DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of the Claims
Claims 1-20 were previously pending and subject to a Notice of Allowance office action mailed 10/14/2025. Claims 1, 10 and 15-16 were previously amended; no claim was cancelled or added in a reply filed 08/11/2025. Therefore claims 1-20 are currently pending and subject to the non-final office action below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 12/11/2025 was considered by the examiner.
Response to Arguments
Applicant's arguments filed 08/11/2025 in regards to section 101 rejection have been fully considered but they are not persuasive.
Applicant argues that “The independent claims recite specific, concrete technical operations performed by the RTDM module that improve the functioning of the computer system itself and the technological field of distributed data management. In particular, the amended limitations define a technical transformation of heterogeneous data into a standardized and enriched format, a distributed storage architecture using purposive datastores dynamically provisioned within a Global Data Lake for optimized retrieval based on data classification, access frequency, or computational workload, and integration of harmonized data into a federated processing layer that enables parallel query execution across multiple purposive datastores to reduce latency and increase throughput. These operations are not mental processes or generic computer implementation but rather provide a specific improvement to the way distributed computing systems capture, transform, store, and process data, solving problems arising specifically in heterogeneous, distributed data environments.
The claims, therefore, integrate any alleged abstract idea into a practical application under Step 2A, Prong Two of the subject matter eligibility guidance, and, under Step 2B, recite features that are not well-understood, routine, or conventional, as evidenced by the detailed technical disclosure in paragraphs [0108]-[0128]. The combination of schema adaptation, normalization, and enrichment, dynamic provisioning of purposive datastores within a Global
Data Lake, and federated parallel query execution provides an inventive concept that amounts to significantly more than any alleged abstract idea. Therefore, Applicant respectfully requests that the Examiner withdraw the rejection under 35 U.S.C. § 101 and pass all claims to allowance.” (remarks p. 9-10). Examiner respectfully disagrees.
While Applicant describes the claims as improving distributed computing systems, the claims themselves do not recite any specific improvement to computer functionality. Instead, the claims recite results-oriented functional language describing what the system is intended to achieve, rather than how the system technically achieves those results.
For example, the claims recite: “transforming…data into a standardized and enriched format”, “dynamically provisioning…purposive datasores”, “optimizing retrieval based on data classification, access frequency, or computation workload”, and “enables parallel query execution…to reduce latency and increase throughput”
However, the claims do not recite any specific data structures, algorithms, scheduling mechanisms, query execution techniques, synchronization protocols, or resource allocation mechanisms that accomplish these purported improvements. Merely stating desired outcomes such as “optimized retrieval”, “reduced Latency” or “increased throughput” does not amount to a technological improvement under step 2A, prong two.
Furthermore, architectural labels do not convert an abstract idea into a practical application. Applicant relies heavily on labels such as: “real time data mesh”, “single Pane of Glass”, “purposive datastores”, “global data lake” and “federated processing layer”. However, these are high level architectural descriptors, not claimed technical mechanisms. The claims do not specify how the data mesh operates differently from conventional distributed systems, how the “purposive datastores” differ structurally from conventional datastores, howe provisioning is performed in a non-conventional manner, or how federated query execution is implemented beyond generic parallel processing. As a result, these terms function as abstractions or field of use limitations, which do not render a claim eligible.
Also, data transformation alone does not confer eligibility. Applicant asserts that technical transformation of heterogenous data integrates the abstract idea into a practical application. This argument is not persuasive.
The mere collection, organization, normalization, enrichment, and analysis of data, even when performed on large data sets or in distributed computing environment, constitutes abstract information processing when the claims recite these steps at a high level of generality and without specifying a technological improvement to computer functionality itself (please See MPEP 2106.04(a)(2) and 2106.04(b).)
The present claims recite generic data processing operations applied to business and operational data, such as aggregating data from multiple sources, transforming data into standardized formats, analyzing the data, and presenting results. Such operations fall within the category of collecting, analyzing, and displaying information, which is identified as an abstract idea in MPEP 2106.04(a).
