Monetized voice system and method for interactive business promotions based on artificial intelligence

Abstract

A system comprising a data storage containing merchant offers and an Al-based digital assistant module connected via network interface for two-way communication with computing devices. The digital assistant includes: an interactive graphical interface for text input; a microphone for audio input; a geolocation module generating user location data; and a keyword recognition module processing text commands and transcoding them to voice data. The system enables location-aware, voice-driven commercial interactions by processing user inputs to deliver relevant merchant offers through conversational Al technology.

Description

Title: Monetized Voice System And Method For Interactive Business Promotions Based On Artificial IntelligenceFIELD OF INVENTION

[0001] The present invention relates to Artificial Intelligence based informative digital assistant system, more particularly, voice-based digital assistant system for displaying live business deals, offers or promotions to the customers.BACKGROUND OF THE INVENTION

[0002] In the contemporary business landscape, digital marketing has emerged as an indispensable element of organizational growth and expansion strategies. As consumer behavior increasingly shifts toward digital channels, businesses across all sectors are compelled to adapt their marketing approaches to remain competitive and relevant.

[0003] Digital assistants represent a significant technological advancement in this digital transformation journey. These Al-powered tools offer businesses a cost-effective and convenient mechanism to leverage sophisticated marketing expertise without the overhead associated with traditional marketing departments. By serving as a centralized point of contact for both contractors and customers, digital assistants streamline communication channels and enhance operational efficiency.

[0004] The implementation of digital assistants has been particularly prominent in customer contact centers, where they manage incoming communications and provide consistent service experiences. Unlike simple automated systems of the past, modern digital assistants are complex technological ecosystems underpinned by advanced artificial intelligence architectures and machine learning algorithms. These sophisticated systems continuously improve through iterative learning processes, adapting to user interactions and refining their responses accordingly.

[0005] The integration of geolocation capabilities represents a particularly valuable enhancement to digital assistant functionality. By processing geographical data in real-time, these systems can deliver highly contextual information about live offers, promotions, and business deals specific to a user's immediate vicinity. This location-aware functionality transforms the customer experience from generic to highly personalized, increasing the relevance and value of business-consumer interactions.

[0006] Voice technology has further revolutionized the digital assistant landscape. The ability to process natural language inputs eliminates traditional barriers to technology adoption, making these systems accessible to broader demographic segments. Voice-enableddigital assistants facilitate more intuitive and conversational interactions, mirroring human communication patterns and enhancing user engagement.

[0007] The convergence of voice technology, artificial intelligence, and geolocation awareness creates unprecedented opportunities for businesses to connect with consumers in meaningful ways. These systems enable real-time, context-sensitive commercial interactions that adapt to individual preferences, locations, and behaviors. The resulting personalized experience fosters stronger customer relationships and builds brand loyalty.

[0008] As businesses navigate increasingly competitive markets, the strategic implementation of Al-powered digital assistants offers a significant competitive advantage. These technologies not only optimize operational efficiencies but also enhance customer engagement through personalized interactions. The evolution of these systems continues to accelerate, with advances in natural language processing, contextual understanding, and predictive capabilities constantly expanding their potential applications in the commercial sphere.OBJECTS OF THE INVENTION

[0009] An object of the present invention is to provide an artificial intelligence-based voice system that enables users to discover, interact with, and purchase from location-specific merchant offers through natural language processing while creating a seamless, contextually aware shopping experience that adapts to user behavior patterns and optimizes purchase flows through hierarchical search mechanisms and adaptive feedback.DESCRIPTION OF RELATED ARTS

[0010] Several prior art references disclose various aspects of digital assistant technology, though none fully address the specific combination of features in the present invention.

[0011] U.S. Patent No. 10,043,516 B2 discloses an automated voice-based system for intelligent assistance that interprets natural language input in spoken and / or textual form to infer user intent and performs actions based on the inferred user intent.

[0012] U.S. Patent No. 10,909,980 B2 describes methods, systems, and computer-readable mediums having instructions for implementing machine-learning digital assistants for advanced analytics, procurement, and operations tasks.

[0013] Bharath et al., in U.S. Patent No. 9,047,631 B2, disclose a method and system for providing location-based customer assistance, particularly teaching about a method for providing a distributed mobile call center for a service establishment.

[0014] In U.S. Patent Application Publication No. US20130311286A1, John et al. claim a system and method for facilitating determination of marketing information for onlineconsumers based on a location characteristic of the online consumer.

[0015] Westley et al., in U.S. Patent No. 10,672,066 B2, teach about a digital assistant that interacts with the owner's mobile device.

