DETAILED ACTION
This Office Action is in response to Applicant's response to application and preliminary amendment filed on 10 June 2024. Currently, claims 1-8 are pending. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
The claims are eligible subject matter because the abstract idea is integrated into practical application because the abstract idea is necessarily rooted in technology and because the ordered combination of the additional elements is significantly more than the judicial exception
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.
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.
Claims 1-8 are rejected under 35 U.S.C. 103 as being unpatentable over Sharma et al. (US 2020/0142365 A1) (hereinafter Sharma) in view of Cella et al. (US 2022/0245574 A1) (hereinafter Cella).
Claim 1:
Sharma, as shown, discloses the following limitations of claim 1
A digital device health platform for optimizing an offering of a product, comprising: a plurality of supply chains, each of which contributes a different aspect of the product (see para [0115], "The system 500 is shown to include, a security, privacy, access control, and compliance manager (SPACCM) 548. Security can be provided at both the device (e.g., the smart connected things 514) and network levels (e.g., the cloud platform 502). The same intelligence that enables devices to perform their tasks can also enable them to recognize and counteract threats. At any given point in time, the IoT platform 508 can support multiple users and stake holders including building owners, building operators, tenants, service technicians, manufacturers, and the like. The cloud platform 502 can allow remote accessibility of the smart connected things 514 (e.g., via the Internet). For this reason, remote accessibility of connected products can involve complex identity management through a unified login and product management experience that can be facilitated by the SPACCM 548. For example, a single login can allow a customer to sign on to all authorized connected products and services associated with the IoT platform 508.");
a plurality of input modules comprising: at least one third party data input for receiving third party data (see para [0092], "Building subsystem integration layer 420 can be configured to manage communications between BAS controller 366 and building subsystems 428. For example, building subsystem integration layer 420 can receive sensor data and input signals from building subsystems 428 and provide output data and control signals to building subsystems 428. Building subsystem integration layer 420 can also be configured to manage communications between building subsystems 428. Building subsystem integration layer 420 translate communications (e.g., sensor data, input signals, output signals, etc.) across a plurality of multi-vendor/multi-protocol systems.");
a supply chain management input for receiving supply chain data from each of the plurality of supply chains (see para [0092], showing communication across multi-vendor systems);
an application data input for receiving data regarding operation of at least one application operating in conjunction with hardware of the product to provide a purpose of the product (see para [0093], "Demand response layer 414 can be configured to optimize resource usage (e.g., electricity use, natural gas use, water use, etc.) and/or the monetary cost of such resource usage in response to satisfy the demand of building 10. The optimization can be based on time-of-use prices, curtailment signals, energy availability, or other data received from utility providers, distributed energy generation systems 424, from energy storage 427 (e.g., hot TES 242, cold TES 244, etc.), or from other sources. Demand response layer 414 can receive inputs from other layers of BAS controller 366 (e.g., building subsystem integration layer 420, integrated control layer 418, etc.). The inputs received from other layers can include environmental or sensor inputs such as temperature, carbon dioxide levels, relative humidity levels, air quality sensor outputs, occupancy sensor outputs, room schedules, and the like. The inputs can also include inputs such as electrical use (e.g., expressed in kWh), thermal load measurements, pricing information, projected pricing, smoothed pricing, curtailment signals from utilities, and the like.");
a device hardware data input for receiving data regarding operation of the hardware (see para [0085], " Still referring to FIG. 4, BAS controller 366 is shown to include a communications interface 407 and a BAS interface 409. Interface 407 can facilitate communications between BAS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444, applications residing on client devices 448, etc.) for allowing user control, monitoring, and adjustment to BAS controller 366 and/or subsystems 428. Interface 407 can also facilitate communications between BAS controller 366 and client devices 448. BAS interface 409 can facilitate communications between BAS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.)." and see para [0109], " The IP devices 522 can be any device in building 10 that is connected to the cloud platform 502 via an Internet based network. In this regard, the IP devices 522 can include the hardware and/or software necessary to communicate via an Internet based network. The IP devices 522 can be configured to communicate directly to the cloud platform 502 rather than communicating to the cloud platform 502 via the gateway 524. The IP devices 522 are shown to include IP sensors 526, IP actuators 528, and IP controllers 530. The IP sensors 526 can be similar to the sensors 526. However, the IP sensors 526 can communicate directly with the cloud platform 502 (e.g., the IP sensors 526 can communicate via an Internet based network). Similarly, the IP actuators 528 can be similar to the actuators 518 and the IP controllers 530 can be similar to the controllers 520. Unlike the actuators 518 and the controller 520, the IP actuators 528 and the IP controllers 530 can be configured to communicate directly to the cloud platform 502 via an Internet based network, instead of communicating to the cloud platform 502 via the gateway 524.");
