DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The instant application claims priority to U.S. Provisional App No. 63/726,454, filed on November 29, 2024.
Election/Restrictions
Applicant’s election without traverse of claims 1-18 in the reply filed on May 7, 2026, is acknowledged.
Claims 1-18 are Original, claims 19-26 are canceled, and claims 27-33 are new. All pending claims have been fully considered by Examiner.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
Claim Objections
Claims 1-18 and 27-33 are objected to because of the following informalities:
With respect to claims 1 and 10, the abbreviation “API” on line 3 should be spelled out in full. All dependent claims inherit this deficiency.
With respect to claims 9 and 18, the abbreviations “AI” and “ML” should have been spelled out in full along with the abbreviation in their respective parent claims 1 and 10.
Appropriate correction is required.
With respect to claims 29 and 32, “360 degree”, as recited on lines 3 and lines 3-4 respectively, appear to be typographical errors that should recite “360-degree”.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 4-5 and 10-18, 32-33 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With respect to claims 4-5, each recites on lines 2-3 “learnings and outcomes from the metadata framework and unified logical data models”. It is unclear if this means the previously recited “semantic metadata data framework” or the previously recited “360-degree metadata framework”. Furthermore, if it means “semantic metadata data framework”, it is unclear if “learnings and outcomes from the metadata framework” means the previously recited “learnings and outcomes from the semantic metadata framework”. Also, it is unclear if these are the “dynamic unified data models” as previously recited in respective parent claims 1 and 10. The scope of the claims is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted claims 4 and 5 as reciting “based on the learnings and outcomes from the semantic metadata framework and the dynamic unified logical data models”.
With respect to claims 9 and 18, lines 2-3 recite “business logic, user interface, integration, and AI/ML components”. It is unclear if this is the same as “business logic, user interface, integration, or artificial intelligence/machine learning components”, as previously recited in respective parent claims 1 and 10. For purposes of compact prosecution only, Examiner has interpreted claim 9 as reciting “the business logic, the user interface, the integration, and the artificial intelligence/machine learning components”.
With respect to claim 10, lines 7-8 and 12 recite “the metadata framework”. It is unclear if this means the previously recited “semantic metadata data framework” or the previously recited “360-degree metadata framework”. For purposes of compact prosecution only, Examiner has interpreted claim 10 as reciting “the semantic metadata framework” on lines 7-8 on 12.
Claims 11-18 and 32-33 inherit this deficiency.
With respect to claim 13, lines 2-3 recite “composing business logic by aggregating learnings and outcomes from the semantic metadata framework and unified logical data models.” It is unclear if this is the same as the recitation of “business logic”, “learnings and outcomes”, and “dynamic unified logical data models” in parent claim 10. For purposes of compact prosecution only, Examiner has interpreted claim 13 as reciting “the business logic” and “the learnings and outcomes from the semantic metadata framework and the dynamic unified logical data models”.
With respect to claim 14, lines 2-3 recite “learnings and outcomes from the semantic metadata framework and unified logical data models.” It is unclear if this is the same as the recitation of “learnings and outcomes” and “dynamic unified logical data models” in parent claim 10. For purposes of compact prosecution only, Examiner has interpreted claim 14 as reciting “the learnings and outcomes from the semantic metadata framework and the dynamic unified logical data models”.
With respect to claim 29, lines 5-6 recite “the application ingredients including business logic, user interface, integration, and artificial intelligence or machine learning components”. It is unclear if this is the same as “business logic, user interface, integration, or artificial intelligence/machine learning components”, as previously recited in parent claim 1. The scope of the claim is therefore indefinite. For purposes of compact prosecution only, Examiner has interpreted claim 29 as reciting “the application ingredients including the business logic, the user interface, the integration, and the artificial intelligence/machine learning components”.
With respect to claim 33, the claim recites similar limitations to claim 29 and is indefinite for the same reason and has been interpreted similarly by Examiner.
