DETAILED ACTION
Notice to Applicant
The following is a NON-FINAL Office action upon examination of application number 19/239,624, filed on 06/16/2025. Claims 1-20 are pending in the application and have been examined on the merits discussed below.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Priority
Application 19/239,624 filed 06/16/2025 claims Priority from Provisional Application 63/660,425, filed 06/14/2024.
Information Disclosure Statement
4. The information disclosure statement (IDS) filed on 01/19/2026 has been acknowledged. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Objections
5. Claims 13 and 20 are objected to because of the following informalities: grammatical/typographical errors.
Claim 13 recites “wherein the historical data and real-time trends is based on the real- time visibility data.” The phrase “historical data and real-time trends” is plural. Therefore, claim 13 should recite “are based” instead of “is based” Claim 20 recites substantially similar limitations that stand objected via the rationale applied to claim 13, as discussed above. Appropriate correction is required.
Claim Rejections - 35 USC § 112
6. 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.
7. Claims 1-20 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 pre-AIA the applicant regards as the invention.
8. Claim 1 recites “a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs), configured to execute complex algorithms and deep learning models for rapid data processing and optimization of global supply chain operations.” The phrases “high-performance,” “complex algorithms” and “rapid data processing” are ambiguous because “high,” “complex,” and “rapid” are relative terms. There is no basis or standard provided for determining whether the computing module is high performance. Similarly, the claims does not specify any measurable processing speed, latency, throughput, or other performance criterion for determining whether the clamed date processing is “rapid.” Last, there is no basis or standard for determining whether a particular algorithm qualifies as “complex.” Therefore, the claim is rendered indefinite. Independent claims 8 and 15 recite similar limitations as those discussed above and are therefore found to be indefinite for the same reasons as claim 1. Appropriate correction is required.
9. Claim 1 recites “a centralized operator configured to integrate, coordinate, and synchronize multiple supply chain ecosystems,” which renders the claim indefinite because it is unclear what structure is encompassed by the term “centralized operator.” The claim does not specify whether the operator is a human actor, a software component, a server, a controller, or another structure. Independent claims 8 and 15 recite similar limitations as those discussed above and are therefore found to be indefinite for the same reasons as claim 1. Appropriate correction is required.
10. Claims 4, 8, and 11 recite “trigger secure payment transactions.” The phrase “secure payment transactions” is ambiguous because “secure” is a relative term. There is no basis or standard provided for determining whether the payment transactions are secure. Therefore, the claim is rendered indefinite. Appropriate correction is required.
11. All claims dependent from above rejected claims are also rejected due to dependency.
Claim Rejections - 35 USC § 101
12. 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.
13. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more.
14. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The eligibility analysis in support of these findings is provided below, in accordance with MPEP 2106.
With respect to Step 1 of the eligibility inquiry (as explained in MPEP 2106), it is first noted that the claimed system (claims 1-7), method (claims 8-14), and non-transitory computer-readable medium (claims 15-20) are directed to potentially eligible categories of subject matter (i.e., machine and process, respectively), and therefore claims 1-20 satisfy Step 1 of the eligibility inquiry.
With respect to Step 2A Prong One, it is next noted that the claims recite an abstract idea that falls into the “Certain Methods of Organizing Human Activity” abstract idea set forth in MPEP 2106 because the claims recite steps for managing logistics operations, which encompasses activity for managing personal behavior or relationships or interactions and organizing and coordinating commercial operations, and “Mental Processes” or concepts performed in the human mind such as via observation, evaluation, and judgment. With respect to independent claim 1, the limitations reciting the abstract idea are indicated in bold below: a centralized operator configured to integrate, coordinate, and synchronize multiple supply chain ecosystems; a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors configured to capture real-time data on asset parameters; a tokenization module operatively coupled to the interconnected infrastructures and sensors, configured to tokenize the captured real-time data to secure and anonymize sensitive information; an Artificial Intelligence of Things (AIoT) module configured to analyze the tokenized data to generate predictive analytics and to provide real-time visibility into dynamic pricing, booking availability, payment processing, smart contract execution, and traceability of goods; and a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs), configured to execute complex algorithms and deep learning models for rapid data processing and optimization of global supply chain operations. These steps cover organizing human activity because the claim recites limitations related to managing interactions and activities among participants in a supply chain network, and can also be performed mentally via human evaluation/judgment/opinion perhaps with the aid of pen and paper. Independent claims 8 and 15 recite similar limitations as set forth in claim 1 and are therefore found to recite the same abstract idea as claim 1.
Therefore, because the limitations above set forth activities falling within the “Certain methods of organizing human activity” and “Mental Processes” abstract idea groupings described in MPEP 2106, the additional elements recited in the claims are further evaluated, individually and in combination, under Step 2A Prong Two and Step 2B below.
With respect to Step 2A Prong Two, the judicial exception is not integrated into a practical application. The additional elements are: a centralized operator configured to, a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors, a tokenization module operatively coupled to the interconnected infrastructures and sensors, an Artificial Intelligence of Things (AIoT) module configured to, a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs), and deep learning models (claim 1), a logistics network system, a centralized operator, a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors, a tokenization module configured to, an Artificial Intelligence of Things (AIoT) module, deep learning models, and a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs) (claim 8), a non-transitory computer-readable medium storing instructions, one or more processors, a centralized operator, a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors, a tokenization module configured to, an Artificial Intelligence of Things (AIoT) module, deep learning models, and a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs) (claim 15). These additional elements have been evaluated, but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or computer-executable instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment. See MPEP 2106.05(f) and 2106.05(h). Furthermore, these additional elements fail to integrate the abstract idea into a practical application because they fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment.”).
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
With respect to Step 2B of the eligibility inquiry, it has been determined that the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements are: a centralized operator configured to, a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors, a tokenization module operatively coupled to the interconnected infrastructures and sensors, an Artificial Intelligence of Things (AIoT) module configured to, a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs), and deep learning models (claim 1), a logistics network system, a centralized operator, a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors, a tokenization module configured to, an Artificial Intelligence of Things (AIoT) module, deep learning models, and a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs) (claim 8), a non-transitory computer-readable medium storing instructions, one or more processors, a centralized operator, a plurality of interconnected physical and mobile infrastructures, networks, and smart sensors, a tokenization module configured to, an Artificial Intelligence of Things (AIoT) module, deep learning models, and a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs) (claim 15). The additional elements have been fully considered, but fail to add significantly more because they merely serve to tie the invention to a particular operating environment (i.e., computer-based implementation) by describing the use of generic computing elements to implement the claimed invention, though at a very high level of generality and without imposing meaningful limitation on the scope of the claim, similar to simply saying "apply it” or “apply it using a general purpose computer,” which is not enough to transform an abstract idea into eligible subject matter. Notably, Applicant’s Specification describes generic off-the-shelf computing elements for implementing the claimed invention and suggests that virtually any generic computing devices could be used to implement the invention (See, e.g., Specification paragraph [0009]). Therefore, these additional elements describe generic computing elements that merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976.
