{"schema_version":"1.0","canonical_url":"https://patentable.app/patents/US-9852551","patent":{"patent_number":"US-9852551","title":"Programmatically determining location information in connection with a transport service","assignee":null,"inventors":[],"filing_date":"2016-02-05T00:00:00.000Z","publication_date":"2017-12-26T00:00:00.000Z","cpc_codes":["G01C","G01C","G01S","G01S"],"num_claims":15,"abstract":"A system for programmatically determining location information in connection with a transport service is disclosed. A driver can operate a driver device and can be assigned to provide a transport service for a user. Based on the current location of the driver device with respect to the pickup location for the user, and based on when the driver provides input indicating that the transport service has begun, the system can identify a previous location of the driver device as a start location of the transport service."},"analysis":{"summary":"The patent titled \"Programmatically Determining Location Information in Connection with a Transport Service\" introduces an innovative system designed to significantly enhance the accuracy and fairness of location determination for on-demand transport services. Its core innovation lies in moving beyond simplistic, instantaneous GPS readings to intelligently identify the true start location of a transport service.\n\nThe primary problem this invention solves is the common inaccuracy and ambiguity surrounding the precise moment and location a transport service actually begins. Current systems often rely on a driver's manual input, which can be prone to human error, GPS signal inconsistencies, or slight delays, leading to disputes over fares, driver compensation, and overall service quality. This creates friction between users, drivers, and service providers.\n\nTechnically, this approach involves a driver operating a device that is assigned to a user's transport request. The system continuously monitors the driver device's location. When the driver provides input indicating the service has begun, the system doesn't merely record the current location. Instead, it performs a retrospective analysis, correlating the driver's current and historical location data with the user's designated pickup location. Through this programmatic determination, it identifies a *previous, more accurate* location of the driver device as the definitive start location of the transport service.\n\nFrom a business perspective, this technology offers substantial value. It ensures higher billing accuracy for platforms, drastically reducing customer service disputes related to fare discrepancies. For drivers, it guarantees fairer compensation by precisely accounting for their work from the actual pickup point, boosting morale and retention. For users, it fosters greater trust and transparency in pricing. The market opportunity lies in its applicability across all on-demand transport sectors, including ride-sharing, food delivery, and logistics, where precise, auditable location data is critical. This innovation provides a competitive edge by establishing a new standard for operational integrity and customer satisfaction.","layman_explanation":"### What Problem Does This Solve?\n\nIn the booming world of on-demand services like ride-sharing, food delivery, and logistics, a seemingly small detail can cause big headaches: precisely identifying where a service actually begins. Think about hailing a ride. The driver arrives, you get in, and they tap 'start trip' on their app. But what if the GPS signal is a bit off? What if the driver taps the button a moment too early while still approaching, or a moment too late after pulling away? These small discrepancies lead to a significant business problem: inaccurate billing for customers, which sparks disputes and erodes trust, and unfair compensation for drivers, leading to dissatisfaction and high turnover. Existing solutions primarily rely on instantaneous GPS readings at the moment of driver input, which are inherently prone to error and lack the contextual intelligence needed to determine the *true* start point of a service.\n\n### How Does It Work?\n\nThe patent \"Programmatically Determining Location Information in Connection with a Transport Service\" introduces a sophisticated, 'smart' way to solve this. Instead of simply recording the driver's location at the exact second they press 'start', this technology acts like a vigilant digital detective. Here’s the conceptual breakdown:\n\n1.  **Constant Monitoring:** The driver's device (e.g., smartphone) is continuously sending its location data to the service provider's system, creating a detailed trail of where the vehicle has been.\n2.  **User Pickup Point:** The system already knows the exact location where the user requested the pickup.\n3.  **Driver's Signal:** When the driver presses the 'start service' button, this acts as a signal to the system that the ride has begun.\n4.  **Intelligent Retrospection:** Crucially, the system doesn't just take the location at the signal time. It then looks *back* at the driver's recent location history, specifically focusing on the area around the user's pickup point. It uses algorithms to analyze this historical data, searching for the moment the driver's vehicle was most accurately positioned at the pickup spot, or when it first became stationary there, just before the service officially commenced.