[0016] Victor et al., in U.S. Patent Application Publication No. US20210134263A1, disclose a platform and system for the transcription of electronic online content from mostly visual / text format to an aural format, adapted for being read by an intelligent speaker system. This application specifically discloses an automated engine with artificial intelligence and / or machine learning for transforming written websites into audio-enabled content for use with intelligent speaker technology, implementing data mining, processing, and summarizing tools.

[0017] Another patent granted to Apple Inc., U.S. Patent No. 9,548,050 B2 filed by Thomas et al., teaches an intelligent automated assistant system that engages with users in an integrated, conversational manner using natural language dialog and invokes external services when appropriate to obtain information or perform various actions.

[0018] Despite these advancements in digital assistant technology, none of the above-discussed prior art teaches about informative voice-based, Al-enabled digital assistants specifically designed for displaying location-based live business deals, promotions, or offers according to customer interests and preferred geographical locations.SUMMARY OF THE INVENTION

[0019] Embodiments of the present disclosure may include a system including a data storage populated with a plurality of merchant offers' data records. Embodiments may also include an artificial intelligence-based digital assistant module connected to the data storage over a network interface configured for two-way communication between the artificial intelligence-based digital assistant module and a plurality of computing devices.

[0020] In some embodiments, the artificial intelligence-based digital assistant module may include an interactive graphical user interface configured to receive text input from a user. Embodiments may also include at least a first microphone configured to receive an audio input from the user. Embodiments may also include a geolocation module configured to generate geolocation data of the user.

[0021] Embodiments may also include a keyword recognition module configured to process text-based commands from the user and transcode the text-based commands into voice data. Embodiments may also include a speech processing module configured to process audio input from the user. In some embodiments, the speech processing module may be further configured to parse the audio input to derive at least one keyword from the audio input.

[0022] Embodiments may also include functionality to distinguish purchase-related inputs from standard speech through trained recognition patterns. Embodiments may also include capabilities to generate contextual purchase prompts based on the distinguished purchase-related inputs. Embodiments may also include an adaptive feedback module configured to interject during user inputs based on learned purchase flows.

[0023] Embodiments may also include functionality to provide real-time guidance to users for proper voice input format based on historical successful purchase patterns. Embodiments may also include capabilities to dynamically adjust guidance based on user response patterns. Embodiments may also include a hierarchical search module configured to implement a two-step search process that first prioritizes brand-specific queries before performing broader keyword searches.

[0024] Embodiments may also include functionality whereby when a brand name is recognized, the system initially limits search results to that brand's offerings before expanding to related products. Embodiments may also include capabilities whereby when no brand name is detected, the system proceeds with keyword-based search using identified relevant terms. Embodiments may also include functionality to filter out non-value-adding words from search queries to improve search precision.

[0025] Embodiments may also include a purchase prompt and confirmation module configured to enable users to complete purchases using stored payment methods and provide delivery or pickup options. Embodiments may also include a user ratings module configured to allow users to hear ratings for products they may be interested in. Embodiments may also include a product availability notifications module configured to notify users when products become available and facilitate automatic purchases.

[0026] In some embodiments, the digital assistant module may be configured to receive input data including voice or text. In some embodiments, the digital assistant module may be configured to parse the input data for at least one keyword. In some embodiments, the digital assistant module may be coupled to the data storage and configured to fetch at least one merchant offers' data record based on the at least one keyword. In some embodiments, the digital assistant module may be configured to transcode the at least one merchant offers' data record into voice data. In some embodiments, the digital assistant module may be configured to transmit the voice data over the network interface to the plurality of computing devices.

[0027] In some embodiments, the speech processing module may be further configured to identify and parse specific elements from purchase-related inputs including product specifications, payment method preferences, and delivery options. Embodiments may alsoinclude functionality to maintain context awareness across multiple user utterances within a single shopping session. Embodiments may also include capabilities to adapt speech recognition patterns based on successful purchase completions.

[0028] In some embodiments, the adaptive feedback module may be further configured to track common error patterns in user voice inputs. Embodiments may also include functionality to generate personalized correction suggestions based on user's historical interaction patterns. Embodiments may also include capabilities to store successful voice input patterns for future reference and guidance. Embodiments may also include functionality to provide progressive guidance by starting with minimal intervention and increasing assistance based on user response.

[0029] In some embodiments, the hierarchical search module's two-step search process may include a first search phase that identifies and extracts brand names from user input. Embodiments may also include functionality to query brand-specific product databases. Embodiments may also include capabilities to rank results based on brand relevance scores.

[0030] Embodiments may also include a second search phase that activates when no brand is identified or brand-specific results are insufficient. Embodiments may also include functionality to perform keyword-based searches across all product categories. Embodiments may also include capabilities to apply relevance filtering based on user context and history.