Furthermore, the claims do not recite a specific asserted improvement in computer capabilities, such as an improved data structure, memory architecture, processing technique, or network protocol, as required to demonstrate integration of the abstract idea into a practical application under MPEP 2106.04(a)(1). Instead, the claims describe the desired results of data processing (i.e. optimization, reduced latency, improved efficiency) without reciting the particular technical means by which those results are achieved. Accordingly, the claims are directed to an abstract idea and do not integrate the abstract idea into a practical application.
Applicant further argues that the claims recite feature that are not well-understood, routine or conventional, relying on specification 108-128. While the specification may describe technical detail, eligibility is determined based on the claim language, not unclaimed implementation details. The claims here do not recite any specific schema adaptation technique, any particular enrichment algorithm, any non-conventional datastore, provisioning logic or any specific federated query execution strategy.
As such, Applicant’s reliance on specification detail cannot substitute for claim limitations that define a non-conventional technical solution.
Even when considered as an ordered combination, the claims recite collecting and standardizing data, storing data in distributed storage, analyzing the data, presenting results in a user interface. This is a routine enterprise data processing pipeline, implemented using generic computer components performing their expected functions. The mere recitation of dynamic provisioning, federated processing, or machine learning, without a specific technical improvement does not constitute an inventive concept.
Furthermore, in regards to Applicant’s argument that “the claims solve problems arising specifically in heterogenous, distributed data environments”, this statement describes the intended benefit, not a claimed technical solution. The claims do not identify a specific distributed system bottleneck, a particular latency source, a known inefficiency in prior systems, or a technical mechanism that resolves such issues. As such, the claims merely apply abstract data processing concepts to a distributed environment, which is insufficient under 101.
In conclusion, Applicant’s arguments amount to recasting abstract data processing concepts in technical sounding language, without claiming a specific technological improvement to computer operation. Therefore, the claims remain directed to an abstract idea without it being integrated into a practical application.
Applicant’s arguments, see remarks p. 11, filed 08/11/2025, with respect to 112b rejection of claim 16 have been fully considered and are persuasive. The 112b rejection of claim 16 has been withdrawn.
Claim Objections
Claims 1/10/15 are objected to because of the following informalities:
Claims 1/10/15: “the captured data” should read “the aggregated data”
“the harmonized data” should read “the standardized data”.
“…based on the specific roles and responsibilities of each user” should read “…based on specific roles and responsibilities of each user of a plurality of users”
“..that utilize the RTDM analyzed data” should read “…that utilize the analyzed interaction data retired from the RTDM”
“wherein the system employs the one or more artificial intelligence and machine learning algorithms to dynamically refine the user interface” should read “wherein the one or more artificial intelligence and machine learning algorithms dynamically refine the customizable role specific dashboard …”
“…dynamically updating the role specific dashboards using data aggregated and categorized by a plurality of purposive datastores” should read “dynamically updating each of the relevant users’ customizable role specific dashboard using data aggregated and categorized by a plurality of purposive datastores
Claim 7-8: ”the artificial intelligence” should read “ the one or more artificial intelligence”.
Claim 9: “wherein the improvements” should read “wherein the data driven improvements”.
Claim 10: “pefform” should read “perform”
Claims 11: “the artificial intelligence module” should read “the one or more artificial intelligence and machine learning algorithms”.