With respect to claim 30, line 1 recites “the processor”. Parent claim 1 recites “a processor configured to establish a 360-degree metadata framework”, “a processor configured to establish dynamic unified logical data models from the 360-degree metadata framework”, and “a processor configured to learn, create, and house data with semantics embedded”. It is unclear which of these is being referred to in claim 30, which renders the scope of the claim indefinite. For purposes of compact prosecution only, Examiner has interpreted claim 30 as reciting “processor configured to establish dynamic unified logical data models from the 360-degree metadata framework”.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4, 5, 6, 10, 12, 13, 14, 15 27, 29, 30, 32, and 33 are rejected under 35 U.S.C. 103 as being unpatentable over Tran, “How Salesforce’s Einstein 1 Platform Transforms Customer Experiences with CRM, AI, Data, and Trust” (hereinafter Tran) in view of Norris “A Visual Guide to Salesforce Data Cloud Capabilities” (hereinafter Norris) and Salesforce, Inc., “Agentforce and AI App Development” (hereinafter Salesforce 1).
With respect to claim 1, Tran discloses A system for application composition (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that … supports app building; p. p. 2, 3rd para., The metadata framework enables customers to build apps.), comprising:
a semantic metadata framework configured to receive framework input comprising at least one of data definitions, external data, internal data, digital assets, user interface designs, API learnings, or sensor data, or a combination of two or more thereof (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework [receive framework input], enabling a 360-degree view of customers and activation of the data across CRM applications; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data; see also p. 2, 5th full para., Metadata Platform also holds valuable information about user interactions and business processes … Generative AI utilizes this valuable metadata to provide customized and grounded responses.);
a processor configured to establish a 360-degree metadata framework based on the framework input (Id., particularly, p. 1, The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications.);
;
a processor configured to learn, create, and house data (e.g., p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data, which is key to Einstein 1 Platform enabling relevant action and unlocking new insights.); and
a semantic composition framework configured to receive learnings and outcomes from the semantic metadata framework, , and thedata (e.g., p. 2, top para. – 4th para., Einstein 1 Platform’s generative AI capabilities are enabled through the integration of the Metadata Platform [semantic metadata framework] and Unified Data Layer. These two components work together to provide the necessary foundation for AI-driven processes and personalized customer strategies. This empowers businesses to create tailored customer experiences … It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data, which is key to Einstein 1 Platform enabling relevant action and unlocking new insights.).), and to compose application ingredients including at least one of business logic, user interface, integration, or artificial intelligence/machine learning components, or a combination of two or more thereof (e.g., p. 2, § Unleashing the power of unified data and metadata platforms, Einstein 1 Platform’s Metadata Platform is a powerful tool that configures and extends Salesforce to solve unique business challenges, enhance customer engagement, and achieve superior results. It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces. These customizations are instantly available to the rest of the organization’s core applications without having to write costly and often unstable integration code … The metadata framework enables customers to build apps that continue to work for years.).
Tran does not appear to disclose the following, which is taught in analogous art, Norris: a processor configured to establish dynamic unified logical data models from the 360-degree metadata framework (e.g., p. 2, last para., Data sources have differing schemas. Structured data will have table and column names that differ but need to be represented in the same way. Data mapping is the capability that maps your source data to a common data model. For example, Contacts, Guests, Customers, and People are all individuals with the same attributes and map to the Individual object in Data Cloud; p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.) … with semantics embedded (e.g., p.4, § Vector search, Unstructured information, such as free-form text, chat transcripts, PDFs, and emails can be brought into Data Cloud, broken into meaningful chunks, and converted into machine-readable vector embeddings. These vector embeddings are then added to a search index, which can be used to perform vector search queries from apps like Prompt Builder, Agentforce, or Tableau) … the dynamic unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.) … semantic (e.g., p.4, § Vector search, Unstructured information, such as free-form text, chat transcripts, PDFs, and emails can be brought into Data Cloud, broken into meaningful chunks, and converted into machine-readable vector embeddings. These vector embeddings are then added to a search index, which can be used to perform vector search queries from apps like Prompt Builder, Agentforce, or Tableau).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris because Tran discloses that Data Cloud is a component of Tran’s Einstein 1 Platform (e.g., p. 1, The Unified Data Layer, also known as Data Cloud; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data) and “Understanding its capabilities is key for developers creating more data-driven applications that deliver better customer experiences,” as suggested by Norris (see p. 1, top para.).
Tran as modified does not appear to disclose the following, which is taught in analogous art, Salesforce 1: zero-code (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data. Empower business users, admins, and developers to embed AI into every business solution quickly with no code.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the no code development invention of Salesforce 1, such that it is no code, because it is part of the same Salesforce Einstein ecosystem and it allows users with no coding knowledge to develop AI-powered apps.
