Obviousness

The obviousness analysis for US Patent 10917483, as of April 26, 2026, focuses on combinations of prior art elements that a person having ordinary skill in the art (PHOSITA) would have been motivated to combine to achieve the claimed invention. Since the full claims text is not provided, this analysis is based on the patent's abstract, summary, definitions, and detailed description, which outline the scope of the invention and its improvements over the described prior art. The "Prior Art" discussed here refers to the technologies and methods described in the Background section of the US10917483 patent as existing before the priority date.

PHOSITA Definition: A PHOSITA in 2017 (the patent's priority date) would be an individual with expertise in software development, machine learning, natural language processing, data aggregation, network communication protocols (including APIs for telecom, messaging, and social networks), and business intelligence systems focused on customer communication management and competitive analysis.

Motivation for Combination: The patent's Background section explicitly details several significant problems in the prior art that businesses faced [cite: "BACKGROUND"]. These problems include:

• The difficulty in keeping communication-based information (e.g., phone numbers, addresses, hours, FAQs) up-to-date and accurate, leading to customer loss [cite: "Modern entities face major problems with trying to keep their communication-based information up-to-date and accurate. Incorrect or outdated information can easily drive customers or users away while enabling competitors to become more effective."].
• The significant manual effort and human judgment required for creating and maintaining FAQs, which often grew stale quickly without any automated update mechanism or identification of information gaps [cite: "In the prior art FAQs took significant effort in identifying and answering even common questions.", "In addition, FAQs often grow stale almost as soon as they are published. ... In the prior art there was just no way to automate updating FAQs or to analyze entity communication-based information to identify information gaps in FAQs or even to automate the delivery of communication-based information based on an analysis of prior communications."].
• Inefficient, inaccurate, and non-automatable methods for identifying competitors, relying on manual categorization or self-identification [cite: "The once rather simple problem being faced by a multitude of modern businesses is identifying who its competitors actually are. In the prior art this was fairly easily handled by manually assigning businesses into certain categories... The foregoing no longer works effectively.", "Other prior art approaches to identifying competitors include allowing customers to identify such competitors, by asking businesses to self-identify, to manually search the internet, to track information from your own suppliers, and to obtain information from on-line services. Those prior art approaches are inefficient, can be manipulated, are costly, are prone to significant errors and cannot be automated."].
• The existence of only "primitive" automation for tracking user journeys, such as e-commerce funnel analytics, with limited scope [cite: "The prior art includes a rather primitive example of automating the following of a user's journey through an entities communication-based information path. E-commerce websites have made use of funnel analytics to better understand why and how potential customer make or do not make purchases."].
• Basic services from online organizations (e.g., Yelp, Facebook, Google) for businesses to update facts, but these were "incomplete and ha[d] little value beyond assisting data entry and form creation" [cite: "For example, Yelp, Facebook, and Google enable businesses to update their open hours and other business facts so that they are both prominently presented and accurate. While such is useful it is also incomplete and has little value beyond assisting data entry and form creation."].

These recognized deficiencies would have provided clear and compelling motivations for a PHOSITA to combine known technologies to develop a more sophisticated, automated, and intelligent solution for managing communication-based business intelligence.

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Obviousness Arguments based on Combinations of Prior Art:

1. Automated Entity Model Creation and Dynamic Updates

• Prior Art Elements:

  • Primitive user journey tracking/funnel analytics in e-commerce: Known for analyzing customer paths [cite: "The prior art includes a rather primitive example of automating the following of a user's journey through an entities communication-based information path. E-commerce websites have made use of funnel analytics to better understand why and how potential customer make or do not make purchases."].
  • General machine learning (ML) and Natural Language Processing (NLP): Widely used by 2017 for data analysis, pattern recognition, sentiment analysis, and understanding human language in text or transcribed voice. The patent itself mentions using NLP in the user interface 42 [cite: "the system 10 is programmed to understand a customer's question such as “What time do you close on Tuesdays?” by using Natural Language Processing (NLP) techniques in the user interface 42."].
  • Automated bots/chatbots: Basic automated response systems were in use, particularly for customer service.
  • API integration with communication platforms: Standard practice for connecting applications to telecommunication carriers, messaging platforms, social networks, and online services to monitor and publish data [cite: "The entity intelligence engine 12 is configured to acquire data from a processor-enabled telecommunication carrier application program interface (“telecom API”) 14 , from a messaging platform API 16 , from a social network platform API 17 , from an online services host 19 , and from a device monitor 18 that handles communications to and from communication devices 20 ."].