Claim 12: “wherein the communication integration module is capable” should read “further comprises a communication integration module capable…”
Claim 13: “wherein the data collection module includes…” should read “further comprises a data collection module that includes…”
Claim 14: “wherein the artificial intelligence module includes…” should read “wherein the one or more artificial intelligence and machine learning algorithms include”
Claim 15: “wherein the content is dynamically adjusted” should read “wherein the personalized content is dynamically adjusted…”
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1/10 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “aggregating data from a plurality of diverse communication channels; continuously synchronizing the aggregated data across the diverse communication channels, wherein the synchronizing comprises: transforming the captured data into a standardized format, wherein the transformation comprises applying schema adaptation techniques, data normalization, and enrichment processes to maintain consistency across disparate data sources; allocating the standardized data into [storage ledgers], enabling real-time presentation of coherent and integrated data; presenting, analytic data derived from the aggregated data, wherein the customization of the dashboard is adaptable based on the specific roles and responsibilities of each user; managing a lifecycle of user interactions by continuously collecting, synchronizing, and analyzing interaction data to produce business insights aimed at enhancing operational efficiency; executing one or more machine learning algorithms that utilize the analyzed data to facilitate real-time, data-driven improvements”
The limitations above, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “managing interaction points between a population of users” which is a method of organizing human activity and mathematical concepts. This is because the claims allow for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions); mathematical relationships, mathematical formulas or equations and mathematical calculations.
This judicial exception is not integrated into a practical application. In particular, the claim “aggregating, by a real-time data mesh (RTDM), data from a plurality of diverse communication channels into a Single Pane of Glass (SPoG) user interface (UI) configured as a unified interface in a computer system, wherein the SPoG UI functions as a central integration point for interaction, data, and/or functionalities and wherein the aggregating comprises standardizing the data from multiple distinct formats of the plurality of diverse communication channels”. The aggregating step is recited at a high level of generality (i.e., as a general means of receiving data), and amounts to mere data gathering, which is a form of insignificant extra solution activity.
The claim (1/10) further recites “wherein the system employs the one or more artificial intelligence and machine learning algorithms to dynamically refine the user interface based on real-time user data for directly enhancing market responsiveness within the distribution ecosystem; updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, ensuring immediate access to current data and tools, wherein updating the SPoG UI in real time comprises dynamically updating the role specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role based access updates”. According to the specification, “to refine the user interface based on real time user data, wherein updating the SPoG UI in real time comprises dynamically updating the role specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role based access updates” and “updating a customizable role specific dashboard within the SPoG” simply translates to validating data and updating the data displayed based on the user’s role which amounts to processing and outputting information. Processing and outputting information are generic computer functions and amount to apply it instructions. The claim further recites “a distributed storage framework, including dynamically provisioning a plurality of Purposive Datastores (PDSes) within a Global Data Lake, wherein each PDS is configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload: and integrating, by the RTDM, the harmonized data into a federated data processing layer configured to enable parallel query execution across the plurality of PDSes” which is recited at a high level of generality that amounts to applying the abstract idea on a generic computer architecture. Additionally, claim 10 recites “a processor” which is recited at a high level of generality and amounts to apply it instructions.
Each of the additional limitations is recited at a high level of generality which is no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, alone or in combination, are nothing more than mere instructions to apply the exception on a general computer. In addition, the specification of the application as filed (paragraph 49-63 and 69-78) does not provide any indication that the additional elements described above are anything that generic, off the shelf computer components, and MPEP 2106.05(d)(II) indicate that mere collection or receipt and transmission of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the aggregating of data is well understood, routine and conventional activity is supported under Berkheimer.
Dependent claim 2 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“establishing communication links with multiple pre-existing business platforms”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 3 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“one or more websites, customer relationship management systems, vendor platforms, and supply chain and distribution systems”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 4 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 5 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“collecting data comprises monitoring and/or logging of user activities within the SPoG UI” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 6 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 7 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“artificial intelligence and machine learning algorithms include predictive analytics to identify market trends” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 8 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“the artificial intelligence and machine learning algorithms include recommendation systems to personalize user interactions” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 9 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 1 without successfully integrating the exception into a practical application (“the improvements are based on user feedback received through the SPoG UI” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 11 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the SPoG is a graphical user interface displayed on a computer monitor”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 12 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the communication integration module is capable of establishing communication links with various business platforms”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 13 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the data collection module includes a monitoring and logging system to track user activities” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 14 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 10 without successfully integrating the exception into a practical application (“the artificial intelligence module includes a predictive analytics component and a recommendation system component” is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Claim 15 is rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim recites “aggregating, diverse data sources from multiple communication channels and presenting real-time data and/or analytics on a customizable dashboard wherein the customization of the dashboard is based on the specific roles and/or responsibilities of each user; formats and synchronizes the aggregated data from multiple distinct formats and to maintain real time accuracy and relevance, wherein the synchronizing comprises: transforming the captured data into a standardized format, wherein the transformation comprises applying schema adaptation techniques, data normalization, and enrichment processes to maintain consistency across disparate data sources; allocating the standardized data into [storage ledgers]; collecting data from user interactions, and analyzing the data to generate personalized insights to enhance distribution operations and decision-making processes; and displaying personalized content based on the analyzed data, wherein the content is dynamically adjusted to reflect current data insights and user interaction patterns; wherein the population of users comprises individuals selected from two or more diverse groups comprising distributors, resellers, customers, end-customers, vendors, and suppliers.”