a software application input for receiving purpose data from the at least one application (see para [0085], " Still referring to FIG. 4, BAS controller 366 is shown to include a communications interface 407 and a BAS interface 409. Interface 407 can facilitate communications between BAS controller 366 and external applications (e.g., monitoring and reporting applications 422, enterprise control applications 426, remote systems and applications 444, applications residing on client devices 448, etc.) for allowing user control, monitoring, and adjustment to BAS controller 366 and/or subsystems 428. Interface 407 can also facilitate communications between BAS controller 366 and client devices 448. BAS interface 409 can facilitate communications between BAS controller 366 and building subsystems 428 (e.g., HVAC, lighting security, lifts, power distribution, business, etc.)." and see para [0109], " The IP devices 522 can be any device in building 10 that is connected to the cloud platform 502 via an Internet based network. In this regard, the IP devices 522 can include the hardware and/or software necessary to communicate via an Internet based network. The IP devices 522 can be configured to communicate directly to the cloud platform 502 rather than communicating to the cloud platform 502 via the gateway 524. The IP devices 522 are shown to include IP sensors 526, IP actuators 528, and IP controllers 530. The IP sensors 526 can be similar to the sensors 526. However, the IP sensors 526 can communicate directly with the cloud platform 502 (e.g., the IP sensors 526 can communicate via an Internet based network). Similarly, the IP actuators 528 can be similar to the actuators 518 and the IP controllers 530 can be similar to the controllers 520. Unlike the actuators 518 and the controller 520, the IP actuators 528 and the IP controllers 530 can be configured to communicate directly to the cloud platform 502 via an Internet based network, instead of communicating to the cloud platform 502 via the gateway 524."); and
a firmware input for receiving data regarding operation of firmware drivers for the hardware (see para [0161], " The cloud platform 502 is shown to include the block chain 1156. In some embodiments, the block chain 1156 is a Block chain As A Service (BAAS), for example, such as MICROSOFT AZURE Ethereum Block chain, DELOITTE Rubix block chain, IBM Bluemix block chain, and/or any other suitable BAAS. The block chain 1156 is configured to secure data using block chain technology. For example, controller parameters, firmware, equipment settings, and/or the like can be stored in the block chain 1156 so that the data can be verified. Furthermore, the real-time data 1152 and/or the historical data can be transmitted between the devices of the building 10 and the cloud platform 502 via block chain 1156. For example, the block chain manager 1124 can store the real-time data 1152 or the historical data of the controller 1110 and/or 1112 in the block chain 1156 from which the cloud platform 502 (e.g., the block chain manager 1154) can retrieve the data.");
a digital device health engine that receives processed data from each of the input modules, and that processes the received processed data, via at least one computer processor processing non-transitory computing code, to generate a holistic digital twin of all aspects of the product corresponding to the plurality of the inputs, which includes the supply chain data from each of the plurality of supply chains (see para [0014], "Another implementation of the present disclosure is a method. The method includes receiving, from a physical building device of a building, environmental inputs and environmental outputs of the physical building device; generating a building device digital twin for the physical building device based on the received environmental inputs and the received environmental outputs; generating a predicted future performance of the physical building device based on the building device digital twin; and generating a recommendation based on the predicted future performance of the physical building device, the recommendation indicating one or more changes to implement on the physical building device. The building device digital twin is a model for predicting the behavior of the physical building device." and see para [0149], "The cloud platform 502 is shown to include the notifications 1030, the visualizations 1034, and the dashboards 1038. The notifications 1030, the visualizations 1034, and/or the dashboards 1038 may be generated by the cloud platform 502 based on the hybrid models 1036 (e.g., a digital twin) or based on data received from the sensors 1010 and/or the actuators 1012. The notifications 1030, visualizations 1034, and/or the dashboards 1038 can be generated with and/or based on the data of the data lake 1018."), wherein the corresponding comprises at least a correlating to an optimized level of performance of the purpose, a comparing to a plurality of rules governing the performance of the purpose, and feedback from machine learning (see para [0147]-[0148], "The service bus 1024 can be configured to facilitate communications between multiple computing devices of the cloud platform 502 and/or the building 10 (e.g., the sensors 1010 and/or the actuators 1012). The