By normalizing the data, selecting the subset, and applying weights to the variables, a breakthrough score can be calculated to generate a ranked list of the most competitive companies in the selected space.

[0051] Each product has a list of attributes stored in the database, including one or more of the following attributes: ingredients, nutritional value, tokenized description, and a list of categories. The list of attribute categories may vary for different products, may include more or fewer examples than those presented herein, and in other embodiments, may include one or more attributes different from those presented herein as examples. As described above, the sustainability score or attribute score 104 is a score calculated based on public opinion regarding a particular product attribute (e.g., sustainability) and company attribute (e.g., a brand's sustainability score). The data is obtained by allowing the public to review products and brands by answering a series of questions grouped into "question groups," as shown and described herein with respect to Figures 6-7.

[0052] In various embodiments, each score has two components: a product score and a company or brand score. The product score, in some embodiments, is a calculated product score and a calculated company score. The company score is a calculated company score, which is an average of multiple calculated product scores for a brand. Each score is stored as a pure score and an aggregate score. A pure score includes only reviews for a specific product or brand. An aggregate score includes a pure score and an average of the scores of related entities. For example, for a product, it includes a brand score and an average of the product scores for a company.

[0053] The term "question" as used herein refers, in some embodiments, to a question intended to gauge a user's opinion of a product's attributes, e.g., sustainability aspects. In various embodiments, each question includes five answers on a scale of 1 to 10. However, in other embodiments, each question may have more or less than five answers, and the scale may vary, for example, from 1 to 5 and / or from 1 to 100, and between, above, or below these numbers. As will be appreciated, the number of questions, answers, and scale of the scale may be varied as known to those skilled in the art.

[0054] The term "question group" as described herein refers to a group. A group represents an attribute of a product, e.g., a sustainability aspect. The answers to the questions within a question group are averaged and a score is calculated for each group and each product.

[0055] Each question group has a weight that defines its importance in the final score.

[0056] In various embodiments, if an exchange is offered and the user / consumer chooses to exchange the product, the user may be awarded an incentive for exchanging for a more sustainable product, e.g., tokens / coins, tree planting, etc. In various embodiments, the tokens / coins may be used to purchase additional products, i.e., similar to store credit.

[0057] In various embodiments, all data about any given product, such as ingredient lists, marketing slogans, etc., may be broken down into keywords. Using these keywords, the platform / engine / method stitches and ranks similarities between products based on the number of keywords shared between them. The engine / method uses a learning algorithm that refines results with human interaction through the suggested replacements feature on the platform. For example, if a customer recommends product X as a sustainable alternative to product Y, it a) generates matches between those products that go beyond keyword matching, and b) refines the algorithm by weighting certain keywords that already match.

[0058] Once products are matched by similarity, the engine / method ranks them by attribute scores 104 and provides top suggested replacements. The attribute scores 104 are driven by a crowd-sourced aggregation of consumer reviews.

[0059] Various embodiments allow users to view analysis and rankings of companies and products based on their likelihood of future success. The analysis and rankings are based on past and current popularity, consumer sentiment, sales, trends, ethical and financial health data. Past performance data for a given product or company is compared against key success indicators to predict its future popularity and likelihood of financial success.

[0060] Popularity data includes, but is not limited to, consumer sentiment, brand awareness data, social media presence in terms of reach, engagement, content quality and regularity. Popularity data may be reduced by any negative data, such as litigation or recalls. Sales and financial health data includes, but is not limited to, historical revenue, financial records, accounts receivable and payable, and publicly available records.

[0061] The popularity, sales, trends, ethics, and financial health data are given individual scores out of 100 based on several criteria, which are weighted and blended to create a single Breakthrough Score that is an overall predictive score of future success. The individual scores for each product and company's popularity, sales, trends, ethics, and financial health data may be utilized to construct the Breakthrough Score.

[0062] Companies may be clustered into subgroups, such as company size subgroups, location subgroups, and other subgroups, depending on parameters that determine subgroup membership. For example, company size may be determined by various factors, including number of employees, sales, and company age. The systems and methods described herein normalize the data across all variables within each subgroup, making the data comparable. Using deep learning techniques, weights assigned to popularity indicators, such as social media followers and website traffic, are dynamically adjusted based on historical data patterns. This ensures that the predictive system algorithm remains accurate and adaptable to changing business contexts.

[0063] The computer-readable media described herein may be any known or future data storage that is read by a computer. Furthermore, it is understood that the term "memory" as used herein is intended to encompass various types of suitable data storage media, whether permanent or transitory, such as transient electronic memory, non-transitory computer-readable media, and / or computer-writable media.

[0064] It should be understood that the present disclosure may be implemented in various forms of hardware, software, firmware, special purpose processes, or combinations thereof. In one embodiment, the present disclosure may be implemented in software as an application program embodied in a computer-readable program storage device or over a transmission medium, such as a local area network or a wide area network such as the Internet. The application program may be uploaded to and executed by a machine comprising any suitable architecture. It should be further understood that, because some of the constituent system components and method steps illustrated in the accompanying figures may be implemented in software, the actual connections between the system components (or process steps) may vary depending on how the present disclosure is programmed. Given the teachings of the present disclosure provided herein, those skilled in the relevant art will be able to contemplate these and similar implementations or configurations.

[0065] Many embodiments have been described herein. In addition to the exemplary embodiments shown and described herein, other embodiments are contemplated within the scope of the present disclosure. Accordingly, it will be understood that various modifications can be made without departing from the scope of the disclosure. Additionally, while operations are shown in a particular order in the figures, this should not be understood as requiring such operations to be performed in a particular order or sequential order, or that all of the operations shown be performed, to achieve desirable results.