With respect to claim 10, Tran discloses A method for application composition (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that … supports app building; p. p. 2, 3rd para., The metadata framework enables customers to build apps.), comprising:
receiving, by a semantic metadata framework, framework input comprising at least one of data definitions, external data, internal data, digital assets, user interface designs, API learnings, and sensor data (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework [receive framework input], enabling a 360-degree view of customers and activation of the data across CRM applications; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data; see also p. 2, 5th full para., Metadata Platform also holds valuable information about user interactions and business processes … Generative AI utilizes this valuable metadata to provide customized and grounded responses.);
establishing, by a processor, a 360-degree metadata framework based on the framework input (Id., particularly, p. 1, The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications.);
;
learning, creating, and housing, by a processor, data ; and
composing, by a semantic composition framework, application ingredients including business logic, user interface, , and artificial intelligence/machine learning components (e.g., p. 2, § Unleashing the power of unified data and metadata platforms, Einstein 1 Platform’s Metadata Platform is a powerful tool that configures and extends Salesforce to solve unique business challenges, enhance customer engagement, and achieve superior results. It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces. These customizations are instantly available to the rest of the organization’s core applications without having to write costly and often unstable integration code … The metadata framework enables customers to build apps that continue to work for years.), based on learnings and outcomes from the metadata framework, , and data (e.g., p. 2, top para. – 4th para., Einstein 1 Platform’s generative AI capabilities are enabled through the integration of the Metadata Platform [semantic metadata framework] and Unified Data Layer. These two components work together to provide the necessary foundation for AI-driven processes and personalized customer strategies. This empowers businesses to create tailored customer experiences … It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces; (e.g., p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data, which is key to Einstein 1 Platform enabling relevant action and unlocking new insights.).
Tran does not appear to disclose the following, which is taught in analogous art, Norris: establishing, by a processor, dynamic unified logical data models from the metadata framework (e.g., p. 2, last para., Data sources have differing schemas. Structured data will have table and column names that differ but need to be represented in the same way. Data mapping is the capability that maps your source data to a common data model. For example, Contacts, Guests, Customers, and People are all individuals with the same attributes and map to the Individual object in Data Cloud; p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.) … with semantics embedded (e.g., p.4, § Vector search, Unstructured information, such as free-form text, chat transcripts, PDFs, and emails can be brought into Data Cloud, broken into meaningful chunks, and converted into machine-readable vector embeddings. These vector embeddings are then added to a search index, which can be used to perform vector search queries from apps like Prompt Builder, Agentforce, or Tableau) … integration (e.g., p. 7, § Automation, You can build automation workflows based on data changing in Data Cloud, get records, and start administration jobs using Flow Builder to orchestrate changes within the context of Salesforce data. For example: … Get records from a data model object) … the unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.) … semantic (e.g., p.4, § Vector search, Unstructured information, such as free-form text, chat transcripts, PDFs, and emails can be brought into Data Cloud, broken into meaningful chunks, and converted into machine-readable vector embeddings. These vector embeddings are then added to a search index, which can be used to perform vector search queries from apps like Prompt Builder, Agentforce, or Tableau.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris because Tran discloses that Data Cloud is a component of Tran’s Einstein 1 Platform (e.g., p. 1, The Unified Data Layer, also known as Data Cloud; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data) and “Understanding its capabilities is key for developers creating more data-driven applications that deliver better customer experiences,” as suggested by Norris (see p. 1, top para.).
Tran as modified does not appear to disclose the following, which is taught in analogous art, Salesforce 1: zero-code (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data. Empower business users, admins, and developers to embed AI into every business solution quickly with no code.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the no code development invention of Salesforce 1, such that it is no code, because it is part of the same Salesforce Einstein ecosystem and it allows users with no coding knowledge to develop AI-powered apps.
With respect to claim 3, Tran also discloses wherein the semantic metadata framework is further configured to handle (e.g., p. 1, This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications.) both structured and unstructured data (e.g., p. 2, Data Cloud unlocks your data; the figure/graphic on p. 2, which states “Connect any data from anywhere” and “Structured · Semistructured · Unstructured”).