Furthermore, it is noted that that the claimed use of smart sensors is recited at a high level of generality, these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Yoshida, US 2017/0279457 (paragraph [0006]: “conventional smart sensor”).
Even if the artificial intelligence and deep learning models were evaluated as elements beyond software/code for a generic computer to execute, it is noted that that the claimed use of artificial intelligence and deep learning is recited at a high level of generality these elements amount to well-understood, routine, and conventional activity in the art, which fails to add significantly more to the claims. See, e.g., Magdon-Ismail et al., US 2009/0055270 (paragraph 0039: “Both local and central engines may incorporate analysis techniques, such as artificial intelligence, machine learning and other techniques, which are well known in the art.”). See also, Jo, US 2018/0124319 (paragraph 0095: “Various conventional deep-learning methods may be used as a method for learning the initial background image, using the updated background image. The background image deep-learning of the traffic information providing apparatus may be continuously performed in real time, as long as the traffic information providing apparatus is driven.”).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements integrate the abstract idea into a practical application. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a practical application of the abstract idea or that the ordered combination amounts to significantly more than the abstract idea itself.
Dependent claims 2-7, 9-14, and 16-20 recite the same abstract idea as recited in the independent claims, and when evaluated under Step 2A Prong One of the eligibility inquiry, merely recite further details of the same abstract idea recited in the independent claims accompanied by, at most, the involvement of the same generic computing elements as the independent claims which, as noted above, are not sufficient to amount to a practical application or significantly more than the abstract idea itself. In particular, dependent claims 2-7 and 13 recite “orchestrate and streamline logistics processes across the multiple supply chain ecosystems,” “capturing real-time data on asset parameters comprising geolocation, temperature, humidity, vibrations, shock, and light exposure,” “generate unique tokens for logistics events that trigger secure payment transactions based on predefined criteria in service level agreements,” “implement algorithms that continuously adapt and optimize supply chain operations based on historical data and real-time trends,” “utilize parallel processing capabilities to simultaneously execute multiple algorithms and models,” “provide records of transactions,” “wherein the historical data and real-time trends is based on the real-time visibility data,” however, these claims also set forth steps falling within the same Certain methods of organizing human activity and/or Mental Processes abstract idea groupings recited in the independent claim. The other dependent claims have been fully considered as well; however these claims are also directed to the abstract idea itself without integrating it into a practical application and implemented by, at most, a general purpose computer that serves to tie the idea to a particular technological environment, which does not add significantly more to the claims. The additional elements recited in the dependent claims including the devices (claim 3), machine learning algorithms (claim 5), deep learning models (claim 6), a blockchain component integrated with the smart contract execution (claims 7, 14) are recited at a high level of generality and fails to yield any discernible improvement to the computer or to any technology, nor set forth any additional function or result that provided meaningful limitation beyond linking the abstract idea to a particular technological environment (i.e., automated/computing environment), and thus fail to integrate the abstract idea into a practical application. When evaluated under Step 2A Prong Two and Step 2B, the additional elements do not amount to a practical application or significantly more since they merely require generic computing devices (or computer-implemented instructions/code) which as noted in the discussion of the independent claims above is not enough to render the claims as eligible.
The ordered combination of elements in the dependent claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to a practical application or significantly more than the abstract idea itself.
For more information, see MPEP 2106.
Claim Rejections - 35 USC § 103
15. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
16. 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 of this title, 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.
17. 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.
18. 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.
19. Claims 1-2, 4-9, 11-16, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Sahoo et al., Pub. No.: US 2024/0428167 A1, [hereinafter Sahoo], in view of Cella et al., Pub. No.: US 2026/0154435 A1, [hereinafter Cella].
As per claim 1, Sahoo teaches a logistics network system (paragraph 0002: “a real-time data mesh method and system that encompass distribution, supply chain management, and related functionalities”; paragraph 0038: “an advanced distribution platform including System for managing a complex distribution network, which can be an embodiment of System, and provides a technology distribution platform for optimizing the management and operation of distribution networks”; paragraphs 0025, 0055), comprising:
a centralized operator configured to integrate, coordinate, and synchronize multiple supply chain ecosystems (paragraph 0055, discussing that the operating environment of the system within the IT distribution model encompasses customers, end customers, vendors, resellers, and other entities involved in the distribution process. The system serves as a centralized platform that facilitates efficient collaboration, communication, and transactional processes between these stakeholders. By leveraging real-time data exchange, integration, scalability, and flexibility, the system empowers stakeholders to optimize their operations, enhance customer experiences, and drive business success within the IT distribution ecosystem; paragraph 0051, discussing that vendors play a crucial role within the operating environment of the system. These vendors encompass manufacturers, distributors, and suppliers who offer a diverse range of IT products and services. The system acts as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage the system to streamline their supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with the system, vendors can expand their reach, access new markets, and enhance their overall visibility and competitiveness; paragraph 0052, discussing that resellers play a vital role in the IT distribution ecosystem by connecting customers with the right IT solutions from various vendors…The system enables resellers to access a comprehensive catalog of IT solutions, manage their sales pipeline, and provide value-added services to customers. By leveraging the system, resellers can enhance their customer relationships, optimize their product offerings, and increase their revenue streams; paragraph 0053, discussing that within the operating environment of the system, there are various dynamics and characteristics that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system ensures that relevant data is exchanged in real-time between stakeholders, enabling accurate decision-making and timely actions. Integration with existing enterprise systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems allows for communication and interoperability; paragraph 0076, discussing that the SPoG UI integrates with other modules of the system, facilitating real-time data exchange, synchronized operations, and streamlined workflows; paragraph 0198, discussing that the computing device facilitates collaboration among stakeholders involved in the vendor onboarding process through the Collaborative Decision-Making Module. This module enables real-time communication, document sharing, and workflow coordination; paragraphs 0004, 0016, 0017, 0030, 0076, 0124, 0163, 0216);
a plurality of interconnected physical and mobile infrastructures and networks, and capture real-time data (paragraph 0025, discussing that the platform can be include implementation(s) of a Real-Time Data Mesh (RTDM)…RTDM, a distributed data architecture, enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allowing for efficient management and enabling distributors to handle disruptions more effectively; paragraph 0031, discussing that the real-time tracking and analytics offered by RTDM improve SPoG's ability to manage the supply chain and inventory effectively. It provides accurate and up-to-date information, enabling distributors to make informed decisions quickly; paragraph 0049, discussing that customers can also access real-time data and analytics through the system, empowering them to make informed decisions and optimize their IT infrastructure; paragraph 0058, discussing Customer System Integration: Integration point 210 can enable the system to connect with customer systems, enabling efficient data exchange and synchronization. The customer systems may include various entities…These systems represent the internal systems utilized by customers, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems. Integration with customer systems empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, enhancing their visibility and decision-making capabilities; paragraph 0059, discussing that integration with vendor systems ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details; paragraph 0065, discussing that data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between the different entities, systems, and components. The integrated data is processed, harmonized, and made available in real-time to relevant stakeholders through the system; paragraph 0075, discussing that a logistics manager can use the SPoG UI to monitor the status of shipments, track delivery routes, and view real-time inventory levels across multiple warehouses; paragraph 0113, discussing a state-of-the-art storage and processing infrastructure designed to handle the ever-increasing volume, variety, and velocity of data generated within the supply chain; paragraph 0134, discussing that the SPoG UI can include Mobile and Cross-Platform Accessibility Module to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected and informed while on the go. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, ensuring access to real-time data and functionalities across various devices; paragraph 0165, discussing that the Data Layer can be configured as a suite of interconnected systems and infrastructure designed to manage, process, and analyze supply chain data; paragraphs 0034, 0094, 0103, 0166);