[0066] Furthermore, it is to be understood that the phrases and terms used herein are for purposes of description and should not be considered limiting. The use herein of "comprising," "comprising," "containing," "including," "involving," or "having," and variations thereof, is intended to encompass the items listed thereafter and equivalents thereof, as well as additional items, with only the terms "consisting only of" or "consisting essentially of" being interpreted in a limiting sense. References to "or" may be interpreted inclusively, such that any term described using "or" may refer to one, more than one, and all of the described term. The terms "enterprise" or "enterprise(s)" are used interchangeably with the terms "business(es)," refer to similar and / or the same concept, and should not be interpreted in a limiting sense.

[0067] Moreover, the purpose of the abstract is to enable the U.S. Patent and Trademark Office and the general public, particularly engineers, technicians, and practitioners unfamiliar with patent or legal or technical language, to quickly determine from a cursory inspection the nature and substance of the application's technical disclosure. The abstract is not intended to define the scope of the claims of the application or to limit the scope of the claims in any way.

Claims

1. 1. A computer-implemented method for exchanging a product selected for purchase by a user with an alternative product, comprising: the computer network including one or more servers that operate to receive, analyze, and store data; accessing the computer network from at least one external user interface to input data for the selected product; storing the input data as a product catalog database on one of the one or more servers; retrieving the entered data from the product catalog database; analyzing the entered data to determine a company score for the source of the selected product and a product score for the selected product; storing the company scores and the product scores on one of the at least one or more servers in a reviews repository; analyzing the company scores and the product scores to determine attribute scores for the selected products; storing the attribute scores of the selected products on one of the at least one server in the reviews repository; Collecting data, including explicit and implicit data, about substitute products; analyzing the explicit data and the implicit data to identify alternative products similar to the selected product; calculating a corresponding attribute score for each of the identified alternative products; filtering alternative products having lower attribute scores than the selected product; storing the attribute scores for each of the alternative products on one of the at least one server in the reviews repository; ranking the remaining alternative products by both their similarity to the selected product and their attribute scores; displaying the highest scoring alternative products from the reviews repository through the user interface; selecting a replacement product from the displayed alternative products; exchanging the selected product for the replacement product; 11. A computer-implemented method comprising:

2. calculating a breakthrough score as an indicator of the predicted success of the product; 2. The computer-implemented method of claim 1, wherein the breakthrough score is calculated by using source data provided by the source of the product and blending it with third-party analytical data, both types of data being accessed by the computer network and stored on one of the one or more servers.

3. The computer-implemented method of claim 2 , further comprising using the breakthrough score to calculate the attribute score.

4. The computer-implemented method of claim 3 , wherein the attribute score is a sustainability score.

5. The computer-implemented method of claim 4 , wherein the lower the sustainability score, the higher the ecological impact of the product, and the higher the sustainability score, the lower the ecological impact of the product.

6. 10. The computer-implemented method of claim 1, further comprising: loading a review wizard over the computer network; preparing questions for the user to answer; displaying the questions to the user; and saving the user's answers in the product catalog database.

7. The computer-implemented method of claim 1 , wherein the explicit data includes quantifiable attributes about the substitute products, and the implicit data is collected from behavior of users of the substitute products.

8. The computer-implemented method of claim 7 , wherein the explicit data is selected from the group consisting of product ingredients, nutrients, allergens, manufacturer, and packaging.

9. The computer-implemented method of claim 7 , wherein the implicit data is collected from historical data of users interacting with alternative products.

10. 10. The computer-implemented method of claim 9, wherein the user's historical data is selected from the group consisting of website page views, clicks on alternative products, number of shopping carts containing alternative products, user search history, advertisements, and social media posts about alternative products.

11. The computer-implemented method of claim 1 , wherein the product is a business.

12. 1. A system for determining a sustainability score of a selected product and replacing the selected product with an alternative product selected from a group of alternative products having a higher sustainability score, the system comprising: a product catalog database stored on at least one server, the product catalog database containing input data collected from user responses and product data for both the selected product and the alternative product; a review repository database stored on at least one server, the review repository database including product scores and product supplier scores; at least one processor that receives data on the selected product and the substitute product, analyzes and stores the data; at least one processor scorer that calculates sustainability scores for the selected product and the alternative product from both the product scores and the product supplier scores associated with the selected product and the alternative product; at least one processor ranker that ranks the alternative products for replacement by both similarity and sustainability score; at least one processor selector for selecting a replacement product from products similar to the selected product, the replacement product having a high similarity ranking with the selected product and a corresponding sustainability score higher than the sustainability score of the selected product; an interface for presenting the selected replacement product and exchanging the selected product for the replacement product; Including, the system.

13. 1. A system for replacing a selected product with an alternative product, wherein the at least one processor that receives and analyzes data to identify similar alternative products includes a processor searcher, and similarity is determined by data related to one or more of the characteristics, qualities, and capabilities of the alternative products.

14. 10. The system for replacing a selected product with an alternative product of claim 1, further comprising a breakthrough score as an indicator of the product's predicted success, the breakthrough score being calculated by using source data provided by the source of the product and blending it with third-party analytical data, both types of data being accessed by the computer network and stored on one of the one or more servers.

15. 15. The system for exchanging a selected product for an alternative product of claim 14, wherein the breakthrough score is used to calculate the attribute score.

16. 13. The system for exchanging a selected product for an alternative product having a higher sustainability score of claim 12, wherein the product is a business.

17. 13. The system for replacing a selected product with an alternative product of claim 12, wherein the sustainability score is calculated to include one of product performance data or a product's past popularity.

18. 13. The system for exchanging a selected product for a substitute product of claim 12, wherein the product is a business.

Images