service bus 1024 may enable communications between software applications via a queue, via a publish and subscribe technique, and/or direct connection between multiple software applications. The data ingestion 1022 can be configured to “ingest” data received from various data sources (e.g., from the sensors 1010, actuators 1012, dashboards 1038, and/or the like) into data storage. The data ingestion 1022 can be configured to implement continuous real-time and/or batched data ingestion and/or asynchronous data ingestion. The data ingestion 1022 can store data in various databases or file systems (e.g., the data lake 1018). The legacy data 1026 may be data that is collected via systems of the building 10 that are not currently used. However, this legacy data 1026 can be utilized to perform machine learning on historical data. The cloud platform 502 is shown to include artificial intelligence 1028 and cognitive engines 1032. The artificial intelligence 1028 and/or cognitive engines 1032 can be configured to implement various forms of machine learning, data mining, pattern recognition, natural language processing (NLP), and/or the like. The artificial intelligence 1028 and/or the cognitive engines 1032 can be configured to generate and/or train the hybrid models 1036. In some embodiments, the hybrid models 1036 are digital twin models (e.g., the digital twin 904), and can be a model for physical device 902 (e.g., the sensors 1010 and/or the actuators 1012)."); and
a plurality of outputs reflecting the corresponding to a graphical user interface (see para [0045], "FIG. 15 is a graphical illustration of the parameters used to generate a recommendation for load optimization of an AHU, according to some embodiments." and see para [0091], "Enterprise integration layer 410 can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications 426 can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, user identification systems, etc.). Enterprise control applications 426 can also or alternatively be configured to provide configuration GUIs for configuring BAS controller 366. In yet other embodiments, enterprise control applications 426 can work with layers 410-420 to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface 407 and/or BAS interface 409.")
Sharma, however, does not specifically disclose feedback from machine learning regarding anonymized prior similar iterations of other products similar to the product as reflected by the third party data. In analogous art, Cella discloses the following limitations:
feedback from machine learning regarding anonymized prior similar iterations of other products similar to the product as reflected by the third party data (see para [0319], "The value chain management platform 604, referred to in some cases herein for convenience as the platform 604, may include, integrate with, and enable the various value chain network processes, workflows, activities, events and applications 630 described throughout this disclosure that enable an operator to manage more than one aspect of a value chain network environment or entity 652 in a common application environment (e.g., shared, pooled, similarly licenses whether shared data for one person, multiple people, or anonymized), such as one that takes advantage of common data storage in the data storage layer 624, common data collection or monitoring in the monitoring systems layer 614 and/or common adaptive intelligence of the adaptive intelligence layer 614. Outputs from the applications 630 in the platform 604 may be provided to the other data handing layers 624. These may include, without limitation, state and status information for various objects, entities, processes, flows and the like; object information, such as identity, attribute and parameter information for various classes of objects of various data types; event and change information, such as for workflows, dynamic systems, processes, procedures, protocols, algorithms, and other flows, including timing information; outcome information, such as indications of success and failure, indications of process or milestone completion, indications of correct or incorrect predictions, indications of correct or incorrect labeling or classification, and success metrics (including relating to yield, engagement, return on investment, profitability, efficiency, timeliness, quality of service, quality of product, customer satisfaction, and others) among others. Outputs from each application 630 can be stored in the data storage layer 624, distributed for processing by the data collection layer 614, and used by the adaptive intelligence layer 614. The cross-application nature of the platform 604 thus facilitates convenient organization of all of the necessary infrastructure elements for adding intelligence to any given application, such as by supplying machine learning on outcomes across applications, providing enrichment of automation of a given application via machine learning based on outcomes from other applications or other elements of the platform 604, and allowing application developers to focus on application-native processes while benefiting from other capabilities of the platform 604. In examples, there may be systems, components, services and other capabilities that optimize control, automation, or one or more performance characteristics of one or more value chain network entities 652; or ones that may generally improve any of process and application outputs and outcomes 1040 pursued by use of the platform 604. In some examples, outputs and outcomes 1040 from various applications 630 may be used to facilitate automated learning and improvement of classification, prediction, or the like that is involved in a step of a process that is intended to be automated.")