a tokenization module operatively coupled to the interconnected infrastructures and sensors, configured to tokenize the captured real-time data to secure and anonymize sensitive information (paragraph 0053, discussing that within the operating environment of the system, there are various dynamics and characteristics that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system ensures that relevant data is exchanged in real-time between stakeholders, enabling accurate decision-making and timely actions. Integration with existing enterprise systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems allows for communication and interoperability, paragraph 0062, discussing that the integration points within the operating environment are facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and the connected systems; paragraph 0063, discussing that the system incorporates authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of the exchanged information; paragraph 0105, discussing facilitating secure and efficient communication, document sharing, and version control; paragraph 0106, discussing that the incorporation of specific technologies and algorithms enables efficient data management, secure communication, personalized experiences, and effective performance monitoring, contributing to enhanced operational efficiency and success in supply chain and distribution management; paragraph 0118, discussing that the data layer can incorporate robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify the data within the data lake and PDSes (Purposive Datastores). Data encryption techniques, both at rest and in transit, safeguard the sensitive supply chain information against unauthorized access. Additionally, the data layer can implement data lineage and audit trail mechanisms, allowing stakeholders to trace the origin and history of data, ensuring data integrity and compliance with regulatory requirements; paragraph 0131, discussing that to ensure secure and controlled access to functionalities and data, the SPoG UI incorporates the Role-Based Access Control (RBAC) Module. Administrators can define roles, assign permissions, and control user access based on their responsibilities and organizational hierarchy. The RBAC Module ensures that only authorized users can access specific features and information, safeguarding data privacy, security, and compliance within the distribution ecosystem; paragraph 0170, discussing that PDSes also allow for data segmentation based on specific data types, ensuring data privacy and confidentiality. By organizing data into dedicated PDSes, sensitive information can be segregated, and access controls can be applied to protect data privacy and comply with regulatory requirements; paragraphs 0115, 0195);
an Artificial Intelligence of Things (AIoT) module configured to analyze the tokenized data to generate predictive analytics and to provide real-time visibility into dynamic pricing, payment processing, and traceability of goods (abstract, discussing that method involves integrating multiple touchpoints of communication between distributors, resellers, end-users, vendors, and suppliers into a unified interactive interface. This interface enables the management of end-to-end partner lifecycle, systematic data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, consolidation, lifecycle management, data collection, data analysis, and artificial intelligence; paragraph 0016, discussing that through real-time tracking and analytics, SPoG can deliver valuable insights into inventory levels and the status of goods, ensuring that the process of supply chain and distribution management is handled efficiently; paragraph 0026, discussing that RTDM's predictive analytics capability offers a solution for efficient inventory control. By providing insights into demand trends, it aids companies in managing inventory, reducing risks of overstocking or stockouts. paragraph 0031, discussing that the real-time tracking and analytics offered by RTDM improve SPoG's ability to manage the supply chain and inventory effectively. It provides accurate and up-to-date information…; paragraph 0058, discussing that integration with customer systems empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, enhancing their visibility and decision-making capabilities; paragraph 0059, discussing that integration with vendor systems ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details; paragraph 0063, discussing that the system incorporates authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of the exchanged information; paragraph 0065, discussing that the data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between the different entities, systems, and components. The integrated data is processed, harmonized, and made available in real-time to relevant stakeholders through the system; paragraph 0079, discussing that the RTDM module collects data from various systems, including inventory management systems, point-of-sale terminals, and customer relationship management systems. It harmonizes this data by aligning formats, standardizing units of measurement, and reconciling any discrepancies. The harmonized data is then made available in real-time; paragraph 0083, discussing that another component of the system is the Advanced Analytics and Machine Learning (AAML) module…It enables advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities; paragraph 0122, discussing that the data engine layer comprises a set of interconnected systems responsible for data ingestion, processing, transformation, and integration; paragraph 0123, discussing that these systems can be configured to receive data from multiple sources, such as transactional systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and transforming it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration; paragraph 0183, discussing enabling the tracking of subscription-based services, usage/metering information, and billing/payment details; paragraphs 0075, 0084, 0143, 0180); and
a high-performance computing module comprising Central Processing Units (CPUs) and Graphics Processing Units (GPUs), configured to execute complex algorithms and deep learning models for rapid data processing and optimization of global supply chain operations (paragraph 0038, discussing a technology distribution platform for optimizing the management and operation of distribution networks; paragraph 0046, discussing that embodiments may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors; paragraph 0065, discussing that the integration points may also employ algorithms, data analytics, and machine learning techniques to derive valuable insights, optimize distribution processes, and personalize customer experiences. The integration points and data flow within the operating environment enable stakeholders to operate within a connected ecosystem. Data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between the different entities, systems, and components. The integrated data is processed, harmonized, and made available in real-time to relevant stakeholders through the system. This real-time access to accurate and up-to-date information empowers stakeholders to make informed decisions, optimize supply chain operations, and enhance customer experiences; paragraph 0068, discussing that the distribution platform and its components can be operator-configurable using applications that run on central processing units, such as the processor system, which may include Intel Pentium processors or similar processors, and/or multiple processor units; paragraph 0073, discussing that the system can include several interconnected components and modules that work in harmony to optimize supply chain and distribution operations, enhance collaboration, and drive business efficiency; paragraph 0083, discussing that another component of the system is the Advanced Analytics and Machine Learning (AAML) module. Leveraging powerful analytics tools and algorithms, the AAML module extracts valuable insights from the collected data. It enables advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities; paragraph 0084, discussing that the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure stock availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes; paragraph 0101, discussing that the Predictive Analytics Module employs machine learning algorithms and predictive models to forecast demand patterns, optimize inventory levels, and enhance overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and prediction. For example, the module can utilize TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs; paragraph 0106, discussing that the system can incorporate various modules that utilize a diverse range of technologies and algorithms to optimize supply chain and distribution management; paragraph 0117, discussing that in terms of data processing and analytics, the data layer leverages the capabilities of distributed computing frameworks. These frameworks can enable parallel processing and distributed computing across large-scale datasets stored in the data lake and PDSes. By leveraging these frameworks, supply chain stakeholders can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data…; paragraph 0226, discussing that the computer system may include one or more processors (also called central processing units, or CPUs); paragraph 0228, discussing that one or more processors may be a graphics processing unit (GPU). In an embodiment, a GPU may be a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common to computer graphics applications, images, videos, etc.; paragraph 0089, 0095, 0120, 0121, 0135, 0226).