[0031] In some embodiments, the filter algorithm of the hierarchical search module may be configured to maintain a dynamic database of non-value-adding words. Embodiments may also include functionality to analyze word frequency and correlation with successful searches. Embodiments may also include capabilities to remove common filler words while preserving context-specific terms. Embodiments may also include functionality to adapt filtering rules based on search success rates.

[0032] In some embodiments, the speech processing module implements a learning algorithm that tracks successful purchase-related voice interactions. Embodiments may also include functionality to identify patterns in voice inputs that lead to completed purchases. Embodiments may also include capabilities to adjust recognition parameters based on user-specific speech patterns. Embodiments may also include functionality to maintain separate recognition models for purchase-related and non-purchase speech.

[0033] In some embodiments, the adaptive feedback module implements a purchase flow training model that analyzes historical purchase completion data. Embodiments may also include functionality to identify common points of user hesitation or confusion. Embodiments may also include capabilities to generate context-appropriate intervention triggers.Embodiments may also include functionality to customize guidance based on product category and user expertise level.

[0034] Embodiments may also include receiving and processing user input that includes receiving user input through multiple channels including voice input through at least one microphone. Embodiments may also include text input through a typing interface. Embodiments may also include location data through a global positioning system.

[0035] Embodiments may also include processing the multi-channel input by analyzing voice commands through the speech processing module. Embodiments may also include processing text-based commands through the keyword recognition module. Embodiments may also include integrating GPS data with search parameters. Embodiments may also include maintaining context across input channels by synchronizing user intent across voice and text inputs.

[0036] Embodiments may also include preserving search context across input methods. Embodiments may also include combining location context with user queries. Embodiments may also include adapting input processing based on user's preferred input methods. Embodiments may also include historical success rates of different input channels. Embodiments may also include current interaction context.

[0037] Embodiments of the present disclosure may also include a method including the steps of receiving by an artificial intelligence-based digital assistant module connected to a data storage over a network interface at least one merchant offers' data record via a network interface. Embodiments may also include storing by the artificial intelligence-based digital assistant module the at least one merchant offers' data record in the data storage.

[0038] Embodiments may also include receiving by the artificial intelligence-based digital assistant module inbound voice data and user geolocation data from a user communication device via the network interface. Embodiments may also include processing the inbound voice data by distinguishing purchase-related inputs from standard speech using trained recognition patterns.

[0039] Embodiments may also include identifying specific elements including product specifications, payment preferences, and delivery options. Embodiments may also include generating contextual purchase prompts based on the distinguished purchase-related inputs. Embodiments may also include implementing an adaptive feedback process including monitoring user input patterns in real-time.

[0040] Embodiments may also include interjecting during user inputs based on learned purchase flows. Embodiments may also include providing dynamic guidance for proper voiceinput format based on historical successful purchase patterns. Embodiments may also include performing a hierarchical search process including executing a first search phase that prioritizes brand-specific queries by identifying and extracting brand names from the inbound voice data.

[0041] Embodiments may also include querying brand-specific product databases. Embodiments may also include ranking results based on brand relevance scores. Embodiments may also include executing a second search phase when no brand is identified or brand-specific results are insufficient by filtering out non-value-adding words while preserving context-specific terms.

[0042] Embodiments may also include performing keyword-based search across all product categories. Embodiments may also include applying relevance filtering based on user context and history. Embodiments may also include deriving by the artificial intelligence-based digital assistant module at least one keyword from the inbound voice data.

[0043] Embodiments may also include fetching by the artificial intelligence-based digital assistant module the at least one merchant offers' data record from the data storage based on the at least one keyword. Embodiments may also include inbound location data. Embodiments may also include results of the hierarchical search process.

[0044] Embodiments may also include user's historical purchase patterns. Embodiments may also include transcoding by the artificial intelligence-based digital assistant module the at least one merchant offers' data record to outbound voice data. Embodiments may also include transmitting by the artificial intelligence-based digital assistant module the outbound voice data via network interface to the user communication device.

[0045] Embodiments may also include continuously improving search accuracy by tracking successful purchase completions. Embodiments may also include analyzing patterns in voice inputs that lead to successful purchases. Embodiments may also include updating speech recognition models based on aggregate user data. Embodiments may also include refining search algorithms based on purchase completion rates.

[0046] In some embodiments, the data storage may include a database containing latest offers, business promotions, and live deals within a given geographical area of a user. In some embodiments, the trained recognition patterns may be continuously updated based on successful purchase completions. In some embodiments, the learned purchase flows may be derived from historical successful transactions. In some embodiments, the hierarchical search process adapts its ranking algorithms based on user-specific purchase patterns.

[0047] Embodiments may also include processing the inbound voice data which mayinclude receiving voice keywords from the user through a trained speech recognition model. Embodiments may also include determining the geographical location of the user through real-time GPS data. Embodiments may also include analyzing the voice keywords in conjunction with the geographical location to identify relevant local offers and promotions.