It would have been obvious to one or ordinary skill in the art at the time of the invention to combine the teachings of Cella with Sharma because using such feedback enables more useful third party data from large data sets to gain insight (see Cella, para [0005]-[0008]).
Moreover, it would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for digital product network systems as taught by Cella in the building management system with device twinning of Sharma, since 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.
Claim 2:
Further, Sharma discloses the following limitations:
wherein the received data regarding operation of the hardware includes at least one of connectivity data, performance data, operating environment data and operating context data for currently operating hardware (see para [0081], "In some embodiments, AHU controller 330 receives information from BAS controller 366 (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BAS controller 366 (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller 330 can provide BAS controller 366 with temperature measurements from temperature sensors 362-364, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BAS controller 366 to monitor or control a variable state or condition within building zone 306.")
Claim 3:
Further, Sharma discloses the following limitations:
wherein the received receiving data regarding operation of at least one application operating in conjunction with hardware of the product includes at least one of patient data, user data, operational data, risk data, success rate date, ultimate outcome data and specification data (see para [0080]-[0081], "Still referring to FIG. 3, airside system 300 is shown to include a building automation system (BAS) controller 366 and a client device 368. BAS controller 366 can include one or more computer systems (e.g., servers, supervisory controllers, subsystem controllers, etc.) that serve as system level controllers, application or data servers, head nodes, or master controllers for airside system 300, waterside system 200, HVAC system 100, and/or other controllable systems that serve building 10. BAS controller 366 can communicate with multiple downstream building systems or subsystems (e.g., HVAC system 100, a security system, a lighting system, waterside system 200, etc.) via a communications link 370 according to like or disparate protocols (e.g., LON, BACnet, etc.). In various embodiments, AHU controller 330 and BAS controller 366 can be separate (as shown in FIG. 3) or integrated. In an integrated implementation, AHU controller 330 can be a software module configured for execution by a processor of BAS controller 366. In some embodiments, AHU controller 330 receives information from BAS controller 366 (e.g., commands, setpoints, operating boundaries, etc.) and provides information to BAS controller 366 (e.g., temperature measurements, valve or actuator positions, operating statuses, diagnostics, etc.). For example, AHU controller 330 can provide BAS controller 366 with temperature measurements from temperature sensors 362-364, equipment on/off states, equipment operating capacities, and/or any other information that can be used by BAS controller 366 to monitor or control a variable state or condition within building zone 306.").