The limitations of generating, as drafted, is a process that, under its broadest reasonable interpretation, covers a method of “managing interaction points between a population of users” which is a method of organizing human activity and mathematical formula. This is because the claims allow for commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) and mathematical formula and relationships.
This judicial exception is not integrated into a practical application. In particular, the claim recites “displaying, on a computer monitor, a Single Pane of Glass (SPoG) user interface (UI) configured as a unified interactive interface wherein the SPoG is a central interface component for consolidating user interactions, data, and/or functionalities [[of]] among a population of users”.
The claim further recites “aggregating, by the SPoG UI diverse data sources from multiple communication channels using a real-time data mesh (RTDM) and presenting real-time data and/or analytics on a customizable dashboard wherein the customization of the dashboard is based on the specific roles and/or responsibilities of each user, wherein the RTDM continuously standardizes the aggregated data from multiple distinct formats and synchronizes the aggregated data to maintain real-time accuracy and relevance”. The aggregating step is recited at a high level of generality (i.e., as a general means of receiving data), and amounts to mere data gathering, which is a form of insignificant extra solution activity.
The claim also recites “updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, wherein updating the SPoG UI in real-time comprises dynamically updating the role-specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role-based access updates”. According to the specification, “updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, wherein updating the SPoG UI in real-time comprises dynamically updating the role-specific dashboards using data aggregated and categorized by a plurality of purposive datastores via the RTDM, and facilitates onboarding processes utilizing automated data validation and role-based access updates” simply translates to validating data, updating data based on roles and updating the data displayed based on the user’s role which amounts to processing and outputting information. The RTDM is recited at a high level of generality and is used for its generic functions. Therefore, processing and outputting information are generic computer functions and amount to apply it instructions.
The claim also recites “providing interactive elements within the SPoG UI that represent the consolidated communication channels, enabling users to interact with the elements to manage end-to-end lifecycle of the distribution ecosystem” and “a distributed storage framework, including dynamically provisioning a plurality of Purposive Datastores (PDSes) within a Global Data Lake, wherein each PDS is configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload: and
integrating, by the RTDM, the harmonized data into a federated data processing layer configured to enable parallel query execution across the plurality of PDSes” which are recited at a high level of generality and amounts to apply it instructions.
Each of the additional limitations is recited at a high level of generality which is no more than mere instructions to apply the exception using a generic computer component. Accordingly, these additional elements, alone or in combination, do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements, alone or in combination, are nothing more than mere instructions to apply the exception on a general computer. In addition, the specification of the application as filed (paragraph 49-63 and 69-78) does not provide any indication that the additional elements described above are anything that generic, off the shelf computer components, and MPEP 2106.05(d)(II) indicate that mere collection or receipt and transmission of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner (as it is here). Accordingly, a conclusion that the aggregating of data is well understood, routine and conventional activity is supported under Berkheimer.
Dependent claim 16 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application (“automatically updating a configuration of the SPoG user interface to enhance user engagement based on the customized insights, wherein updates include modifications to the user interface layout, featured products, and personalized recommendations, wherein the customized insights facilitate real-time decision-making processes within the distribution ecosystem, enhancing operational alignment with market dynamics”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 17 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application (“the user interaction includes actions comprises one or more clicks, hovers, and input data”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
Dependent claim 18 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 19 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application or providing significantly more limitations.