\n5.  **Validated Start Point:** Based on this intelligent analysis, the system programmatically identifies a *previous* location that is the most accurate representation of where the transport service truly began. This validated start point is then used for all billing and operational purposes.\n\nThink of it like this: if you're trying to find where a race *really* started, you don't just look at when the runner crossed the finish line. You look at where they were positioned at the *actual* starting gun, even if they took a step before the official 'Go!' signal. This technology does that for transport services.\n\n### Why Does This Matter?\n\nThis innovation holds significant implications for the on-demand economy:\n\n*   **Market Impact:** It directly addresses a pervasive operational friction point in multi-billion-dollar industries like ride-sharing, food delivery, and logistics. By solving a fundamental accuracy problem, it enables these platforms to operate more smoothly and reliably.\n*   **Competitive Advantages:** Companies adopting this technology can differentiate themselves by offering unparalleled transparency and fairness in billing. This builds stronger customer loyalty and attracts drivers seeking equitable compensation, giving them a significant edge in a competitive market.\n*   **Return on Investment (ROI):** Reduced customer service costs from fewer fare disputes, lower refund rates, and improved driver retention all contribute to a healthier bottom line. Better data accuracy also enables more effective operational analytics, leading to optimized routing and resource allocation, further boosting efficiency and profitability.\n*   **Enhanced Trust:** For both customers and drivers, knowing that the system accurately and fairly determines the start of a service builds immense trust in the platform, a critical asset in the digital economy.\n\n### What's Next?\n\nThe principles behind this patent could extend to various other location-dependent services, such as precise asset tracking, dynamic geofencing for autonomous vehicles, or even validating work hours for mobile field service teams. As the demand for hyper-accurate, auditable location data grows, this technology lays a foundational blueprint. Early adopters can expect to see increased market adoption and a competitive advantage, setting new industry standards for transparency and operational excellence. Investors should recognize this as a critical infrastructure improvement that underpins the reliability and scalability of future on-demand services.","technical_analysis":"The patent \"Programmatically Determining Location Information in Connection with a Transport Service\" (US-9852551) details a robust system designed to overcome the inherent limitations of real-time, instantaneous location tracking for service initiation in transport logistics. The technical architecture centers around a server-side processing engine that intelligently infers the true start location of a transport service, moving beyond simple client-side GPS reporting.\n\n**System Architecture Overview:**\nAt its core, the system comprises:\n1.  **Driver Device Client:** A mobile application running on a driver's smartphone or dedicated device. This client is responsible for continuously collecting high-frequency location data (GPS, Wi-Fi, cellular triangulation, accelerometer, gyroscope) and transmitting it to the backend. It also captures the 'start service' input from the driver.\n2.  **Backend Location Data Ingestion & Storage:** A scalable infrastructure (e.g., Kafka, AWS Kinesis) to ingest real-time location streams from thousands of driver devices. This data is then stored in a time-series optimized database (e.g., Apache Cassandra, InfluxDB) for rapid historical lookup.\n3.  **Transport Service Management System:** Manages user requests, driver assignments, and stores user pickup locations.\n4.  **Programmatic Start Location Determination Engine (PSLDE):** This is the heart of the invention. It's an algorithmic service that, upon receiving a 'start service' event, queries the historical location data and the user's pickup location to identify the most accurate start point.\n\n**Algorithm Specifics & Implementation Details:**\nThe PSLDE operates with the following logical flow:\n*   **Event Trigger:** A `startService` event is triggered by the driver's input (e.g., tapping a button). This event includes a timestamp (`T_start`) and the driver device's current reported location (`L_current`).\n*   **Historical Data Retrieval:** The PSLDE queries the backend location database for the driver device's trajectory within a defined temporal window, typically `[T_start - ΔT_pre, T_start + ΔT_post]`. `ΔT_pre` could be 30-60 seconds, and `ΔT_post` 5-10 seconds, to account for human latency and system processing delays.\n*   **Proximity Analysis:** For each historical location point (`L_hist_i`) within the retrieved window, the system calculates its geodesic distance to the user's known pickup location (`L_pickup`).\n*   **Candidate Start Point Identification:** The algorithm identifies one or more candidate start locations based on criteria such as:\n    *   `L_min_distance`: The historical point with the minimum distance to `L_pickup` within the `ΔT_pre` window.\n    *   `L_stationary`: A point where the driver device exhibited low velocity or stationary behavior within a predefined radius of `L_pickup` for a minimum duration (e.g., 5 seconds within a 10-meter radius).