Claims 4, 6, 8:
Sharma does not explicitly disclose wherein the digital device health engine includes a digital device mirroring. In analogous art, Cella discloses the following limitations:
wherein the digital device health engine includes a digital device mirroring (see para [0393], "the platform 604 may include, integrate, integrate with, manage, control, coordinate with, or otherwise handle any of a wide variety of digital twins 1700, such as distribution twins 1714 (such as representing distribution facilities, assets, objects, workers, or the like); warehousing twins 1712 (such as representing warehouse facilities, assets, objects, workers and the like); port infrastructure twins 1714 (such as representing a seaport, an airport, or other facility, as well as assets, objects, workers and the like); shipping facility twins 1720; operating facility twins 1722; customer twins 1730 (such as representing physical, behavioral, demographic, psychographic, financial, historical, affinity, interest, and other characteristics of groups of customers or individual customers); worker twins 1740 (such as representing physical attributes, physiologic data, status data, psychographic information, emotional states, states of fatigue/energy, states of attention, skills, training, competencies, roles, authority, responsibilities, work status, activities, and other attributes of or involving workers); wearable/portable device twins 1750; process twins 1760; machine twins 21010 (such as for various machines used to support a value chain network 668); product twins 1780; point of origin twins 1560; supplier twins 1630; supply factor twins 1650; maritime facility twins 1572; floating asset twins 1570; shipyard twins 1620; destination twins 1562; fulfillment twins 1600; delivery system twins 1610; demand factor twins 1640; retailer twins 1790; ecommerce and online site and operator twins 1800; waterway twins 1810; roadway twins 1820; railway twins 1830; air facility twins 1840 (such as twins of aircraft, runways, airports, hangars, warehouses, air travel routes, refueling facilities and other assets, objects, workers and the like used in connection with air transport of products 650); autonomous vehicle twins 1850; robotics twins 1860; drone twins 1870; and logistics factor twins 1880; among others. Each of these may have characteristics of digital twins described throughout this disclosure and the documents incorporated by reference herein, such as mirroring or reflecting changes in states of associated physical objects or other entities, providing capabilities for modeling behavior or interactions of associated physical objects or other entities, enabling simulations, providing indications of status, and many others.")
wherein the digital device health engine includes at least one of a correlation engine and a data weighing engine (see para [0378], "In addition to detecting problem states, the platform 102, such as through the methods of semi-sentient problem recognition, predict a pain point based at least in part on a correlation with a detected problem state. The correlation may be derived from the value chain, such as a shipper cannot deliver international goods until they are processed through customs, or a sales forecast cannot be provided with a high degree of confidence without high quality field data and the like. In embodiments, a predicted pain point may be a point of value chain activity further along a supply chain, an activity that occurs in a related activity (e.g., tax planning is related to tax laws), and the like. A predicted pain point may be assigned a risk value based on aspects of the detected problem state and correlations between the predicted pain point activity and the problem state activity. If a production operation can receive materials from two suppliers, a problem state with one of the suppliers may indicate a low risk of a pain point of use of the material. Likewise, if a demand management application indicates high demand for a good and a problem is detected with information on which the demand is based, a risk of excess inventory (pain point) may be high depending on, for example how far along in the value chain the good has progressed." and see para [0382], "In embodiments, a semi-sentient problem recognition system may include a machine learning/artificial intelligence prediction of a correlated pain point further along a supply chain due to a detected pain point, such as a risk and/or need for overtime, expedited shipping, discounting goods prices, and the like." and see para [0507], "The units and/or nodes may each have one or more connections to other units and/or nodes. The units and/or nodes may be configured to transmit information, e.g., one or more signals, to other units and/or nodes, process signals received from other units and/or nodes, and forward processed signals to other units and/or nodes. One or more of the units and/or nodes and connections therebetween may have one or more numerical “weights” assigned. The assigned weights may be configured to facilitate learning, i.e., training, of the machine learning model 3000. The weights assigned weights may increase and/or decrease one or more signals between one or more units and/or nodes, and in some embodiments may have one or more thresholds associated with one or more of the weights. The one or more thresholds may be configured such that a signal is only sent between one or more units and/or nodes, if a signal and/or aggregate signal crosses the threshold. In some embodiments, the units and/or nodes may be assigned to a plurality of layers, each of the layers having one or both of inputs and outputs.")
wherein the digital device health engine includes a supply chain intelligence engine (see para [0025], "The set of one or more processors collectively execute a job request ingestion system configured to receive job content relating to at least one of picking, packing, moving, storing, warehousing, transporting or delivering of a set of items in a supply chain, the job content including an electronic job request and related data. A job content parsing system is configured to apply filters to the received job content to identify candidate portions thereof for robot automation. A fleet intelligence layer activates a set of intelligence services to process terms in the candidate portions of the job content and receive therefrom at least one recommended robot task and associated contextual information that facilitates robot selection and task ordering in a workflow of robot tasks. A demand intelligence layer provides real time information relating to a parameter of demand for the set of items in the supply chain.").