[0048] Embodiments may also include filtering results based on proximity to user location. Embodiments may also include ranking results based on both relevance and distance. Embodiments may also include presenting geographically-targeted voice results around the vicinity of the user. Embodiments may also include continuously updating the local offer database based on user interaction patterns with local offers. Embodiments may also include successful purchase completions within specific geographical areas. Embodiments may also include temporal relevance of offers and promotions.BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The novel features believed to define the illustrative embodiments are detailed in the appended claims. To fully comprehend these embodiments, along with their preferred usage, objectives, and detailed descriptions, one should refer to the comprehensive description of one or more examples of these embodiments, as provided in this disclosure. This understanding is further enhanced when considered alongside the accompanying drawings, wherein the drawings show:

[0050] FIG. 1 is a block diagram illustrating a system, according to some embodiments of the present disclosure.

[0051] FIG. 2 is a block diagram further illustrating the system from FIG. 1, according to some embodiments of the present disclosure.

[0052] FIG. 3 is a block diagram further illustrating the system from FIG. 1, according to some embodiments of the present disclosure.

[0053] FIG. 4 is a block diagram further illustrating the system from FIG. 1, according to some embodiments of the present disclosure.

[0054] FIG. 5A is a flowchart illustrating a method, according to some embodiments of the present disclosure.

[0055] FIG. 5B is a flowchart extending from FIG. 5A and further illustrating the method, according to some embodiments of the present disclosure.

[0056] FIG. 5C is a flowchart extending from FIG. 5B and further illustrating the method from FIG. 5A, according to some embodiments of the present disclosure.

[0057] FIG. 5D is a flowchart extending from FIG. 5C and further illustrating the method from FIG. 5A, according to some embodiments of the present disclosure.

[0058] FIG. 6 is a flowchart further illustrating the method from FIG. 5A, according to some embodiments of the present disclosure.DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

[0059] Unless otherwise defined, all technical terms used herein related to voice recognition, artificial intelligence, machine learning, search algorithms, and e-commerce systems have the same meaning as commonly understood by one of ordinary skill in the relevant arts of speech processing, digital assistants, and electronic commerce. It will be further understood that terms such as "speech recognition," "natural language processing," "machine learning," "artificial intelligence," and other technical terms commonly used in the fields of voice commerce and digital assistants should be interpreted as having meanings consistent with their usage in the context of this specification and the current state of voice shopping technology. These terms should not be interpreted in an idealized or overly formal sense unless expressly defined herein. For brevity and clarity, well-known functions or constructions related to voice processing, search algorithms, or e-commerce systems may not be described in detail.

[0060] The terminology used herein describes particular embodiments of the voice-based shopping system and is not intended to be limiting. As used herein, singular forms such as "a speech recognition module," "an adaptive feedback module," and "the hierarchical search process" are intended to include plural forms as well, unless the context clearly indicates otherwise. Similarly, references to "voice input" or "search process" should be understood to include multiple instances or iterations of such elements, where applicable.

[0061] With reference to the use of the words "comprise" or "comprises" or "comprising" in describing the components, processes, or functionalities of the voice-based shopping system, and in the following claims, unless the context requires otherwise, these words are used on the basis and clear understanding that they are to be interpreted inclusively rather than exclusively. For example, when referring to "comprising a speech recognition module," the term should be understood to mean including but not limited to the described speech recognition capabilities, and may include additional related functionalities or components not explicitly described. Each instance of these words is to be interpreted inclusively in construing the description and claims, particularly in relation to the modular and adaptable nature of the voice shopping system described herein.

[0062] Furthermore, terms such as "connected," "coupled," or "communication with" as used in describing the interaction between various modules of the system (such as between the speech processing module and the adaptive feedback module) should be interpreted toinclude both direct connections and indirect connections through one or more intermediary components, unless explicitly stated otherwise. References to "processing," "analyzing," or "adapting" should be understood to encompass both real-time operations and delayed or batch processing, unless specifically limited to one or the other in the context.

[0063] In some aspects thereof, FIG. 1 is a block diagram that describes a system 102, according to some embodiments of the present disclosure. In some embodiments, the system 102 may include a data storage 104 populated with a plurality of merchant offers' data records, an artificial intelligence-based digital assistant module 106 connected to the data storage 104 over a network interface configured for two-way communication between the artificial intelligence-based digital assistant module 106 and a plurality of computing devices, an interactive graphical user interface 108 configured to receive text input from a user, a geolocation module 110 configured to generate geolocation data of the user, a keyword recognition module 112 configured to process text-based commands from the user and transcode the text-based commands into voice data, a speech processing module 114 configured to process audio input 116 from the user, an adaptive feedback module 118 configured to interject 120 during user inputs based on learned purchase flows, a hierarchical search module 122, a purchase prompt 124, confirmation module 126 configured to enable users to complete purchases using stored payment methods and provide delivery or pickup options, a user ratings module 128 configured to allow users to hear ratings for products they may be interested in, a product availability notifications module 130 configured to notify users when products may be available and facilitate automatic purchases, by voice 132, and / or text 134.