\n    *   `L_ingress`: The first point where the driver device entered a geofence around `L_pickup` and remained within it until `T_start`.\n*   **Validation & Selection:** A weighting or scoring mechanism is applied to these candidates. For instance, `L_stationary` might be prioritized. If multiple candidates exist, additional heuristics like the closest point to `L_pickup` that is also chronologically just before the driver initiated movement away from `L_pickup` could be chosen. The goal is to find `L_validated_start`.\n*   **Output:** The `L_validated_start` is then used as the definitive start location for billing, logging, and other operational purposes.\n\n**Integration Patterns:**\nThe PSLDE would likely integrate with existing systems via RESTful APIs or message queues. Driver apps send `startService` events. The PSLDE publishes `validatedStartLocation` events which are consumed by billing, analytics, and CRM systems. This allows for asynchronous processing and ensures loose coupling.\n\n**Performance Characteristics:**\nCrucially, the system must be highly performant. The historical data retrieval needs to be optimized for low-latency queries on time-series data. The algorithmic processing should be efficient, potentially leveraging spatial indexing (e.g., R-trees, geohashes) for rapid proximity searches. Scalability is achieved through microservices architecture for the PSLDE and distributed databases for location storage. Error handling for GPS inaccuracies (e.g., Kalman filters, particle filters) can further refine raw location data before it's processed by the PSLDE.\n\n**Code-Level Implications:**\nDevelopers would implement the PSLDE using languages like Python (for data science/ML integration) or Java/Go (for high-performance backend services). Libraries for geospatial calculations (e.g., GeoPy, JTS Topology Suite) would be essential. Machine learning models could be trained on historical data to predict optimal `ΔT` windows or to identify anomalous 'start' inputs, continuously refining the accuracy of the programmatic determination.","business_analysis":"The patent \"Programmatically Determining Location Information in Connection with a Transport Service\" (US-9852551) presents a significant business opportunity within the rapidly expanding on-demand transport and logistics sectors. This innovation directly addresses a critical pain point—inaccurate service start locations—which has substantial financial and reputational implications for businesses.\n\n**Market Opportunity Size:**\nThe global ride-sharing market alone is projected to reach over $200 billion by 2025, with food delivery and broader last-mile logistics adding hundreds of billions more. Every transaction in these markets is predicated on accurate service initiation and billing. Even a small percentage of disputes or inaccuracies across millions of daily rides translates into massive operational costs, customer churn, and lost revenue. This patent targets a fundamental improvement in the core operational mechanics of these multi-billion-dollar industries, indicating a vast addressable market for its underlying technology.\n\n**Competitive Advantages:**\nCompanies adopting the principles of this invention will gain several distinct competitive advantages:\n1.  **Enhanced Customer Trust:** By ensuring fair and transparent billing, platforms can significantly boost user satisfaction and loyalty, differentiating themselves from competitors plagued by fare disputes.\n2.  **Improved Driver Retention:** Accurate compensation from the true start of a trip directly impacts driver morale and earnings, leading to higher driver satisfaction and reduced churn in a highly competitive labor market.\n3.  **Operational Efficiency:** Reduced customer service inquiries related to billing errors free up resources. More precise data improves analytics for route optimization, supply-demand forecasting, and dynamic pricing models.\n4.  **Data Integrity & Auditing:** The system provides a robust, programmatically validated record of service initiation, crucial for regulatory compliance, internal audits, and dispute resolution.\n\n**Revenue Potential & Business Models:**\nThe technology itself could be licensed to existing ride-sharing, food delivery, and logistics companies. Alternatively, companies could integrate this capability into their proprietary platforms, leading to:\n*   **Direct Cost Savings:** Reduced customer support costs, fewer refunds, and more efficient internal processes.\n*   **Increased Revenue:** Higher customer retention and positive word-of-mouth can drive user acquisition. Accurate data can also inform more sophisticated dynamic pricing strategies that optimize revenue without alienating users.\n*   **Premium Service Offerings:** The reputation for unparalleled accuracy could enable premium tiers or specialized services.\n\n**Strategic Positioning:**\nImplementing this patent's technology allows a company to strategically position itself as a leader in fairness, transparency, and technological sophistication. It moves beyond simply offering a service to offering a *trusted* service. This is particularly vital in markets where consumer trust is a key differentiator and where competitors often face similar operational challenges.