While Sahoo describes real-time data (paragraphs 0049, 0058, 0059, 0075), Sahoo does not explicitly teach smart sensors configured to capture real-time data on asset parameters; a tokenization module operatively coupled to the sensors; and an Artificial Intelligence of Things (AIoT) module configured to provide real-time visibility into booking availability and smart contract execution. However, Cella in the analogous art of supply chain management teaches these concepts. Cella teaches:
smart sensors configured to capture real-time data on asset parameters (paragraph 0016, discussing real-time data reflecting the status, condition, and performance of the physical asset; paragraph 0022, discussing collecting real-time data and analyze the real-time data to provide personalized recommendations for goods or services to users based on their historical transaction data. In embodiments, instructions further cause the system to implement a smart contract orchestration engine to automate transactional workflows within the enterprise ecosystem. In embodiments, instructions further cause the system to operate in conjunction with technologies deployed in private networks of the enterprise, the private networks including at least one of on-premises and cloud resources and platforms. In embodiments, instructions further cause the system to tokenize digital assets to digitally represent transactions within the enterprise ecosystem; paragraph 0794, discussing that Internet has enabled a whole new ecosystem to flourish where Internet of Things (IoT) devices, such as industrial machinery, and infrastructure equipped with smart sensors, actuators, memory modules, and processors, may be capable of exchanging real-time information across systems and networks. The data generated by such IoT devices offers great value. It may assist in assessing consumption behavior and usage patterns and may also serve to inform macro-level tasks like city planning and assessing the quality and demand of water across a region; paragraph 0760, discussing that sequential data inputs and/or outputs can include a wide variety of types, such as strings of text, sequences of sensor data from or about an entity, sequences of steps in a process or flow,…, and many others…In various embodiments described, a set of transformer models may be deployed for a wide range of use cases, including for predictive text applications; for extraction of information (such as extraction of meaningful elements from sensor data, signal data, and the like, such as analog signal data from sensors on machines, wearable devices, infrastructure sensors, edge and IoT devices, and many others; paragraph 0024);
a tokenization module operatively coupled to the sensors (paragraph 0022, discussing collecting real-time data and analyze the real-time data to provide personalized recommendations for goods or services to users based on their historical transaction data. In embodiments, instructions further cause the system to implement a smart contract orchestration engine to automate transactional workflows within the enterprise ecosystem…In embodiments, instructions further cause the system to tokenize digital assets to digitally represent transactions within the enterprise ecosystem; paragraph 0024, discussing that an embedded marketplace employs robotic process automation (RPA) to streamline procurement and sales processes by automating repetitive tasks and data handling. In embodiments, an embedded marketplace includes a digital twin of the enterprise ecosystem to simulate and analyze marketplace dynamics and enterprise resource planning scenarios. In embodiments, an embedded marketplace integrates with a blockchain network to manage and secure transactions, ensuring data integrity and traceability. In embodiments, an embedded marketplace is configured to use artificial intelligence (AI) for dynamic pricing strategies based on real-time market data and predictive analytics. In embodiments, an embedded marketplace incorporates a smart contract orchestration engine to automate agreement execution and compliance with contractual terms. In embodiments, an embedded marketplace is capable of interfacing with Internet of Things (IoT) devices to facilitate transactions based on sensor data and automated triggers…In embodiments, an embedded marketplace is configured to tokenize digital assets, representing ownership and transactions of digital and physical goods within the enterprise ecosystem…In embodiments, an embedded marketplace is further configured to integrate with supply chain management systems to optimize inventory levels and logistics; paragraph 0760, discussing that sequential data inputs and/or outputs can include a wide variety of types, such as strings of text, sequences of sensor data from or about an entity, sequences of steps in a process or flow,…, and many others…In various embodiments described, a set of transformer models may be deployed for a wide range of use cases, including for predictive text applications; for extraction of information (such as extraction of meaningful elements from sensor data, signal data, and the like, such as analog signal data from sensors on machines, wearable devices, infrastructure sensors, edge and IoT devices, and many others); for analysis of human factors; for summarizing data (such as providing summaries of text, images, video, sensor data, and many other streams of data of the type collected and processed as described); for trend detection, prediction and forecasting,…, and many others…); and
an Artificial Intelligence of Things (AIoT) module configured to provide real-time visibility into booking availability and smart contract execution (paragraph 0024, discussing that an embedded marketplace employs robotic process automation (RPA) to streamline procurement and sales processes by automating repetitive tasks and data handling. In embodiments, an embedded marketplace includes a digital twin of the enterprise ecosystem to simulate and analyze marketplace dynamics and enterprise resource planning scenarios. In embodiments, an embedded marketplace integrates with a blockchain network to manage and secure transactions, ensuring data integrity and traceability. In embodiments, an embedded marketplace is configured to use artificial intelligence (AI) for dynamic pricing strategies based on real-time market data and predictive analytics. In embodiments, an embedded marketplace incorporates a smart contract orchestration engine to automate agreement execution and compliance with contractual terms. In embodiments, an embedded marketplace is capable of interfacing with Internet of Things (IoT) devices to facilitate transactions based on sensor data and automated triggers…In embodiments, an embedded marketplace is configured to tokenize digital assets, representing ownership and transactions of digital and physical goods within the enterprise ecosystem…In embodiments, an embedded marketplace is further configured to integrate with supply chain management systems to optimize inventory levels and logistics; paragraph 0805, discussing that the digital twin(s) may be implemented with smart contracts with, such as for digital twin transactions enabled by smart contracts (e.g., using smart contract orchestration engines), leading to the marketplace being a decentralized marketplace; paragraph 0485, discussing that with blockchain wallets configured for blockchain transactions (e.g., cryptocurrency transactions, NFT transactions, smart contract transactions, and/or the like), the set of digital keys functions as secure digital codes needed to interact with a blockchain; paragraph 0824, discussing that IoT and/or digital twins may be equipped with edge artificial intelligence (AI) that may use information to make financial decisions-a sensor in or a digital twin of a warehouse or distribution network managed by an insurer, trader, financial stakeholder may detect environmental anomalies (e.g., humidity, temperature, pressure, etc.) and provide near real-time data upon which the edge AI may analyze risk and predict future impacts to cashflows. This may turn into automatic hedging of positions or execution of transactions depending on the potential loss of product/goods; paragraph 1075, discussing that the embedded marketplace module serves as an interface for users to interact with the system, and is configured to aggregate offerings from multiple vendors, presenting these in a unified and coherent manner within the host application's user interface. In embodiments, the module integrates with various external marketplaces, thereby providing users with a comprehensive view of available goods and services across different platforms; paragraphs 0468, 1049).