Dependent claim 20 is also directed to an abstract idea without significantly more because it further narrows the abstract idea described in relation to claim 15 without successfully integrating the exception into a practical application (“collecting user feedback through the SPoG UI and updating the SPoG UI based on the user feedback and data analysis results”) is recited at a high level of recitation which amounts to mere instructions to apply the exception in a computer environment) or providing significantly more limitations.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1-10 and 12-14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Byrnes (US 2019/0251457) in view of Hadar (US 2023/0067777).
As per claim 1/10, Byrnes discloses computer-implemented method for dynamically managing interaction points within a distribution ecosystem, the method comprising:
aggregating, by a data storage, data from a plurality of diverse communication channels into a Single Pane of Glass (SPoG) user interface (UI) configured as a unified interface in a computer system, wherein the SPoG UI functions as a central integration point for interaction, data, and/or functionalities and wherein the aggregating comprises standardizing the data from multiple distinct formats of the plurality of diverse communication channels (paragraph 24, 28, 33, 50, 75-76, “In the above-mentioned embodiments, only after data is collected, normalized, and characterized does the data processing platform compute against the data to determine useful information from the data. For example, in these embodiments, fifty thousand (50,000) units of data may be aggregated from various data vendors and entered into the data processing platform for processing into intelligences.”, “[0028] Referring to FIG. 1, a schematic representation of an intelligence system 100 for implementing data processing with a big data platform for identifying business intelligence and stories based on entity data is described. The intelligence system 100 is configured to obtain and consume data associated with an entity, such as a business entity, and generate intelligence information (e.g., business intelligence) in one or more formats including in the format of stories that are published to a story feed of a user interface accessible to the entity. In many embodiments, the entity referred to herein is a business entity; however, it shall be understood that the entity may also refer to any type of operational entity or other type of entity having electronic data associated therewith.”, “[0050] The story generator 161 of a preferred embodiment of the present application is configured to compile intelligence information identified at the intelligence unit 154 and otherwise into one or more stories and/or generate one or more stories. Specifically, the one or more stories compiled or generated by the story generator 161 are based on one or more data points processed at the data processing pipeline of intelligence system 100. The one or more stories preferably include content associated with each of the one or more data points used in generating the one or more stories.”, “[0075] At step S410, data from a number of data sources are aggregated at a data aggregator and collector of a cloud-based big data platform. The data sources of a preferred embodiment include one or more of applications, services, servers, databases, and the like which are operated by or otherwise, subscribed to by the entity.”, “n a preferred embodiment, the data points are joined solely on the basis of time, which thereby simplifies the aggregation process moving forward into other processes of method 400, including normalization and data characterization.”)
continuously synchronizing the aggregated data across the diverse communication channels, enabling real-time presentation of coherent and integrated data within the SPoG UI, wherein the synchronizing comprises (paragraph 34, “[0034] Additionally, and/or alternatively, the schematic and process flow 800 operates automatically to manage constraints on data acquisition imposed by various data vendors (e.g., one or more of the plurality of data sources), and automatically ensures that data is periodically and/or continuously current in the intelligence system 100. For instance, in a preferred embodiment, the task scheduler 808 is able to obtain one or more parameters from each of the various data vendors.”)