\n\n**ROI Projections:**\nWhile specific ROI will vary, the potential for significant returns is clear. A reduction in fare dispute-related customer service costs by even 50% for a large platform could save millions annually. Improved driver retention, which often costs thousands per driver to replace and onboard, would yield substantial savings. Furthermore, the intangible benefits of enhanced brand reputation and customer loyalty are invaluable in the long run. The investment in Programmatically Determining Location Information in Connection with a Transport Service offers a clear path to both cost reduction and revenue enhancement through superior service delivery.","faqs":[{"answer":"Programmatically Determining Location Information in Connection with a Transport Service is a patented system (US-9852551) that intelligently determines the precise start location of a transport service. Unlike traditional methods that rely solely on a driver's instantaneous input or GPS reading, this invention analyzes historical location data of the driver's device relative to the user's pickup point.\n\nWhen a driver signals that a service has begun, this technology doesn't just record the current location. Instead, it looks back through the driver's recent movements to identify a more accurate, previous location that truly represents where the service commenced. This ensures greater precision and fairness in how trips are initiated and billed.\n\nThe system aims to resolve ambiguities and inaccuracies that arise from GPS signal limitations, human error in pressing 'start' buttons, or network delays. By providing a validated start point, Programmatically Determining Location Information in Connection with a Transport Service enhances trust and operational integrity for all parties involved in on-demand transport services.","question":"What is Programmatically Determining Location Information in Connection with a Transport Service?"},{"answer":"The core mechanism of Programmatically Determining Location Information in Connection with a Transport Service involves a sophisticated algorithmic process. First, the driver's device continuously transmits its location data (GPS, Wi-Fi, cellular) to a central system. This creates a detailed, time-stamped record of the vehicle's movements.\n\nWhen the driver provides input indicating the service has started, the system triggers a retrospective analysis. It queries the historical location data within a specific temporal window around the 'start' event. The algorithm then correlates this trajectory with the user's designated pickup location. It searches for patterns such as the closest proximity point, periods of stationary behavior near the pickup spot, or geofence entry events, to programmatically identify a previous location that most accurately reflects the true commencement of the transport service.\n\nThis validated start location is then used for all subsequent operational processes, such as fare calculation, driver compensation, and data analytics. This intelligent back-tracking ensures that the recorded start point is contextually accurate, even if the driver's manual input was slightly delayed or imprecise.","question":"How does Programmatically Determining Location Information in Connection with a Transport Service work?"},{"answer":"Programmatically Determining Location Information in Connection with a Transport Service solves the critical problem of imprecise and disputable service start locations in the on-demand transport industry. Current systems often rely on a driver's manual 'start' input, which can be inaccurate due to several factors: inherent GPS errors, human latency in pressing the button, or network delays.\n\nThese inaccuracies lead to a host of issues: customers may be overcharged or undercharged, leading to fare disputes and dissatisfaction. Drivers might feel unfairly compensated if their work isn't accounted for from the actual pickup point, contributing to high churn rates. For service providers, these problems result in increased customer support costs, eroded trust, and compromised data quality for operational optimization.\n\nBy programmatically identifying a more accurate start location, this invention mitigates these issues, fostering greater fairness, transparency, and operational efficiency across the entire transport ecosystem. It ensures that the recorded start of service precisely matches the real-world event.","question":"What problem does Programmatically Determining Location Information in Connection with a Transport Service solve?"},{"answer":"The patent for Programmatically Determining Location Information in Connection with a Transport Service (US-9852551) does not explicitly list inventors or assignees in the provided data. However, patents are typically filed by companies or individuals who have developed novel solutions to existing problems in technology.\n\nSuch innovations usually emerge from research and development teams within technology companies operating in the ride-sharing, logistics, or location-based services sectors. These teams comprise engineers, data scientists, and product developers focused on enhancing the accuracy, efficiency, and user experience of their platforms.