With respect to claims 4 and 13, Tran also discloses wherein the semantic composition framework is configured to compose business logic by aggregating learnings and outcomes from the metadata framework and (please note the 35 USC 112(b) rejection of claim 13 and interpretation above; e.g., p. 1, 2nd para., The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data, which is key to Einstein 1 Platform enabling relevant action and unlocking new insights; p. 2, 1st – 4th paras., Einstein 1 Platform’s generative AI capabilities are enabled through the integration of the Metadata Platform [semantic metadata framework] and Unified Data Layer [unified logical data and the data]. These two components work together to provide the necessary foundation for AI-driven processes and personalized customer strategies. This empowers businesses to create tailored customer experiences … It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces; p. 2, Data Cloud unlocks your data; the figure/graphic on p. 2, which states “Resolve customer identities”.) and Norris further teaches unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud techniques of Norris for the same reason set forth above.
With respect to claims 5 and 14, Tran also discloses wherein the semantic composition framework is configured to compose user interface elements based on learnings and outcomes from the metadata framework and (please note the 35 USC 112(b) rejection of claim 5 above; e.g., p. 1, 2nd para., The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data, which is key to Einstein 1 Platform enabling relevant action and unlocking new insights; p. 2, 1st – 4th paras., Einstein 1 Platform’s generative AI capabilities are enabled through the integration of the Metadata Platform [semantic metadata framework] and Unified Data Layer [unified logical data and the data]. These two components work together to provide the necessary foundation for AI-driven processes and personalized customer strategies. This empowers businesses to create tailored customer experiences … It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces; p. 2, Data Cloud unlocks your data; the figure/graphic on p. 2, which states “Resolve customer identities”.) and Norris further teaches unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud techniques of Norris for the same reason set forth above.
With respect to claims 6 and 15, Tran also discloses wherein the semantic composition framework is configured to compose integration components for connecting with external systems and APIs (e.g., p. 7, § Automation, You can build automation workflows based on data changing in Data Cloud, get records, and start administration jobs using Flow Builder to orchestrate changes within the context of Salesforce data. For example: … Get records from a data model object; p. 6, § APIs, You can programmatically extract your data from Data Cloud using APIs. Retrieve metadata and query key data points using well-defined REST APIs … For Python developers, there’s a connector that uses the Query API and extracts data from Data Cloud. The Models API (Beta) provides Apex classes and REST endpoints that connect your application to large language models (LLMs) from Salesforce partners, including Anthropic, Google, and OpenAI. The capabilities of the Models API are expressed as Apex methods or REST endpoints; p. 7, § Data action, Integrate data actions in Mulesoft Anypoint by sharing aggregated event data with external partners based on criteria; p. 10, 1st para., Data Cloud allows you to use various tools and products to analyze your data with direct data connectors for Salesforce, CRM Analytics, and Tableau. Developers can also use the APIs provided for programmatic access).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud techniques of Norris for the same reason set forth above.
With respect to claim 12, Tran also discloses handling both structured and unstructured data in the semantic metadata framework (e.g., p. 2, Data Cloud unlocks your data; also see the figure/graphic on p. 2, which states “Connect any data from anywhere” and “Structured · Semistructured · Unstructured”; p. 1, This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications.).
With respect to claim 27, Tran also discloses wherein the semantic metadata framework is configured to learn the framework input as metadata independently of a business context of underlying data (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data; see also p. 2, 5th full para., Metadata Platform also holds valuable information about user interactions and business processes … Generative AI utilizes this valuable metadata to provide customized and grounded responses.), such that the system treats the framework input as attributes or pieces of information without requiring predefined data models or semantic meanings (e.g., p. 2, Data Cloud unlocks your data; the figure/graphic on p. 2, which states “Connect any data from anywhere” and “Structured · Semistructured · Unstructured”).