Sahoo is directed towards a real-time data mesh method and system that encompass distribution, supply chain management, and related functionalities. Cella is directed to transaction systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for streamlining supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sahoo with Cella because the references are analogous art because they are both directed to solutions for supply chain operations, which falls within applicant’s field of endeavor (supply chain managements systems), and because modifying Sahoo to include Cella’s features for including smart sensors configured to capture real-time data on asset parameters, a tokenization module operatively coupled to the sensors, and an Artificial Intelligence of Things (AIoT) module configured to provide real-time visibility into booking availability and smart contract execution, in the manner claimed, would serve the motivation of enhancing the overall efficacy and strategic agility of the enterprise's operations (Cella at paragraph 0778); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 2, the Sahoo-Cella combination teaches the logistics network system of claim 1. Sahoo further teaches wherein the centralized operator is further configured to orchestrate and streamline logistics processes across the multiple supply chain ecosystems (paragraph 0015, discussing a holistic, user-friendly, and efficient platform that streamlines the distribution process; paragraph 0055, discussing that the operating environment of the system within the IT distribution model encompasses customers, end customers, vendors, resellers, and other entities involved in the distribution process. The system serves as a centralized platform that facilitates efficient collaboration, communication, and transactional processes between these stakeholders. By leveraging real-time data exchange, integration, scalability, and flexibility, the system empowers stakeholders to optimize their operations, enhance customer experiences, and drive business success within the IT distribution ecosystem; paragraph 0051, discussing that vendors play a crucial role within the operating environment of the system. These vendors encompass manufacturers, distributors, and suppliers who offer a diverse range of IT products and services. The system acts as a centralized platform for vendors to showcase their offerings, manage inventory, and facilitate transactions with customers and resellers. Vendors can leverage the system to streamline their supply chain operations, manage pricing and promotions, and gain insights into customer preferences and market trends. By integrating with the system, vendors can expand their reach, access new markets, and enhance their overall visibility and competitiveness; paragraph 0053, discussing that within the operating environment of the system, there are various dynamics and characteristics that contribute to its effectiveness. These dynamics include real-time data exchange, integration with existing enterprise systems, scalability, and flexibility. The system ensures that relevant data is exchanged in real-time between stakeholders, enabling accurate decision-making and timely actions. Integration with existing enterprise systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems allows for communication and interoperability; paragraph 0076, discussing that the SPoG UI integrates with other modules of the system, facilitating real-time data exchange, synchronized operations, and streamlined workflows; paragraph 0198, discussing that the computing device facilitates collaboration among stakeholders involved in the vendor onboarding process through the Collaborative Decision-Making Module. This module enables real-time communication, document sharing, and workflow coordination; paragraphs 0024, 0106).
As per claim 4, the Sahoo-Cella combination teaches the logistics network system of claim 1. Sahoo further teaches wherein the tokenization module is further configured to generate unique tokens for logistics events (paragraph 0062, discussing that the integration points within the operating environment are facilitated through standardized protocols, APIs, and data connectors. These mechanisms ensure compatibility, interoperability, and secure data transfer between the distribution platform and the connected systems; paragraph 0063, discussing that the system incorporates authentication and authorization mechanisms to ensure secure access and data protection. Technologies such as OAuth or JSON Web Tokens (JWT) can be employed to authenticate users, authorize data access, and maintain the integrity and confidentiality of the exchanged information; paragraph 0105, discussing facilitating secure and efficient communication, document sharing, and version control; paragraph 0118, discussing that the data layer can incorporate robust data governance and security measures. Fine-grained access control mechanisms and authentication protocols ensure that only authorized users can access and modify the data within the data lake and PDSes (Purposive Datastores). Data encryption techniques, both at rest and in transit, safeguard the sensitive supply chain information against unauthorized access. Additionally, the data layer can implement data lineage and audit trail mechanisms, allowing stakeholders to trace the origin and history of data, ensuring data integrity and compliance with regulatory requirements; paragraphs 0131, 0170), but it does not explicitly teach generate unique tokens for logistics events that trigger secure payment transactions based on predefined criteria in service level agreements. However, Cella in the analogous art of supply chain management teaches this concept. Cella teaches:
generate unique tokens for logistics events that trigger secure payment transactions based on predefined criteria in service level agreements (paragraph 0022, discussing collecting real-time data and analyze the real-time data to provide personalized recommendations for goods or services to users based on their historical transaction data. In embodiments, instructions further cause the system to implement a smart contract orchestration engine to automate transactional workflows within the enterprise ecosystem…In embodiments, instructions further cause the system to tokenize digital assets to digitally represent transactions within the enterprise ecosystem; paragraph 0024, discussing that an embedded marketplace integrates with a blockchain network to manage and secure transactions, ensuring data integrity and traceability… In embodiments, an embedded marketplace incorporates a smart contract orchestration engine to automate agreement execution and compliance with contractual terms. In embodiments, an embedded marketplace is capable of interfacing with Internet of Things (IoT) devices to facilitate transactions based on sensor data and automated triggers…In embodiments, an embedded marketplace is configured to tokenize digital assets, representing ownership and transactions of digital and physical goods within the enterprise ecosystem…; paragraph 0475, discussing that the transaction system supports and executes digital transactions on behalf of the enterprise and/or entities thereof. Within the context of the transaction systems, the types of digital transactions that may be executed, or otherwise supported by the transaction system include out-bound payments, invoices/payment requests, blockchain transactions (e.g., transfers of cryptocurrency and other blockchain tokens on a blockchain, tokenization of data on a blockchain, and/or any other blockchain action that requires a digital signature). In embodiments, the transaction system may be configured to control one or more digital wallets of an enterprise. In embodiments, the term “digital wallet” may refer to a software program that executes one or more respective types of transactions using respective credentials, keys, and/or other transaction parameters corresponding to a respective account of the enterprise; paragraph 0485, discussing that with blockchain wallets configured for blockchain transactions (e.g., cryptocurrency transactions, NFT transactions, smart contract transactions, and/or the like), the set of digital keys functions as secure digital codes needed to interact with a blockchain; paragraph 0854, discussing generating a verifiable action token for the transactions; paragraphs 0869, 0940).