• Rationale for Combination: Faced with the problem of manually maintaining outdated FAQs and other business information, and the general need for up-to-date communication-based information [cite: "Modern entities face major problems with trying to keep their communication-based information up-to-date and accurate."], a PHOSITA would be motivated to combine these existing technologies.

  • It would be obvious to leverage ML and NLP to analyze a broader range of "communication-based information" (e.g., texts, voice calls, social media messages) from various channels (via APIs) beyond just e-commerce funnel data, to automatically extract factual information about an entity [cite: "Communication-based information can include texts, voicemails, voice conversations, messages on messaging platforms such as Facebook MessengerTM and Apple's iMessageTM, emails, social media posts and broadcasts, and other social media messaging including photos and direct messages."].
  • Using this analysis to build and maintain an "entity model" encompassing facts like hours, services, and tone would be a logical application of information extraction and knowledge representation techniques.
  • Further, integrating automated bots to handle common queries and then feeding back accepted user responses or corrections into the entity model via ML (a "self-scoring process" or "automated feedback") would be an obvious approach for continuous improvement and to overcome the "stale FAQs" problem [cite: "The foregoing process may also include receiving a first plurality of queries via a network, suggesting a first plurality of responses to the first plurality of queries based on the entity model, accepting selections of the suggested first plurality of responses, and updating the entity model based on the accepted selections.", "the system 10 implements a self-scoring process based on predictions."]. This closed-loop learning mechanism is a fundamental aspect of many intelligent systems. Publishing these updates to various platforms via APIs would be a known method for disseminating information [cite: "That update is then applied to the particular platform (step 310 ).", "The system 10 also implements a publishing server in the telecom carrier interface 32 that publishes the updated fact to any connected services and products that are listening for updates."].

2. Automated Entity Categorization and Competitor Identification

• Prior Art Elements:

  • Manual competitor identification: As noted, prior methods were inefficient and prone to error [cite: "Other prior art approaches to identifying competitors include allowing customers to identify such competitors, by asking businesses to self-identify, to manually search the internet, to track information from your own suppliers, and to obtain information from on-line services. Those prior art approaches are inefficient, can be manipulated, are costly, are prone to significant errors and cannot be automated."].
  • Web crawling: Established technology for gathering publicly available data from the internet (websites, directories).
  • Data analytics and clustering algorithms: Common techniques for grouping data points based on similarities, applied in various fields including market research.

• Rationale for Combination: Given the "major problems" with identifying competitors [cite: "The once rather simple problem being faced by a multitude of modern businesses is identifying who its competitors actually are."], a PHOSITA would be motivated to automate and improve this process.

  • It would be obvious to combine web crawling (to gather published data and initial entity types) with the comprehensive monitoring of communication-based information (metadata like number, time, length, channel, and content via NLP/sentiment analysis) of secondary entities [cite: "analyzing the plurality of communications to determine at least one of a number of communications, time of communications, length of communications, channel of distribution of communications, or communications content of the plurality of secondary entities"].
  • Applying clustering algorithms to this rich, aggregated data would logically group similar entities, thereby identifying competitors more effectively than manual methods [cite: "The system 10 clusters secondary entities that have similar metadata patterns together. For example, businesses that field about the same volume of communications at relatively similar times in similar manners with similar types of customers are clustered together into a competitor cluster."].
  • Furthermore, known techniques for improving data quality and efficiency, such as identifying errors in clustering by cross-referencing published data and communications, and then performing targeted "re-crawls" for specific entities rather than entire networks, would be obvious optimizations to conserve resources and improve accuracy [cite: "The system 10 also identifies errors in existing secondary entity clusters.", "A re-crawl of the network is beneficially performed when such conflict is determined to properly cluster a particular entity. By re-crawling for a specific entity based on such conflict rather than re-crawling a large portion of a network at periodic or frequent intervals, system processing and network bandwidth resources are conserved."].