With respect to claim 29, Tran also discloses wherein the system is further configured to implement a three phase framework comprising: a data thinking phase that establishes the 360 degree metadata framework and (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data; see also p. 2, 5th full para., Metadata Platform also holds valuable information about user interactions and business processes … Generative AI utilizes this valuable metadata to provide customized and grounded responses; p. 2, Data Cloud unlocks your data; the figure/graphic on p. 2, which states “Resolve customer identities”.); a design thinking phase that composes the application ingredients including business logic, user interface, , and artificial intelligence or machine learning components (e.g., p. 2, top para. – 4th para., Einstein 1 Platform’s generative AI capabilities are enabled through the integration of the Metadata Platform [semantic metadata framework] and Unified Data Layer [dynamic unified logical data and the data]. These two components work together to provide the necessary foundation for AI-driven processes and personalized customer strategies. This empowers businesses to create tailored customer experiences … It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces.); and a user acceptance thinking phase that composes applications based on learnings and outcomes from the data thinking and design thinking phases (Id.; p. 2, 4th para., These customizations are instantly available to the rest of the organization’s core applications without having to write costly and often unstable integration code … The metadata framework enables customers to build apps that continue to work for years, even with upgrades in technologies or new functionality added.) and Norris further teaches the dynamic unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.) … integration (e.g., p. 7, § Automation, You can build automation workflows based on data changing in Data Cloud, get records, and start administration jobs using Flow Builder to orchestrate changes within the context of Salesforce data. For example: … Get records from a data model object) and Salesforce 1 further teaches zero code (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data. Empower business users, admins, and developers to embed AI into every business solution quickly with no code.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris and the no code development invention of Salesforce 1 for the same reasons set forth above.
With respect to claim 30, Norris furth teaches wherein the processor is further configured to automatically group attributes within the framework input into the dynamic unified logical data models based on metadata level rules defined in the semantic metadata framework (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company. Identity resolution, Built-in matching and reconciliation rules can identify data that is likely to be related to the same person or business. Matching rules allow you to add criteria based on data in the common data model with match methods, such as fuzzy matching (where entries are approximately similar but not identical), exact matching, and normalized exact matching (where entries are transformed to address issues like trailing spaces, inconsistent formatting, special characters, etc)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris and the no code development invention of Salesforce 1 for the same reasons set forth above.
With respect to claim 32, Tran also discloses treating, by the semantic metadata framework, the framework input as metadata attributes independently of a business context of underlying data, such that establishing the 360 degree metadata framework and is performed without predefined data models or semantics (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications; p. 2, Data Cloud unlocks your data; the figure/graphic on p. 2, which states “Connect any data from anywhere” and “Structured · Semistructured · Unstructured”; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data.) and Norris further teaches the dynamic unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris and the no code development invention of Salesforce 1 for the same reasons set forth above.
With respect to claims 33, Tran also discloses executing a data thinking phase in which the semantic metadata framework and are established (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework, enabling a 360-degree view of customers and activation of the data across CRM applications; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data; see also p. 2, 5th full para., Metadata Platform also holds valuable information about user interactions and business processes … Generative AI utilizes this valuable metadata to provide customized and grounded responses.);
executing a design thinking phase in which the semantic composition framework composes the application ingredients including business logic, user interface, , and artificial intelligence or machine learning components (e.g., p. 2, top para. – 4th para., Einstein 1 Platform’s generative AI capabilities are enabled through the integration of the Metadata Platform [semantic metadata framework] and Unified Data Layer [dynamic unified logical data and the data]. These two components work together to provide the necessary foundation for AI-driven processes and personalized customer strategies. This empowers businesses to create tailored customer experiences … It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces.); and
executing a user acceptance thinking phase in which applications are composed and refined based on learnings and outcomes from the data thinking and design thinking phases (Id.; p. 2, 4th para., These customizations are instantly available to the rest of the organization’s core applications without having to write costly and often unstable integration code … The metadata framework enables customers to build apps that continue to work for years, even with upgrades in technologies or new functionality added.) and Norris further teaches the dynamic unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.) … integration (e.g., p. 7, § Automation, You can build automation workflows based on data changing in Data Cloud, get records, and start administration jobs using Flow Builder to orchestrate changes within the context of Salesforce data. For example: … Get records from a data model object) and Salesforce 1 further teaches zero code (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data. Empower business users, admins, and developers to embed AI into every business solution quickly with no code.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris and the no code development invention of Salesforce 1 for the same reasons set forth above.
Claims 2, 9, 11, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Tran in view of Norris and Salesforce 1, as applied to claims 1 and 10 above, and further in view of Singh “How to Create a Continuous Optimization Loop with Salesforce Einstein and Data Cloud” (hereinafter Singh) and Inspire Planner, “Top Use Cases for Salesforce Einstein (hereinafter Inspire).