Sahoo is directed towards a real-time data mesh method and system that encompass distribution, supply chain management, and related functionalities. Cella is directed to transaction systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for streamlining supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sahoo with Cella because the references are analogous art because they are both directed to solutions for supply chain operations, which falls within applicant’s field of endeavor (supply chain managements systems), and because modifying Sahoo to include Cella’s feature for including generating unique tokens for logistics events that trigger secure payment transactions based on predefined criteria in service level agreements, in the manner claimed, would serve the motivation of enhancing the overall efficacy and strategic agility of the enterprise's operations (Cella at paragraph 0778); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
As per claim 5, the Sahoo-Cella combination teaches the logistics network system of claim 1. Sahoo further teaches wherein the AIoT module is further configured to implement machine learning algorithms that continuously adapt and optimize supply chain operations based on historical data and real-time trends (paragraph 0018, discussing that SPoG offers an innovative solution for improved inventory management through advanced forecasting capabilities. These predictive analytics can highlight demand trends, guiding companies in managing their inventory more effectively and mitigating the risks of stockouts or overstocks; paragraph 0022, discussing that SPoG's advanced analytics capabilities offer invaluable insights that can drive strategy and decision-making. It can track and analyze trends in real-time, allowing companies to stay ahead of the curve and adapt to changing market conditions; paragraph 0038, discussing a technology distribution platform for optimizing the management and operation of distribution networks; paragraph 0083, discussing that another component of the system is the Advanced Analytics and Machine Learning (AAML) module. Leveraging powerful analytics tools and algorithms, the AAML module extracts valuable insights from the collected data. It enables advanced analytics, predictive modeling, anomaly detection, and other machine learning capabilities; paragraph 0084, discussing that the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure stock availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes; paragraph 0086, discussing that the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of consumer sentiment and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations; paragraph 0095, discussing that the AI module within the system leverages advanced analytics and machine learning algorithms to extract valuable insights from data. These algorithms enable the module to automate repetitive tasks, predict demand patterns, optimize inventory levels, and improve overall supply chain efficiency. For example, the AI module can utilize predictive models to forecast demand, allowing stakeholders to optimize inventory management and minimize stockouts or overstock situations; paragraph 0101, discussing that the Predictive Analytics Module employs machine learning algorithms and predictive models to forecast demand patterns, optimize inventory levels, and enhance overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and prediction. For example, the module can utilize TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs; paragraph 0121, discussing that the AI module can apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI module continuously learns from new data inputs and adapts its models to provide accurate and up-to-date insights. The AI module can generate predictions, recommendations, and alerts and publish such insights to dedicated data feeds; paragraph 0123, discussing that these systems can be configured to receive data from multiple sources, such as transactional systems, IoT devices, and external data providers. The data ingestion process involves extracting data from these sources and transforming it into a standardized format. Data processing algorithms are applied to cleanse, aggregate, and enrich the data, making it ready for further analysis and integration; paragraphs 0094, 0109, 0129).
As per claim 6, the Sahoo-Cella combination teaches the logistics network system of claim 1. Sahoo further teaches wherein the high-performance computing module is further configured to utilize parallel processing capabilities to simultaneously execute multiple algorithms and deep learning models (paragraph 0101, discussing that the Predictive Analytics Module employs machine learning algorithms and predictive models to forecast demand patterns, optimize inventory levels, and enhance overall supply chain efficiency. It utilizes technologies such as Apache Spark and TensorFlow for data analysis, modeling, and prediction. For example, the module can utilize TensorFlow's deep learning capabilities to analyze historical sales data and predict future demand, allowing stakeholders to optimize inventory levels and minimize costs; paragraph 0117, discussing that in terms of data processing and analytics, the data layer leverages the capabilities of distributed computing frameworks...These frameworks can enable parallel processing and distributed computing across large-scale datasets stored in the data lake and PDSes. By leveraging these frameworks, supply chain stakeholders can perform complex analytical tasks, apply machine learning algorithms, and derive valuable insights from the data. For instance, the data layer can leverage Apache Spark's machine learning libraries to develop predictive models for demand forecasting, optimize inventory levels, and identify potential supply chain risks; paragraph 0228, discussing that one or more processors may be a graphics processing unit (GPU). In an embodiment, a GPU may be a processor that is a specialized electronic circuit designed to process mathematically intensive applications. The GPU may have a parallel structure that is efficient for parallel processing of large blocks of data, such as mathematically intensive data common to computer graphics applications, images, videos, etc.).
As per claim 7, the Sahoo-Cella combination teaches the logistics network system of claim 1. Although not explicitly taught by Sahoo, Cella in the analogous art of supply chain management teaches further comprising a blockchain component integrated with the smart contract execution, the blockchain component configured to provide records of transactions (paragraph 0019, discussing a blockchain interface configured to interact with one or more distributed ledgers for recording transactions executed within the embedded marketplace; and a smart contract module configured to generate and enforce agreements related to transactions within the embedded marketplace based on predefined rules and conditions; paragraph 0024, discussing an embedded marketplace that integrates with a blockchain network to manage and secure transactions, ensuring data integrity and traceability…In embodiments, an embedded marketplace incorporates a smart contract orchestration engine to automate agreement execution and compliance with contractual terms…In embodiments, an embedded marketplace is configured to tokenize digital assets, representing ownership and transactions of digital and physical goods within the enterprise ecosystem…In embodiments, an embedded marketplace is further configured to integrate with supply chain management systems to optimize inventory levels and logistics; paragraph 0485, discussing that with blockchain wallets configured for blockchain transactions (e.g., cryptocurrency transactions, NFT transactions, smart contract transactions, and/or the like), the set of digital keys functions as secure digital codes needed to interact with a blockchain; paragraph 0618, discussing generating a record of the transaction and storing the record on one or more blockchains; paragraph 0483).