[0064] In some embodiments, at least a first microphone is configured to receive an audio input from the user. The speech processing module 114 may be further configured to parse the audio input to derive at least one keyword from the audio input, distinguish purchase-related inputs from standard speech through trained recognition patterns, and generate contextual purchase prompts based on the distinguished purchase-related inputs.

[0065] In some embodiments, the system provides real-time guidance to users for proper voice input format based on historical successful purchase patterns, dynamically adjusts guidance based on user response patterns, implements a two-step search process that first prioritizes brand-specific queries before performing broader keyword searches, and when a brand name is recognized, initially limits search results to that brand's offerings before expanding to related products.

[0066] In some embodiments, when no brand name is detected, the system proceeds withkeyword-based search using identified relevant terms and filters out non-value-adding words from search queries to improve search precision. The digital assistant module 106 may be configured to receive input data, parse the input data for at least one keyword, and is coupled to the data storage 104 and configured to fetch at least one merchant offer data record based on the at least one keyword. The digital assistant module 106 may be configured to transcode the at least one merchant offer data record into voice data and transmit the voice data over the network interface to the plurality of computing devices.

[0067] In some embodiments, the adaptive feedback module 118 may be further configured to track common error patterns in user voice input, generate personalized correction suggestions based on user's historical interaction patterns, store successful voice input patterns for future reference and guidance, and provide progressive guidance by starting with minimal intervention and increasing assistance based on user response.

[0068] In some embodiments, the filter algorithm of the hierarchical search module 122 may be configured to maintain a dynamic database of non-value-adding words, analyze word frequency and correlation with successful searches, remove common filler words while preserving context-specific terms, and adapt filtering rules based on search success rates. In some embodiments, the speech processing module 114 may implement a learning algorithm that tracks successful purchase-related voice interactions, identifies patterns in voice input that lead to completed purchases, and maintains separate recognition models for purchase-related and non-purchase speech. In some embodiments, the adaptive feedback module implements a purchase flow training model that analyzes historical purchase completion data, identifies common points of user hesitation or confusion, generates context-appropriate intervention triggers, and customizes guidance based on product category and user expertise level.

[0069] FIG. 2 is a block diagram that further describes the system 102 from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the speech processing module 114 may be further configured to identify and parse specific elements from purchase-related inputs, maintain context awareness across multiple user utterances within a single shopping session, and adapt speech recognition patterns based on successful purchase completions.

[0070] FIG. 3 is a block diagram that further describes the system 102 from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, the hierarchical search module's two-step search process queries brand-specific product databases, ranks results based on brand relevance scores, activates when no brand is identified orbrand-specific results are insufficient, performs keyword-based search across all product categories, and applies relevance filtering based on user context and history.

[0071] FIG. 4 is a block diagram that further describes the system 102 from FIG. 1, according to some embodiments of the present disclosure. In some embodiments, receiving and processing user input includes receiving user input through multiple channels and processing the multi-channel input by analyzing voice commands through the speech processing module 114, processing text-based commands through the keyword recognition module 112, and integrating GPS data with search parameters. This includes maintaining context across input channels by preserving search context across input methods, combining location context with user queries, and adapting input processing based on user's preferred input methods.

[0072] FIGS. 5A, 5B, 5C and 5D are flowcharts that describe a method, according to some embodiments of the present disclosure. In some embodiments, at 502, the method may include receiving by an artificial intelligence-based digital assistant module connected to a data storage over a network interface at least one merchant offers' data record via a network interface. At 504, the method may include storing by the artificial intelligence-based digital assistant module the at least one merchant offers' data record in the data storage.

[0073] In some embodiments, at 506, the method may include receiving by the artificial intelligence-based digital assistant module inbound voice data and user geolocation data from a user communication device via the network interface. At 508, the method may include processing the inbound voice data. At 510, the method may include distinguishing purchase-related inputs from standard speech using trained recognition patterns.

[0074] In some embodiments, at 512, the method may include identifying specific elements comprising product specifications, payment preferences, and delivery options. At 514, the method may include generating contextual purchase prompts based on the distinguished purchase-related inputs. At 516, the method may include implementing an adaptive feedback process. At 518, the method may include monitoring user input patterns in real-time.