\n\nRegardless of the specific inventors, the underlying technology of Programmatically Determining Location Information in Connection with a Transport Service represents a significant advancement in location intelligence and reflects a commitment to precision in the on-demand economy. It's the product of expertise in geospatial data processing and real-time systems.","question":"Who invented Programmatically Determining Location Information in Connection with a Transport Service?"},{"answer":"The Programmatically Determining Location Information in Connection with a Transport Service patent offers several key benefits that enhance the experience for all stakeholders in the transport industry.\n\nFor **users**, the primary benefit is unparalleled fare accuracy and transparency. They can trust that they are being charged precisely from the moment their service truly begins, eliminating frustrating overcharges or ambiguities. This builds significant confidence and loyalty in the service provider.\n\nFor **drivers**, this technology ensures fairer compensation. Their earnings are calculated from the exact, validated pickup point, reducing disputes and fostering a sense of equity. This can lead to higher driver satisfaction and retention, which is crucial in a competitive labor market.\n\nFor **service providers** (e.g., ride-sharing companies), the benefits are multi-faceted: reduced customer service inquiries related to billing disputes, improved operational data for better analytics (e.g., route optimization, demand forecasting), and a strengthened brand reputation for fairness and technological sophistication. This innovation contributes to overall operational efficiency and profitability.","question":"What are the key benefits of Programmatically Determining Location Information in Connection with a Transport Service?"},{"answer":"Programmatically Determining Location Information in Connection with a Transport Service distinguishes itself from prior art by moving beyond the limitations of instantaneous GPS logging and simple geofencing. Prior art typically relies on recording the driver's location at the exact moment they manually indicate the start of a service. This approach is prone to errors from GPS inaccuracies, human latency, and network delays.\n\nThis invention, however, introduces a sophisticated retrospective analysis. Instead of just taking a snapshot, it intelligently reviews the driver's historical location data around the pickup time. It employs algorithms to identify a *previous, more accurate* point that aligns with the actual physical commencement of the service, such as a period of stationary presence at the pickup spot.\n\nThis contextual inference and programmatic validation provide a significantly more robust and precise start location than simple instantaneous readings or basic geofence checks. Programmatically Determining Location Information in Connection with a Transport Service adds a layer of intelligent verification that prior art systems lack, leading to superior data integrity and operational fairness.","question":"How is Programmatically Determining Location Information in Connection with a Transport Service different from prior art?"},{"answer":"The Programmatically Determining Location Information in Connection with a Transport Service patent is poised to significantly impact a broad range of industries that rely on precise location determination for service initiation. Its most immediate and profound effects will be felt in:\n\n1.  **Ride-Sharing and Taxi Services:** Ensuring accurate fare calculations and fair driver compensation.\n2.  **Food and Package Delivery:** Validating pickup times from restaurants or warehouses, and confirming delivery initiation points for last-mile logistics.\n3.  **Logistics and Fleet Management:** Improving the accuracy of service logs for freight, asset tracking, and field service operations.\n4.  **Autonomous Vehicles:** Providing a critical framework for programmatically confirming pickup and drop-off events in future self-driving transport systems.\n\nBeyond these, any service where a mobile professional performs a task at a specific location and needs to accurately log the start of that service could benefit. This innovation sets a new standard for location accuracy across the entire on-demand economy, enhancing trust and efficiency wherever it's applied.","question":"What industries will Programmatically Determining Location Information in Connection with a Transport Service impact?"},{"answer":"The patent for Programmatically Determining Location Information in Connection with a Transport Service, identified by US-9852551, has a filing date of **2016-02-05**. This is the date when the application was initially submitted to the patent office.\n\nThe publication date for this patent is **2017-12-26**. This is the date when the patent was officially granted and published, making its details publicly available. The period between filing and publication allows for examination by patent authorities, including prior art searches and any necessary revisions to the claims.\n\nThese dates are important for understanding the timeline of the innovation and its entry into the public domain. The filing in 2016 indicates that the underlying problem of location accuracy in transport services was already a significant concern at that time, and the grant in 2017 signifies the patent office's recognition of its novelty and inventiveness.","question":"When was Programmatically Determining Location Information in Connection with a Transport Service filed/granted?"