As per claim 13, the Sahoo-Cella combination teaches the method of claim 12. Sahoo further teaches wherein the historical data and real-time trends is based on the real-time visibility data (paragraph 0022, discussing that SPoG's advanced analytics capabilities offer invaluable insights that can drive strategy and decision-making. It can track and analyze trends in real-time, allowing companies to stay ahead of the curve and adapt to changing market conditions; paragraph 0025, discussing that the platform can be include implementation(s) of a Real-Time Data Mesh (RTDM)…RTDM, a distributed data architecture, enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allowing for efficient management and enabling distributors to handle disruptions more effectively; paragraph 0053, discussing that the system ensures that relevant data is exchanged in real-time between stakeholders, enabling accurate decision-making and timely actions. Integration with existing enterprise systems such as enterprise resource planning (ERP) systems, customer relationship management (CRM) systems, and warehouse management systems allows for communication and interoperability, enabling end-to-end visibility; paragraph 0084, discussing that the AAML module can analyze historical sales data to identify seasonal patterns and predict future demand. It can generate forecasts that help optimize inventory levels, ensure stock availability during peak seasons, and minimize excess inventory costs. By leveraging machine learning algorithms, the AAML module automates repetitive tasks, predicts customer preferences, and optimizes supply chain processes; paragraph 0086, discussing that the AAML module can analyze data from various sources, such as social media feeds, customer reviews, and market trends, to gain a deeper understanding of consumer sentiment and preferences. This information can be used to inform product development decisions, identify emerging market trends, and adapt business strategies to meet evolving consumer expectations; paragraph 0121, discussing that the AI module can apply predictive analytics, anomaly detection, and optimization algorithms to identify patterns, trends, and potential risks within the supply chain. The AI module continuously learns from new data inputs and adapts its models to provide accurate and up-to-date insights. The AI module can generate predictions, recommendations, and alerts and publish such insights to dedicated data feeds; paragraph 0158, discussing that the supply-demand engine focuses on managing the supply chain dynamics, including order fulfillment, inventory visibility, and supply chain optimization. It provides real-time insights into supply and demand patterns, allowing stakeholders to make informed decisions and optimize their operations. The supply-demand engine acts as a central hub for supply chain visibility, ensuring timely order processing and efficient inventory management; paragraph 0178, discussing providing real-time visibility into the progress of orders, including order placement, fulfillment, and delivery...; paragraphs 0016, 0058, 0101, 0106).
Claims 8 and 15 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 1, as discussed above. Further, as per claim 8 the Sahoo-Cella combination teaches a method for operating a logistics network system (Sahoo, paragraph 0002, discussing a real-time data mesh method and system that encompass distribution, supply chain management, and related functionalities; paragraphs 0014, 0047, 0069). As per claim 15, the Sahoo-Cella combination teaches a non-transitory computer-readable medium storing instructions that, when executed by one or more processors, cause the one or more processors to perform operations (Sahoo, paragraph 0046, discussing that embodiments may be implemented in hardware, firmware, software, or any combination thereof. Embodiments may also be implemented as instructions stored on a machine-readable medium, which may be read and executed by one or more processors. A machine-readable medium may include any mechanism for storing or transmitting information in a form readable by a machine (e.g., a computing device). For example, a machine-readable medium may include read only memory (ROM); random access memory (RAM); magnetic disk storage media; optical storage media; flash memory devices, and others. Further, firmware, software, routines, instructions may be described herein as performing certain actions. However, it should be appreciated that such descriptions are merely for convenience and that such actions in fact result from computing devices, processors, controllers, or other devices executing the firmware, software, routines, instructions, etc.; paragraph 0069, discussing that computer program product embodiments include machine-readable storage media containing instructions to program computers to perform the processes described).
Claims 9 an 16 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 2, as discussed above.
Claims 11 and 18 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 4, as discussed above.
Claims 12 and 19 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 5, as discussed above.
Claim 20 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 13, as discussed above.
Claim 14 recites substantially similar limitations that stand rejected via the art citations and rationale applied to claim 7, as discussed above.
20. Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Sahoo in view of Cella, in further view of Nattar Ranganathan et al., Pub. No.: US 2025/0310852 A1, [hereinafter Nattar].
As per claim 3, the Sahoo-Cella combination teaches the logistics network system of claim 1. Sahoo further teaches wherein the plurality of interconnected physical and mobile infrastructures and networks includes devices for capturing real-time data (paragraph 0025, discussing that the platform can be include implementation(s) of a Real-Time Data Mesh (RTDM)…RTDM, a distributed data architecture, enables real-time data availability across multiple sources and touchpoints. This feature enhances supply chain visibility, allowing for efficient management and enabling distributors to handle disruptions more effectively; paragraph 0031, discussing that the real-time tracking and analytics offered by RTDM improve SPoG's ability to manage the supply chain and inventory effectively. It provides accurate and up-to-date information, enabling distributors to make informed decisions quickly; paragraph 0058, discussing Customer System Integration: Integration point 210 can enable the system to connect with customer systems, enabling efficient data exchange and synchronization. The customer systems may include various entities…These systems represent the internal systems utilized by customers, such as enterprise resource planning (ERP) or customer relationship management (CRM) systems. Integration with customer systems empowers customers to access real-time inventory information, pricing details, order tracking, and other relevant data, enhancing their visibility and decision-making capabilities; paragraph 0059, discussing that integration with vendor systems ensures that vendors can efficiently update their product offerings, manage pricing and promotions, and receive real-time order notifications and fulfillment details; paragraph 0065, discussing that data generated at various touchpoints, including customer orders, inventory updates, pricing changes, or delivery status, flows between the different entities, systems, and components. The integrated data is processed, harmonized, and made available in real-time to relevant stakeholders through the system; paragraph 0075, discussing that a logistics manager can use the SPoG UI to monitor the status of shipments, track delivery routes, and view real-time inventory levels across multiple warehouses; paragraph 0134, discussing that the SPoG UI can include Mobile and Cross-Platform Accessibility Module to ensure accessibility across multiple devices and platforms. Stakeholders can access the interface from desktop computers, laptops, smartphones, and tablets, allowing them to stay connected and informed while on the go. This module optimizes the user experience for different screen sizes, resolutions, and operating systems, ensuring access to real-time data and functionalities across various devices; paragraph 0165).