It would have been obvious to one of ordinary skill in the art at the time of the invention to include the system for digital product network systems as taught by Cella in the building management system with device twinning of Sharma, since 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.
Claim 5:
Further, Sharma discloses the following limitations:
wherein the digital device health engine includes an optimization module for the received supply chain management data (see para [0052], "A digital device twin can be an evolving digital profile of the historical and current behavior of a physical device or process that helps optimize business performance. The digital device twin can be based on herculean, cumulative, and real-time data measurements across an assemblage of dimensions. These measurements may be responsible for creating the evolving profile of the physical device or process and provide users with important insights into system performance." and see para [0091], " Enterprise integration layer 410 can be configured to serve clients or local applications with information and services to support a variety of enterprise-level applications. For example, enterprise control applications 426 can be configured to provide subsystem-spanning control to a graphical user interface (GUI) or to any number of enterprise-level business applications (e.g., accounting systems, user identification systems, etc.). Enterprise control applications 426 can also or alternatively be configured to provide configuration GUIs for configuring BAS controller 366. In yet other embodiments, enterprise control applications 426 can work with layers 410-420 to optimize building performance (e.g., efficiency, energy use, comfort, or safety) based on inputs received at interface 407 and/or BAS interface 409.")
Claim 7:
Further, Sharma discloses the following limitations:
wherein the application data input includes data manipulations unique to the contextual application for the product (see para [0142]-[0143], " Referring now to FIG. 10, a block diagram illustrating the cloud platform 502 in greater detail is shown, according to an exemplary embodiment. Furthermore, FIG. 10 illustrates the process of FIG. 9 within the context of the components of the building 10 and the cloud platform 502. The building 10 is shown to be associated with contextual data 1006, design models 1008, sensors 1010, and actuators 1012. The cloud platform 502 is shown to include an edge processing manager 1014, edge security 1016, a data lake 1018, integration middleware 1020, data ingestion 1022, a service bus 1024, legacy data 1026, artificial intelligence 1028, cognitive engines 1032, hybrid models 1036, notifications 1030, visualizations 1034, and dashboards 1038. The contextual data 1006 may be information for the sensors 1010 and/or actuators 1012 that indicates contextual information of the sensors 1010 and/or the actuators 1012. For example, a particular occupancy sensor may be located in a particular zone, “Zone A.” This contextual data 1006 can be communicated to the cloud platform 502 for use in managing the devices of the building 10. Furthermore, design models 1008 may correspond to particular configuration data files or data design files for the sensors 1010 and/or the actuators 1012. These design models 1008 may include product specifications that include parameters, tolerances, and other information indicative of the correct or incorrect operation of the sensors 1010 and/or actuators 1012. For example, the design models 1008 may include Computer-Aided Design (CAD) models or other visual representations of the sensors 1010 and/or the actuators 1012.")
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Guim Bernat et al. (US 2021/0144517 A1), a system for multi-entity (e.g., multi-tenant) edge computing deployments with various configurations and features that enable the management of resources (e.g., controlling and orchestrating hardware, acceleration, network, processing resource usage), security (e.g., secure execution and communication, isolation, conflicts), and service management (e.g., orchestration, connectivity, workload coordination), in edge computing deployments, such as by a plurality of edge nodes of an edge computing environment configured for executing workloads from among multiple tenants
Nam et al. (KR 20060019932 A), a supply chain committed delivery system that includes the step of inputting the order request to a customer or a salesperson customer relationship management server via the Web and the steps of the analysis process with priority given to the requested order and the steps of: providing a salesperson to turn the customer relationship management customer orders received in connection with the server providing the requested information in order to produce the simulation, and the simulation server to order commit one to calculate the delivery results through customer relationship management server
Zarras et al. "Securing the Software Supply Chain: Innovations and Approaches", a paper discussing applications are built using diverse components sourced externally, such as open-source libraries, cloud services, and hardware, which introduce significant security risks into the software supply chain
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUJAY KONERU whose telephone number is 571-270-3409. The examiner can normally be reached on Monday-Friday, 9 am to 5 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached on 571- 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/SUJAY KONERU/
Primary Examiner, Art Unit 3624