With respect to claims 2 and 11, Salesforce 1 further teaches wherein the system enables and composition of zero-code applications for deployment across internal applications (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data … Build, test, and deploy every solution securely, and with governance, using native tools that make it easy to follow modern DevOps processes on Salesforce.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Salesforce 1 for the same reason set forth above.
Salesforce does not appear to disclose the following, which is taught in analogous art, Singh: bidirectional continuous learning (e.g., pp. 1-2, Salesforce’s Einstein 1 Platform, a comprehensive suite of AI-powered tools, offers immense capabilities, and when combined with a continuous optimization loop, it becomes a game-changer. Utilizing the Einstein 1 Platform and Data Cloud, admins can allow users to easily give feedback, create reports to get a high-level view of the quality of responses, and set up flows to get alerted on feedback or audit data and take appropriate action. In this post, you’ll learn how to set up a continuous loop of feedback to help improve your users’ AI experience on the Einstein 1 Platform.; p. 2, 3rd full para., To opt in to store feedback in your Data Cloud instance, start by turning on Einstein generative AI and feedback data collection and storage from Einstein Setup; p. 8, 1st para., When you enable generative AI features for your users, user feedback and audit logs are saved in Data Cloud. This makes it easy for you to gather insights from audit data and feedback and share them with your team, and continuously optimize the quality and efficacy of your users’ experience with the Einstein 1 Platform; see also pp. 2-7.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Singh because it “can help your organization stay ahead of the curve and drive success for the business”, as suggested by Singh (see p. 8, 2nd para.)
Salesforce does not appear to disclose the following, which is taught in analogous art, Inspire: and external (e.g., pp. 4-5, § 4. Salesforce Einstein for Commerce, Einstein Commerce Insights: Provide user behavior specific offerings on products, product bundles, and deals by leveraging Customer persona specific Cart analysis. Einstein Predictive Sort: Customized view for web pages like search and category page on the blink of an eye based on Customer behavior).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Inspire because customers would like that the shopping experience is “more personal with product recommendations, user-specific product sorting and merchandise insights”, as suggested by Inspire (see p. 4).
With respect to claims 9 and 181, Tran also discloses wherein the system enables business logic, user interface, , and AI/ML components (e.g., p. 2, § Unleashing the power of unified data and metadata platforms, Einstein 1 Platform’s Metadata Platform is a powerful tool that configures and extends Salesforce to solve unique business challenges, enhance customer engagement, and achieve superior results. It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation, Flow for automation, and Lightning for user interfaces. These customizations are instantly available to the rest of the organization’s core applications without having to write costly and often unstable integration code … The metadata framework enables customers to build apps that continue to work for year.) and Norris further teaches integration (e.g., p. 7, § Automation, You can build automation workflows based on data changing in Data Cloud, get records, and start administration jobs using Flow Builder to orchestrate changes within the context of Salesforce data. For example: … Get records from a data model object) and Salesforce 1 further teaches real-time interoperability (see pp. 1-2, particularly the figure which includes “real-time data” with arrows going to and from Data Cloud).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the techniques of Norris and Salesforce 1 for the same reasons set forth above.
Tran as modified does not appear to disclose the following, which is taught in analogous art, Singh: and continuous refinement of (e.g., pp. 1-2, Salesforce’s Einstein 1 Platform, a comprehensive suite of AI-powered tools, offers immense capabilities, and when combined with a continuous optimization loop, it becomes a game-changer. Utilizing the Einstein 1 Platform and Data Cloud, admins can allow users to easily give feedback, create reports to get a high-level view of the quality of responses, and set up flows to get alerted on feedback or audit data and take appropriate action. In this post, you’ll learn how to set up a continuous loop of feedback to help improve your users’ AI experience on the Einstein 1 Platform.; p. 2, 3rd full para., To opt in to store feedback in your Data Cloud instance, start by turning on Einstein generative AI and feedback data collection and storage from Einstein Setup; p. 8, 1st para., When you enable generative AI features for your users, user feedback and audit logs are saved in Data Cloud. This makes it easy for you to gather insights from audit data and feedback and share them with your team, and continuously optimize the quality and efficacy of your users’ experience with the Einstein 1 Platform; see also pp. 2-7.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Singh because it “can help your organization stay ahead of the curve and drive success for the business”, as suggested by Singh (see p. 8, 2nd para.)