Sahoo does not explicitly teach wherein the plurality of interconnected physical and mobile infrastructures, networks, and smart sensors includes devices for capturing real-time data on asset parameters comprising geolocation, temperature, humidity, vibrations, shock, and light exposure. Cella in the analogous art of supply chain management teaches:
wherein the plurality of interconnected physical and mobile infrastructures, networks, and smart sensors includes devices for capturing real-time data on asset parameters comprising geolocation, temperature, and humidity (paragraph 0016, discussing real-time data reflecting the status, condition, and performance of the physical asset; paragraph 0022, discussing collecting real-time data and analyze the real-time data; paragraph 0024, discussing that in embodiments, an embedded marketplace is capable of interfacing with Internet of Things (IoT) devices to facilitate transactions based on sensor data and automated triggers…In embodiments, an embedded marketplace is further configured to integrate with supply chain management systems to optimize inventory levels and logistics; paragraph 0208, discussing inputs received from one or more sensors including vision sensors, location-based data (e.g., GPS data); paragraph 0794, discussing that Internet has enabled a whole new ecosystem to flourish where Internet of Things (IoT) devices, such as industrial machinery, and infrastructure equipped with smart sensors, actuators, memory modules, and processors, may be capable of exchanging real-time information across systems and networks. The data generated by such IoT devices offers great value; paragraph 0760, discussing that sequential data inputs and/or outputs can include a wide variety of types, such as strings of text, sequences of sensor data from or about an entity, sequences of steps in a process or flow,…, and many others…In various embodiments described, a set of transformer models may be deployed for a wide range of use cases, including for predictive text applications; for extraction of information (such as extraction of meaningful elements from sensor data, signal data, and the like, such as analog signal data from sensors on machines, wearable devices, infrastructure sensors, edge and IoT devices, and many others; paragraph 0824, discussing that IoT and/or digital twins may be equipped with edge artificial intelligence (AI) that may use information to make financial decisions-a sensor in or a digital twin of a warehouse or distribution network managed by an insurer, trader, financial stakeholder may detect environmental anomalies (e.g., humidity, temperature, pressure, etc.) and provide near real-time data upon which the edge AI may analyze risk and predict future impacts to cashflows. This may turn into automatic hedging of positions or execution of transactions depending on the potential loss of product/goods; paragraph 0585).
Sahoo is directed towards a real-time data mesh method and system that encompass distribution, supply chain management, and related functionalities. Cella is directed to transaction systems. Therefore, they are deemed to be analogous as they both are directed towards solutions for streamlining supply chain operations. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Sahoo with Cella because the references are analogous art because they are both directed to solutions for supply chain operations, which falls within applicant’s field of endeavor (supply chain managements systems), and because modifying Sahoo to include Cella’s feature for including wherein the plurality of interconnected physical and mobile infrastructures, networks, and smart sensors includes devices for capturing real-time data on asset parameters comprising geolocation, temperature, and humidity, in the manner claimed, would serve the motivation of enhancing the overall efficacy and strategic agility of the enterprise's operations (Cella at paragraph 0778); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
The Sahoo-Cella combination does not explicitly teach capturing real-time data on asset parameters comprising vibrations, shock, and light exposure. However, Nattar in the analogous art of supply chain management teaches this concept. Nattar teaches:
capturing real-time data on asset parameters comprising vibrations, shock, and light exposure (paragraph 0016, discussing that different use cases often include differing network connectivity demands and restrictions, such as data rates, cost-efficient connectivity, resource-limited mobile devices, and communication latency. For example, with supply chain use cases, pervasive connectivity enables timely monitoring of goods to provide precise information that track location, handling and the necessary information to meet certification requirements securely. This can also help avoid breaks in the supply chain caused by unforeseen changes to network availability; paragraph 0043, discussing that the mobile device also receives cargo data associated with cargo that is transported by the vehicle. For example, the vehicle includes one or more sensors configured to collect data about the vehicle and/or the cargo being transported by the vehicle, such as environmental data (e.g., temperature, humidity, pressure, light exposure, or the like), handling data (e.g., shock, vibration, tilt, orientation), container integrity, and vehicle performance data. Such data may be transmitted to the transportation manager via the wireless network(s) and the connectivity to those networks as managed by the mobile device. Such network management offered by the mobile device provides greater reliability of connectivity between the mobile device and the transportation manager, and thus more regular and reliable access to vehicle and cargo data (e.g., for real-time monitoring)).
The Sahoo-Cella combination describes features related to supply chain management. Nattar is directed to a global network management system. Therefore, they are deemed to be analogous as they both are directed towards solutions for managing supply chain operations. 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 Sahoo-Cella combination with Nattar because the references are analogous art because they are both directed to solutions for supply chain operations, which falls within applicant’s field of endeavor (supply chain managements systems), and because modifying the Sahoo-Cella combination to include Nattar’s feature for including capturing real-time data on asset parameters comprising vibrations, shock, and light exposure, in the manner claimed, would serve the motivation of enabling timely monitoring of goods to provide precise information that track location, handling and the necessary information to meet certification requirements securely (Nattar at paragraph 0016); and further obvious because the claimed invention is merely a combination of old elements, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that the results of the combination were predictable.
Claims 10 and 17 recite substantially similar limitations that stand rejected via the art citations and rationale applied to claim 3, as discussed above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Al-Temyatt, Pub. No.: US 2025/0086541 A1 – describes a supply chain digital twin system.
Sahoo et al., Pub. No.: US 2022/0147924 A1 – describes facilitating financing in supply chain management using blockchain
Patterson et al., Pub. No.: US 2020/0005404 A1 – describes a distributed ledger that enables application of smart contracts to smart sensors.
Makhija et al., Pub. No.: US 2020/0279200 A1 – describes a self-driven AI based system and method for operating one or more applications including enterprise application and supply chain management applications.
Bucki, Robert, and Petr Suchánek. "The Method of Logistic Optimization in E-commerce." J. Univers. Comput. Sci. 18.10 (2012): 1238-1258 – describes that the optimal way to ensure the success of logistics and supply chains is to use the methods of modeling and simulation based on appropriate models and especially its mathematical representation.
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/Darlene Garcia-Guerra/
Primary Examiner, Art Unit 3625
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