[0075] In some embodiments, at 520, the method may include interjecting during user inputs based on learned purchase flows. At 522, the method may include providing dynamic guidance for proper voice input format based on historical successful purchase patterns. At 524, the method may include performing a hierarchical search process. At 526, the method may include executing a first search phase that prioritizes brand-specific queries.

[0076] In some embodiments, at 528, the method may include identifying and extracting brand names from the inbound voice data. At 530, the method may include queryingbrand-specific product databases. At 532, the method may include ranking results based on brand relevance scores. At 534, the method may include executing a second search phase when no brand is identified or brand-specific results are insufficient. At 536, the method may include filtering out non-value-adding words while preserving context-specific terms.

[0077] In some embodiments, at 538, the method may include performing keyword-based search across all product categories. At 540, the method may include applying relevance filtering based on user context and history. At 542, the method may include deriving by the artificial intelligence-based digital assistant module at least one keyword from the inbound voice data. At 544, the method may include fetching by the artificial intelligence-based digital assistant module the at least one merchant offers' data record from the data storage.

[0078] In some embodiments, at 546, the method may include transcoding by the artificial intelligence-based digital assistant module the at least one merchant offers' data record to outbound voice data. At 548, the method may include transmitting by the artificial intelligence-based digital assistant module the outbound voice data via network interface to the user communication device. At 550, the method may include continuously improving search accuracy. At 552, the method may include tracking successful purchase completions. At 554, the method may include analyzing patterns in voice inputs that lead to successful purchases. At 556, the method may include updating speech recognition models based on aggregate user data.

[0079] In some embodiments, the method includes steps 502 to 556. These steps involve processing the at least one keyword, inbound location data, results of the hierarchical search process, and user's historical purchase patterns, as well as refining search algorithms based on purchase completion rates. The data storage may comprise a database containing latest offers, business promotions, and live deals within a given geographical area of a user. The trained recognition patterns may be continuously updated based on successful purchase completions. The learned purchase flows may be derived from historical successful transactions. The hierarchical search process may adapt its ranking algorithms based on user-specific purchase patterns.

[0080] FIG. 6 is a flowchart that further describes the method from FIG. 5A, according to some embodiments of the present disclosure. In some embodiments, processing the inbound voice data further comprises the method steps 610 to 650. These steps include filtering results based on proximity to user location, ranking results based on both relevance and distance, presenting geographically-targeted voice results around the vicinity of the user, analyzing user interaction patterns with local offers, tracking successful purchase completions withinspecific geographical areas, and considering temporal relevance of offers and promotions.

[0081] FIG. 7 demonstrates a monetization structure that preferably combines both advertiser-based and subscription-based revenue models. In accordance with step 710 for receiving voice advertisement configurations, the system implements a pay-per-voice (PPV) model where businesses can create and manage voice-optimized advertisements. Advertisers input their promotional content along with specific configuration parameters, including text-format advertisements, trigger keywords for voice activation, budget allocations, target audience parameters, and desired number of voice impressions. Following this, in step 720 for processing voice advertisement configurations, the system processes the submitted advertising content through several steps, including converting submitted text advertisements into voice format, mapping specified trigger keywords to the speech recognition module, storing processed voice advertisements in the data storage, and preparing advertisements for dynamic delivery based on advertiser parameters.

[0082] As outlined in step 730 for monitoring user voice inputs, the speech processing module actively monitors user interactions by identifying advertising keywords in user speech during shopping sessions, analyzing user context and engagement patterns, and utilizing the adaptive feedback module to learn optimal advertisement placement patterns while maintaining natural conversation flow during monitoring. In accordance with step 740 for delivering voice advertisements, advertisement delivery is managed through prioritizing delivery based on advertiser parameters, incorporating advertiser priority in search results through the hierarchical search module, balancing paid and organic results to maintain user experience, and tracking impression counts and budget utilization.

[0083] Finally, as detailed in step 750 for managing monetization, the system implements a dual revenue structure. The advertiser-based model component focuses on tracking voice impression credits, managing budget utilization, and monitoring advertisement performance. Simultaneously, the subscription-based model component offers tiered service levels ranging from basic (with ads) to premium (ad-free), providing enhanced features such as ad-free voice shopping, enhanced product recommendations, priority processing, and advanced customization options, while also managing enterprise-level accounts with sophisticated features.

[0084] In some aspects, the system implements a monetization structure that combines both advertiser-based and subscription-based revenue models. Through an advertisement management module, businesses can create and manage voice-optimized advertisements, selecting specific keywords that trigger their promotional content when voiced by usersduring shopping interactions. This pay-per-voice (PPV) model allows advertisers to purchase voice impression credits, set specific budgets for advertisement delivery, and define precise targeting parameters for their desired audience.