transforming, by the RTDM, the captured data into a standardized format, wherein the transformation comprises applying schema adaptation techniques, data normalization, and enrichment processes to maintain consistency across disparate data sources (paragraph 23-24, “In the above-mentioned embodiments, only after data is collected, normalized, and characterized does the data processing platform compute against the data to determine useful information from the data.”;
presenting, through a customizable role-specific dashboard within the SPoG UI, analytic data derived from the synchronized data, wherein the customization of the dashboard is adaptable based on the specific roles and responsibilities of each user, with configurations stored on network-based, non-transitory storage devices (paragraph 28, 113, 115, “As shown in FIG. 10, an example user interface 950 is illustrated which includes a combination of a plurality of stories 1010 and inquiries 1020. Once intelligence information (e.g., insights and the like) are generated and synthesized into stories by the intelligence system 100 and/or system 200, the one or more stories are presented in a feed format to the user via the user interface 950. The feed format enables efficient browsing of the one or more stories, as well as familiar mechanisms for exploring each story further (e.g., selecting a story to expand into the contents therein, etc.), interacting with other users about the one or more stories (e.g., commenting, sharing, and the like), and providing feedback to the system about the story (e.g., answering embedded queries, dismissing a story, liking a story, and the like). The feed to the user is available in a variety of form factors, including, but not limited to, a website, a mobile application, and a browser extension.”);
managing a lifecycle of user interactions via the SPoG UI by continuously collecting, synchronizing, and analyzing interaction data to produce business insights aimed at enhancing operational efficiency (paragraph 32-34, “The schematic and process flow 800 includes the user interface 120, encrypted store 810, data store 820, system acquired 802, credentials 804, collectors 806, and task scheduler 808. The schematic and process flow 800 illustrates an exemplary schematic and process flow for aggregating data by the data aggregation system 140 in conjunction with the use of the user interface 120, which is preferably used to interact with and/or operate the collectors 806 and task scheduler 808. In the schematic and process flow 800, a user uses user interface 120 to authenticate based on interactions with credentials 804 which are stored and/or otherwise, accessible via the encrypted store 810.”);
executing one or more artificial intelligence and machine learning algorithms that utilize the RTDM-analyzed data to facilitate real-time, data-driven improvements within the SPoG UI, wherein the system employs the one or more artificial intelligence and machine learning algorithms to dynamically refine the user interface based on real-time user data for directly enhancing market responsiveness within the distribution ecosystem (paragraph 24, 33-34, 56-57, 60-61, 115, the machine learning unit uses the data gathered and standardized from the multiple sources to generate business intelligence/story feeds to the users); and
updating the SPoG UI in real-time to reflect enhancements in operations, wherein each update is propagated to all relevant users across the distribution ecosystem through the RTDM, ensuring immediate access to current data and tools, wherein updating the SPoG UI in real-time comprises dynamically updating the role-specific dashboards using data aggregated and categorized by datastore, and facilitates onboarding processes utilizing automated data validation and role-based access updates (fig. 10, paragraph 32-34).
However, Byrnes does not disclose but Hadar discloses aggregating, by a real-time data mesh (RTDM), data from a plurality of diverse communication channels (paragraph 8, 39, 60 and 62); allocating, by the RTDM, the standardized data into a distributed storage framework, including dynamically provisioning a plurality of Purposive Datastores (PDSes) within a Global Data Lake, wherein each PDS is configured for optimized retrieval based on at least one of data classification, access frequency, or computational workload (paragraph 37, 42, 49, 59, 62, 66, 70); integrating, by the RTDM, the harmonized data into a federated data processing layer configured to enable parallel query execution across the plurality of PDSes (paragraph 32, 49, 82, 87 and 107).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Hadar in the teaching of Byrnes, in order to to create a supply chain of data and analytics within an enterprise (paragraph 9, Hadar).
As per claim 2/12, Byrnes does not disclose but Hadar discloses wherein the aggregating includes establishing communication links with multiple pre-existing business platforms (paragraph 8, 39-40 and 89)(please see claim 1 rejection for combination rationale).
As per claim 3, Byrnes discloses wherein the plurality of diverse communication channels comprises one or more websites, customer relationship management systems, vendor platforms, and supply chain and distribution management systems (paragraph 33).
As per claim 4, Byrnes discloses wherein the managing the lifecycle comprises one or more of an initial contact, a service fulfillment, and a follow-up interaction (paragraph 33, 56-57) .
As per claim 5, Byrnes discloses wherein the aggregating comprises monitoring and/or logging of user activities within the SPoG UI (paragraph 56-58)
As per claim 6, Byrnes discloses wherein the analyzing the interaction data is performed using advanced statistical algorithms (paragraph 50, 57 and 116).