3. Intelligent Online Conversation Routing based on Conversion Likelihood

• Prior Art Elements:

  • E-commerce funnel analytics: Used to understand why customers make or don't make purchases [cite: "E-commerce websites have made use of funnel analytics to better understand why and how potential customer make or do not make purchases."].
  • Automated response systems/bots: Capable of handling initial customer interactions.
  • Predictive analytics/machine learning: Applied to forecast business outcomes, including sales or customer conversions.
  • Customer service routing systems: Existed to direct customer inquiries to appropriate agents.

• Rationale for Combination: To enhance the effectiveness of customer engagement and prioritize high-value interactions, a PHOSITA would be motivated to apply predictive intelligence to online conversations.

  • It would be obvious to extend the concept of "conversion" from e-commerce funnel analytics to real-time communication sessions (e.g., chat, messaging) [cite: "The system 10 is trained by using a sample of conversations. Ideally these are actual conversations between the primary entity and a secondary entity, such as a customer. Each conversation is tagged as being successful or not based on some definition of success. ... A success is considered a 'conversion'."].
  • Training a "likelihood of conversion model" using ML based on historical communication data and observed conversions would be a direct application of known predictive modeling techniques to a new data set [cite: "training a likelihood of conversion model based on the plurality of communications and on the plurality of conversions"].
  • Integrating this model with an automated response system (bot) to analyze ongoing communications and dynamically decide whether to continue bot interaction or "hand off" the session to a human agent based on the calculated likelihood of conversion would be an obvious optimization strategy for maximizing business outcomes and efficiently allocating human resources [cite: "analyzing the particular communications to determine a likelihood of conversion based on the conversion model, and handing off the particular communication session from the artificial response system based on the likelihood of conversion."]. This effectively translates the "funnel analytics" concept into a real-time, proactive communication management system.

4. Automated Bot Provisioning

• Prior Art Elements:

  • Basic chatbots and bot templates: Existing bots often had challenges with initial setup and provisioning of information [cite: "One challenge with using bots is their initial set up and provisioning with information."]. Bot templates were available to streamline deployment for common use cases.
  • Lookup services: Online services (e.g., Foursquare, Yelp, Google search, reverse phone number lookups) for finding public information associated with identifiers like phone numbers or addresses.
  • Web crawling and data extraction (including OCR for documents): Standard methods for gathering and structuring information from diverse online sources.

• Rationale for Combination: To address the "challenge with using bots" in terms of setting them up and provisioning them with knowledge [cite: "One challenge with using bots is their initial set up and provisioning with information."], a PHOSITA would be motivated to automate this process.

  • It would be obvious to use a "bot template" as a foundation and then automatically populate it with relevant business information by "seeding" with a basic identifier (like a phone number) and initiating network lookup services and web crawls [cite: "the bot template 45 preferably uses old fashion phone numbers as seed information when setting up and provisioning the bot 43 .", "the bot template 45 uses the entity intelligence engine 12 to actively seek out additional information for the entity model 21 and for the entity datastore 26 as well as for provisioning the bot 43 ."].
  • Extracting information from various sources, including processing documents like menus using optical character recognition (OCR) and natural language processing (NLP) to structure the data, and comparing information from multiple datastores to identify mistakes, are well-known data integration and validation techniques that a PHOSITA would readily apply to create a robust and accurate bot knowledge base [cite: "a lookup service may find a portable document format (“PDF”) file or photo of a menu on a website or through another source (such as YelpTM). That menu PDF can be processed by the user interface 42 to extract structured information about the primary entity. ... a restaurant menu can be processed using optical character recognition (OCR) and natural language processing (NLP) techniques at the user interface 42 to extract the menu's contents."].

In conclusion, the innovations described in US10917483, while presenting a comprehensive system, represent the application of well-known information technology, machine learning, and natural language processing techniques to address long-standing and explicitly identified problems in business intelligence and customer communication management. A PHOSITA, motivated by these recognized problems and equipped with knowledge of the available prior art tools, would have found it obvious to combine and integrate these elements in the ways described to achieve the functionalities of the claimed invention.