[0085] Further, the advertisement delivery process begins when advertisers input their content in text format, along with selected trigger keywords, desired number of voice impressions, and budget allocations. The system processes this content by converting text advertisements to voice format, mapping the keywords to the speech recognition module, and storing the voice advertisements in the data storage. As users interact with the system, their voice inputs are monitored for keyword matches, triggering relevant voice advertisements when appropriate while maintaining careful tracking of impression counts and budget utilization.

[0086] In parallel with the advertiser-based model, the system offers a subscription-based service where users can opt for a premium experience. This includes ad-free voice shopping, enhanced product recommendations, priority processing, and access to advanced features. The subscription model is structured in tiers, ranging from a basic level that includes advertisements to premium and enterprise levels offering increasingly sophisticated features and customization options.

[0087] The monetization features are integrated with the core system architecture. The speech processing module identifies advertising keywords in user speech and prioritizes advertisement delivery based on advertiser parameters while maintaining natural conversation flow. The adaptive feedback module learns optimal advertisement placement patterns and adjusts delivery based on user engagement metrics. Meanwhile, the hierarchical search module incorporates advertiser priority in search results while maintaining a careful balance between paid and organic results to optimize the overall user experience.

[0088] To ensure transparency and effectiveness, the system preferably implements advanced tracking capabilities that measure advertisement performance through voice impression delivery counts, user engagement metrics, and conversion tracking. Advertisers receive access to real-time performance dashboards, keyword effectiveness reports, and optimization recommendations. The billing and payment processing component manages both advertiser payments based on voice impression delivery and subscription payments through automated billing cycles and usage reporting.

[0089] This monetization approach enables multiple revenue streams while providing flexible advertising options for businesses and enhanced user experience options for shoppers. The system's ability to track and measure performance metrics ensures that both advertisersand subscribers receive measurable value from their investment, while the adaptive nature of the system continuously optimizes the balance between monetization and user experience. Through this dual approach of advertising and subscription revenue, the system maintains financial sustainability while providing value to all stakeholders in the voice shopping ecosystem.INDUSTRIAL APPLICATION

[0090] The voice-based Al system for interactive business promotions has significant industrial applications across e-commerce, retail, telecommunications, and digital marketing sectors. It streamlines consumer access to location-specific merchant offers through natural language processing while enabling businesses to deliver targeted promotions. The system's implementation reduces operational costs for businesses by automating customer interactions and enhances consumer shopping experiences through contextual, personalized recommendations. By integrating geolocation data with adaptive speech recognition, the technology creates new monetization channels for merchants while providing valuable market insights through analysis of consumer interaction patterns and purchase behaviors.

Claims

AMENDED CLAIMS received by the International Bureau on 07 November 2025 (07.11.2025)What is claimed is:

1. A system comprising: a data storage populated with a plurality of merchant offers' data records; an artificial intelligence-based digital assistant module connected to the data storage over a network interface configured for two-way communication between the artificial intelligence-based digital assistant module and a plurality of computing devices wherein: the artificial intelligence-based digital assistant module comprises: an interactive graphical user interface configured to receive text input from a user; at least a first microphone configured to receive an audio input from the user; a geolocation module configured to generate geolocation data of the user; a keyword recognition module configured to process text-based commands from the user and transcode the text-based commands into voice data; a speech processing module configured to: process audio input from the user wherein the speech processing module is further configured to parse the audio input to derive at least one keyword from the audio input; distinguish purchase-related inputs from standard speech through trained recognition patterns; generate contextual purchase prompts based on the distinguished purchase-related inputs; an adaptive feedback module configured to: interject during user inputs based on learned purchase flows; provide real-time guidance to users for proper voice input format based on historical successful purchase patterns; dynamically adjust guidance based on user response patterns; a hierarchical search module configured to: implement a two-step search process that first prioritizes brand-specific queries before performing broader keyword searches;when a brand name is recognized, initially limit search results to that brand's offerings before expanding to related products; when no brand name is detected, proceed with keyword-based search using identified relevant terms; filter out non-value-adding words from search queries to improve search precision; a purchase prompt and confirmation module configured to enable users to complete purchases using stored payment methods and provide delivery or pickup options; a user ratings module configured to allow users to hear ratings for products they are interested in; a product availability notifications module configured to notify users when products are available and facilitate automatic purchases; wherein: the digital assistant module is configured to receive input data comprising voice or text; the digital assistant module is configured to parse the input data for at least one keyword; the digital assistant module is coupled to the data storage and configured to fetch at least one merchant offers' data record based on the at least one keyword; the digital assistant module is configured to transcode the at least one merchant offers' data record into voice data; the digital assistant module is configured to transmit the voice data over the network interface to the plurality of computing devices.

2. The system of claim 1 wherein the speech processing module is further configured to identify and parse specific elements from purchase-related inputs comprising product specifications, payment method preferences, and delivery options, maintain context awareness across multiple user utterances within a single shopping session, and adapt speech recognition patterns based on successful purchase completions.