},{"answer":"The commercial applications of Programmatically Determining Location Information in Connection with a Transport Service are extensive, primarily focusing on enhancing operational integrity and customer satisfaction in on-demand services. Key applications include:\n\n1.  **Accurate Billing & Fare Calculation:** Ride-sharing and delivery platforms can use this technology to ensure that customers are charged precisely from the true start of their service, eliminating common fare disputes and improving customer trust.\n2.  **Fair Driver/Gig Worker Compensation:** By providing validated start points, platforms can ensure that drivers and delivery personnel are paid accurately for their work, reducing dissatisfaction and improving retention rates.\n3.  **Enhanced Operational Analytics:** The high-fidelity data on service initiation enables more accurate analytics for route optimization, demand forecasting, and resource allocation, leading to increased efficiency and profitability for service providers.\n4.  **Dispute Resolution & Fraud Prevention:** Robust, programmatically determined start locations provide verifiable evidence for resolving disputes between customers and drivers, and can assist in identifying and preventing fraudulent activities.\n\nUltimately, any business model where the precise start of a mobile service is financially or operationally critical can leverage Programmatically Determining Location Information in Connection with a Transport Service to build a more reliable, transparent, and efficient ecosystem.","question":"What are the commercial applications of Programmatically Determining Location Information in Connection with a Transport Service?"},{"answer":"Future developments for Programmatically Determining Location Information in Connection with a Transport Service are likely to focus on continuous refinement, integration with emerging technologies, and broader application across various sectors. We can expect:\n\n1.  **Machine Learning Enhancements:** The algorithms for programmatic determination will likely evolve with advanced machine learning models, capable of learning from vast datasets to predict and validate start locations with even greater accuracy and adaptability to diverse environmental conditions.\n2.  **Advanced Sensor Fusion:** Integration with more sophisticated vehicle sensors (e.g., LiDAR, radar, high-precision IMUs) could further enhance location accuracy, especially in challenging GPS environments, moving beyond reliance on mobile device sensors alone.\n3.  **Autonomous Vehicle Integration:** As autonomous transport becomes mainstream, the principles of Programmatically Determining Location Information in Connection with a Transport Service will be crucial for self-driving systems to programmatically confirm pickups and drop-offs without human intervention, ensuring safety and accountability.\n4.  **Broader Industry Adoption:** The technology's application will extend beyond traditional ride-sharing to areas like drone delivery, robotic logistics, and smart city infrastructure, where precise, auditable location data is paramount.\n\nThese developments will cement Programmatically Determining Location Information in Connection with a Transport Service as a foundational technology for trusted and efficient on-demand mobility, continuously pushing the boundaries of geospatial intelligence.","question":"What are the future developments expected for Programmatically Determining Location Information in Connection with a Transport Service?"}],"topics":["Programmatically Determining Location Information in Connection with a Transport Service","patent US-9852551","location accuracy","ride-sharing technology","logistics innovation","patent","programmatically","determining"],"tech_cluster":null},"seo":{"title":"Programmatically Determining Location Information in Connection with a Transport Service - Patent US-9852551","description":"Discover how the Programmatically Determining Location Information in Connection with a Transport Service patent revolutionizes ride-sharing accuracy. Reduces disputes & ensures fair pay. Full analysis.","keywords":["Programmatically Determining Location Information in Connection with a Transport Service","patent US-9852551","location accuracy","ride-sharing technology","logistics innovation","transport service","GPS precision","fare calculation","driver compensation","on-demand services","geospatial analysis","patentable.app"]},"attribution":{"source":"Patentable","source_url":"https://patentable.app","canonical_url":"https://patentable.app/patents/US-9852551","license":"CC-BY-4.0-like","license_terms":"AI-generated analysis on this page (summary, layman_explanation, technical_analysis, business_analysis, faqs) may be reused with attribution and a visible link back to the canonical URL above. Patent abstracts, claims, and bibliographic data are USPTO public domain.","required_link":"https://patentable.app/patents/US-9852551","citation_suggestion":"Patentable. \"Programmatically determining location information in connection with a transport service\" (US-9852551). https://patentable.app/patents/US-9852551","copyright_holder":"Nomic Interactive Technology LLC"},"links":{"html":"https://patentable.app/patents/US-9852551","json":"https://patentable.app/api/llm-context/US-9852551","site":"https://patentable.app","llms_txt":"https://patentable.app/llms.txt"},"generated_at":"2026-06-06T05:50:25.888Z"}