As per claim 7, Byrnes discloses wherein the artificial intelligence and machine learning algorithms include predictive analytics to identify market trends (paragraph 50, 93, fig. 10).
As per claim 8, Byrnes discloses wherein the artificial intelligence and machine learning algorithms include recommendation systems to personalize user interactions (paragraph 52-53, 113, 115).
As per claim 9, Byrnes discloses wherein the improvements are based on user feedback received through the SPoG UI (paragraph 85, 114-115).
As per claim 13, Byrnes discloses wherein the data collection module includes a monitoring and logging system to track user activities (paragraph 56, 58).
As per claim 14, Byrnes discloses wherein the artificial intelligence module includes a predictive analytics component and a recommendation system component (paragraph 50, 57-59, 61).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Byrnes (US 2019/0251457) in view of Hadar (US 2023/0067777), as disclosed in the rejection of claim 10, in further view of Ohlsson (US 2020/0143313) and Ha (US 2023/0214773).
As per claim 11, Byrnes does not disclose but Hadar discloses an RTDM (paragraph 32). However, Byrnes in view of Hadar does not disclose but Ohlsson discloses accessing historical transaction data (paragraph 16), apply a machine learning model to predict future inventory requirements based on the historical data (paragraph 16).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Ohlsson in the teaching of Byrnes in view of Hadar, in order to apply machine learning to accurately manage and predict inventory variables with future uncertainty (Ohlsson, abstract).
However, Byrnes in view of Hadar and Ohlsson does not disclose but Ha discloses adjust procurement dynamically in response to the predicted inventory requirements to minimize overstock and stockout, wherein the machine learning model is updated periodically based on incoming new transaction data to improve prediction accuracy (paragraph 12, 14, 58).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Ha in the teaching of Byrnes in view of Hadar and Ohlsson, in order to adjust a reorder point (paragraph 3, Ha).
Claim(s) 15-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Byrnes (US 2019/0251457) in view of Hadar (US 2023/0067777) and Jain (US 2003/0144858).
As per claim 15, Byrnes in view of Hadar discloses the limitations similar to claim 1/10. However, Byrnes does not disclose but Jain discloses wherein the population of users comprises individuals selected from two or more diverse groups comprising distributors, resellers, customers, end-customers, vendors, and suppliers such that the SPoG UI addresses the needs and requirements of a multi-layered user base (paragraph 23, 52, 58).
Therefore, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the limitation above as taught by Jain in the teaching of Byrnes, in order to retain and control supply chain information in the form of objects and associated metadata in a database to provide intelligent and controlled access to such objects (abstract, Jain).
As per claim 16, Byrnes discloses wherein the analyzing the data to generate personalized insights comprises: analyzing user interaction data collected from the SPoG UI to identify behavioral patterns and preferences among the population of users; generating customized insights based on the identified user behaviors and preferences to inform adjustments regarding a marketing process within the distribution ecosystem; and automatically updating a configuration of the SPoG user interface to enhance user engagement based on the customized insights, wherein updates include modifications to a user interface layout, featured products, and personalized recommendations, wherein the customized insights facilitate real-time decision-making processes within the distribution ecosystem, enhancing operational alignment with market dynamics (paragraph 56-61)
As per claim 17, Byrne discloses wherein the user interaction patterns comprise actions comprising one or more clicks, hovers, and input data (paragraph 56-58).
As per claim 18, Byrnes discloses wherein the collected data is analyzed using advanced statistical algorithms (paragraph 51, 56-61, 85).
As per claim 19, Byrnes discloses wherein the personalized content is generated based on recommendation algorithms (paragraph 56-61).
As per claim 20, Byrnes discloses collecting user feedback through the SPoG UI and updating the SPoG UI based on the user feedback and data analysis results (paragraph 85, 114-115).
Conclusion
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OMAR . ZEROUAL
Examiner
Art Unit 3628
/OMAR ZEROUAL/Primary Examiner, Art Unit 3628