3. The system of claim 1 wherein the adaptive feedback module is further configured to track common error patterns in user voice inputs, generate personalized correction suggestions based on user's historical interaction patterns, store successful voice inputpatterns for future reference and guidance, and provide progressive guidance by starting with minimal intervention and increasing assistance based on user response.

4. The system of claim 1 wherein the hierarchical search module's two-step search process comprises a first search phase that identifies and extracts brand names from user input, queries brand-specific product databases, and ranks results based on brand relevance scores, and a second search phase that activates when no brand is identified or brand-specific results are insufficient, performs keyword-based search across all product categories, and applies relevance filtering based on user context and history.

5. The system of claim 1 wherein the filter algorithm of the hierarchical search module is configured to maintain a dynamic database of non-value-adding words, analyze word frequency and correlation with successful searches, remove common filler words while preserving context-specific terms, and adapt filtering rules based on search success rates.

6. The system of claim 1 wherein the speech processing module implements a learning algorithm that tracks successful purchase-related voice interactions, identifies patterns in voice inputs that lead to completed purchases, adjusts recognition parameters based on user-specific speech patterns, and maintains separate recognition models for purchase-related and non-purchase speech.

7. The system of claim 1 wherein the adaptive feedback module implements a purchase flow training model that analyzes historical purchase completion data, identifies common points of user hesitation or confusion, generates context-appropriate intervention triggers, and customizes guidance based on product category and user expertise level.

8. A method comprising the steps of: receiving by an artificial intelligence-based digital assistant module connected to a data storage over a network interface at least one merchant offers' data record via a network interface; storing by the artificial intelligence-based digital assistant module the at least one merchant offers' data record in the data storage; receiving by the artificial intelligence-based digital assistant module inbound voice data and user geolocation data from a user communication device via the network interface; processing the inbound voice data by: distinguishing purchase-related inputs from standard speech using trained recognition patterns;identifying specific elements comprising product specifications, payment preferences, and delivery options; generating contextual purchase prompts based on the distinguished purchase-related inputs; implementing an adaptive feedback process comprising: monitoring user input patterns in real-time; interjecting during user inputs based on learned purchase flows; providing dynamic guidance for proper voice input format based on historical successful purchase patterns; performing a hierarchical search process comprising: executing a first search phase that prioritizes brand-specific queries by: identifying and extracting brand names from the inbound voice data; querying brand-specific product databases; ranking results based on brand relevance scores; executing a second search phase when no brand is identified or brand-specific results are insufficient by: filtering out non-value-adding words while preserving context-specific terms; performing keyword-based search across all product categories; applying relevance filtering based on user context and history; deriving by the artificial intelligence-based digital assistant module at least one keyword from the inbound voice data; fetching by the artificial intelligence-based digital assistant module the at least one merchant offers' data record from the data storage based on: the at least one keyword; inbound location data; results of the hierarchical search process; user's historical purchase patterns; transcoding by the artificial intelligence-based digital assistant module the at least one merchant offers' data record to outbound voice data; transmitting by the artificial intelligence-based digital assistant module the outbound voice data via network interface to the user communication device; continuously improving search accuracy by: tracking successful purchase completions;analyzing patterns in voice inputs that lead to successful purchases; updating speech recognition models based on aggregate user data; refining search algorithms based on purchase completion rates; wherein: the data storage comprises a database containing latest offers, business promotions, and live deals within a given geographical area of a user; the trained recognition patterns are continuously updated based on successful purchase completions; the learned purchase flows are derived from historical successful transactions; the hierarchical search process adapts its ranking algorithms based on user-specific purchase patterns.

9. The method according to claim 8 wherein processing the inbound voice data further comprises receiving voice keywords from the user through a trained speech recognition model, determining the geographical location of the user through real-time GPS data, analyzing the voice keywords in conjunction with the geographical location to identify relevant local offers and promotions, filter results based on proximity to user location, rank results based on both relevance and distance, and present geographically-targeted voice results around the vicinity of the user, while continuously updating the local offer database based on user interaction patterns with local offers, successful purchase completions within specific geographical areas, and temporal relevance of offers and promotions.

10. The method according to claim 8 wherein receiving and processing user input comprises receiving user input through multiple channels comprising voice input through at least one microphone, text input through a typing interface, and location data through a global positioning system, processing the multi-channel input by analyzing voice commands through the speech processing module, processing text-based commands through the keyword recognition module, and integrating GPS data with search parameters, while maintaining context across input channels by synchronizing user intent across voice and text inputs, preserving search context across input methods, and combining location context with user queries, and adapting input processing based on user's preferred input methods, historical success rates of different input channels, and current interaction context.

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