Claims 7 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Tran in view of Norris and Salesforce 1, as applied to claims 1 and 10 above, and further in view of Salesforce, Inc. “Meet Einstein Discovery” (hereinafter Salesforce 2).
With respect to claims 7 and 16, Tran also discloses wherein the semantic composition framework is configured to compose artificial intelligence and machine learning components for (e.g., p. 2, § Unleashing the power of unified data and metadata platforms, Einstein 1 Platform’s Metadata Platform is a powerful tool that configures and extends Salesforce to solve unique business challenges, enhance customer engagement, and achieve superior results. It allows organizations to customize every user experience and take action on their data using a variety of low-code platform services — including Einstein AI for predictions and content generation … The metadata framework enables customers to build apps that continue to work for years.).
Tran does not appear to disclose the following, which is taught in analogous art, Salesforce 2: predictive and prescriptive analytics (e.g., pp. 3-4, § Generate Predictions and Improvements, Models are based on a comprehensive, statistical understanding of past outcomes that are used to predict future outcomes and suggest improvements. Einstein Discovery supports rapid model development, deployment, and automated maintenance. You can explore your model's predictions as what-if scenarios: select a variable (or combination of variables) to predict outcomes. Select an actionable variable to see ways to improve predicted outcomes. prediction is a derived value, produced by a model, that represents a possible future outcome. Predictors are the variables that contribute to the predicted outcome. Top predictors contribute most significantly. An improvement is a suggested action that a user can take to improve the predicted outcome. Improvements are associated with actionable variables which users can possibly control, such as the shipping method or a subscriber’s membership level.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Salesforce 2 because it would allow users to improve prediction outcomes, as suggested by Salesforce 2 (see pp. 3-4, § Generate Predictions and Improvements).
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Tran in view of Norris and Salesforce 1, as applied to claims 1 and 10 above, and further in view of DiCesare “Model-Driven Development: The Foundation of Low-Code” (hereinafter DiCesare).
With respect to claim 8, Salesforce 1 further discloses wherein the system supports application composition and deployment (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data. Empower business users, admins, and developers to embed AI into every business solution quickly with no code.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Salesforce 1 for the same reason set forth above.
Tran as modified does not appear to disclose the following, which is taught in analogous art, DiCesare: without generating, packaging, or maintaining software code (e.g., p. 3, § How to build a model-driven application without code, 3rd para., In true model-driven applications, the model itself is executable in the runtime with no need for code. When you don’t have to write and troubleshoot code, the process is exponentially faster and the finished application is higher quality.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the technique of DiCesare because “When you don’t have to write and troubleshoot code, the process is exponentially faster and the finished application is higher quality”, as suggested by DiCesare (see p. 3, § How to build a model-driven application without code, 3rd para.).
With respect to claim 17, Salesforce 1 further discloses supporting unlimited application composition and deployment (e.g., p. 1, top para., Create and deploy custom AI powered apps across your organization, grounded in your data. Empower business users, admins, and developers to embed AI into every business solution quickly with no code.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Salesforce 1 for the same reason set forth above.
Tran as modified does not appear to disclose the following, which is taught in analogous art, DiCesare: without generating, packaging, or maintaining software code (e.g., p. 3, § How to build a model-driven application without code, 3rd para., In true model-driven applications, the model itself is executable in the runtime with no need for code. When you don’t have to write and troubleshoot code, the process is exponentially faster and the finished application is higher quality.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the technique of DiCesare because “When you don’t have to write and troubleshoot code, the process is exponentially faster and the finished application is higher quality”, as suggested by DiCesare (see p. 3, § How to build a model-driven application without code, 3rd para.).
Claims 28 and 31 rejected under 35 U.S.C. 103 as being unpatentable over Tran in view of Norris and Salesforce 1, as applied to claim 1 above, and further in view of Salesforce, Inc. “Understanding Identity Resolution Rules” (hereinafter Salesforce 3).
With respect to claim 28, Norris also teaches define the framework input and to specify the dynamic unified logical data models (e.g., p. 2, § Represent data in a consistent format, Data sources have differing schemas. Structured data will have table and column names that differ but need to be represented in the same way. Data mapping is the capability that maps your source data to a common data model. For example, Contacts, Guests, Customers, and People are all individuals with the same attributes and map to the Individual object in Data Cloud; p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company. Identity resolution, Built-in matching and reconciliation rules can identify data that is likely to be related to the same person or business. Matching rules allow you to add criteria based on data in the common data model with match methods, such as fuzzy matching (where entries are approximately similar but not identical), exact matching, and normalized exact matching (where entries are transformed to address issues like trailing spaces, inconsistent formatting, special characters, etc.); see also pp. 3-4, § Data source objects, § Data lake objects, and § Data model objects.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the invention of Norris for the same reason set forth above.
Tran as modified does not appear to disclose the following, which is taught in analogous art, Salesforce 3: a configuration user interface configured to allow a user to … via one or more graphical tools including at least one of drag and drop components, forms, or spreadsheet style import templates (e.g., p. 2, which examiner notes depicts a user interface form for defining rules.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the technique of Salesforce 3 because the interface is user-friendly with English rules that are easy to understand.
With respect to claim 31, Tran also discloses wherein the semantic metadata framework is further configured to interpret incoming data and route the data into (e.g., p. 1, Salesforce has unveiled its Einstein 1 Platform, which integrates Salesforce’s suite of applications that span sales, service, marketing, ecommerce, analytics, and industry solutions … This integration is delivered via Salesforce’s Metadata Platform, a powerful metadata framework [semantic metadata framework] that launched in 2008 and supports app building and customization while ensuring secure and compliant data usage. The Unified Data Layer, also known as Data Cloud, followed more recently, enabling customers to bring all of their company and customer data to Salesforce. The Unified Data Layer harmonizes the various data models from each connected system and maps it into Salesforce’s metadata framework [receive framework input], enabling a 360-degree view of customers and activation of the data across CRM applications; p. 3, top, Data Cloud unifies and harmonizes all of a customers’ Salesforce and third-party data; see also p. 2, 5th full para., Metadata Platform also holds valuable information about user interactions and business processes … Generative AI utilizes this valuable metadata to provide customized and grounded responses; p. 2, Data Cloud unlocks your data; also see the figure/graphic on p. 2, which states “Connect any data from anywhere” and “Structured · Semistructured · Unstructured … Resolve customer identities”.) and Norris further teaches receive rule definitions (e.g., p. 4, Identity resolution, Built-in matching and reconciliation rules can identify data that is likely to be related to the same person or business. Matching rules allow you to add criteria based on data in the common data model with match methods, such as fuzzy matching (where entries are approximately similar but not identical), exact matching, and normalized exact matching (where entries are transformed to address issues like trailing spaces, inconsistent formatting, special characters, etc).) … the dynamic unified logical data models (e.g., p. 4, Unify data sources for the same person and business, Data about your customers and businesses is typically stored across a number of systems. Each source likely has some overlap but also contains unique details, and it is sometimes difficult to know if records across these systems relate to the same person without a common identifier. Identity resolution helps connect these dots, creating a single, comprehensive profile [dynamic unified logical data models] for each customer or company.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Tran with the Data Cloud technique of Norris for the same reason set forth above.
Tran as modified does not appear to disclose the following, which is taught in analogous art, Salesforce 3: expressed in a human readable language describing how to (e.g., p. 1, which examiner notes depicts a user interface with rule definitions in English, e.g., Fuzz Name AND Normalized Email.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to further modify the invention of Tran with the technique of Salesforce 3 because the interface is user-friendly with English rules that are easy to understand.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Specifically, Manzano et al. US 20240412157 A1 discloses programmatically generating and deploying an integration application based on a natural language request without requiring any coding by a user.
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/STEPHEN D BERMAN/ Examiner, Art Unit 2192
1 Examiner notes that while the scope of claims 9 and 18 differ, it is merely to the extent that claim 18 requires only “continuous refinement of at least one of business logic, user interface, integration, or AI/ML components, or a combination of two or more thereof” (emphasis added), whereas claim 9 requires “continuous refinement of business logic, user interface, integration, and AI/ML components”. Thus for the purposes of readability, the rejections of these claims have been combined.
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