DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
The following FINAL Office Action is in response to Applicant’s communication filed 12/24/2025 regarding Application 18/713,599.
Status of Claim(s)
Claim(s) 1-5, and 7-23 is/are currently pending and are rejected as follows.
Response to Arguments – 101 Rejection
Applicant’s arguments in regards to the previously applied 101 rejection have been fully considered and deemed persuasive.
Examiner therefore withdraws the previously applied 101 rejection.
Response to Arguments – 102/103 Rejection
Applicant’s arguments in regards to the previously applied prior art rejection have been fully considered but are not deemed persuasive.
Applicant argues that the amended prior art, specifically that of a user-provided first set of attributes pertaining to predefined constraints, a user-selected second set pertaining to techniques and a user-selected third set pertaining to tools, and therefore the applied 102 of Cella does not read on Applicant’s claims. Applicant further argues that Cella fails to teach enabling a user to define a persistent, new, customized template based on additional constraints.
Examiner does not find the arguments persuasive, for the following reasons. Firstly, Applicants limitations recite actions for one or more users to operate a system where the users are associated with an entity, and then extracting a variety of first and second attributes based on input parameters pertaining to predefined constraints provided by the users for the entity. Cella is able to extract attributes from parameters from a user as input through an interface (Cella: Paragraph 329, “…In embodiments, an interface to the application store 157 may take input from a developer and/or from the platform (such as from an opportunity miner 153) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence…”) which reads on Applicant’s claims of user-provided constraints. Secondly, Cella allows for a updating of a model, including the parameters and constraints, after the initial inputs and attributes have been determined, which reads on Applicant’s amended language regarding additional constraints for generating the customized templates. Therefore the previously applied 102 and 103 rejections continue to be applicable. Further rationale and citations are given in the amended prior art rejection below.
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1-2, 5, 7-9, 14, 16-19, and 23 is/are rejected under 35 U.S.C. 102(a)(l) as being anticipated by Cella (US 2021/0248514 Al).
Claim(s) 1, and 17-18 –
Cella discloses the following limitations:
one or more processors operatively coupled to a plurality of computing devices, the one or more processors coupled with a memory , wherein said memory stores instructions which when executed by the one or more processors causes said system to (Cella: Paragraph 332, “In embodiments, criteria for determining the recommendation may include the availability of different AI types, models, algorithms, or systems (including heuristic/model-based AI, neural networks, and others). Availability may be limited by the computational environment that the user intends to use such as a given cloud platform, an on-premises IT system, or in a network (edge or other network), and the like and whether a given type, model, or algorithm will run in the client's environment. In embodiments, computational factors and configurations may be criteria. For example, the available processor types for running the AI solution in the client's environment may be a factor including: chipsets, modules, device, cloud components, number, and architecture of processor types (e.g. multi-core processor availability, GPU availability, CPU availability, FPGA availability, custom ASIC availability, and the like), and the like. Additionally, computational factors, which may be expressed as minimum capability criteria, may include available processing capacity, both for solution training (for example utilizing a cloud computing resource) and solution operation deployment environment/capacity (e.g. IoT, in-vehicle, edge, mesh network, on-premises IT solution, stand along, or other deployment environments). Additional criteria may include software and interface criteria such as software environment such as operating systems (Linux, Mac, PC, and the like), languages and protocols used for APIs for access to input data sources for solution training as well as access to runtime data and data integration and output.”)
receive one or more input parameters from one or more users using a predefined template, wherein the one or more users operate through the plurality of computing devices connected to each other through a network , wherein the one or more users are associated with an entity; (Cella: Paragraph 178, "Services/microservices may include application programming interfaces that facilitate connection among the components of the system performing the service (e.g., microservices) and between the system to entities (e.g., programs, web sites, user devices, etc.) that are external to the system. Without limitation to any other aspect of the present disclosure, example microservices that may be present in certain embodiments include (a) a multimodal set of data collection circuits that collect information about and monitor entities related to a lending transaction; (b) blockchain circuits for maintaining a secure historical ledger of events related to a loan, the blockchain circuits having access control features that govern access by a set of parties involved in a loan; (c) a set of application programming interfaces, data integration services, data processing workflows and user interfaces for handling loan-related events and loan-related activities; and (d) smart contract circuits for specifying terms and conditions of smart contracts that govern at least one of loan terms and conditions, loan-related events and loan-related activities. Any of the services/microservices may be controlled by or have control over a controller. Certain systems may not be considered to be a service/microservice. For example, a point of sale device that simply charges a set cost for a good or service may not be a service. In another example, a service that tracks the cost of a good or service and triggers notifications when the value changes may not be a valuation service itself, but may rely on valuation services, and/or may form a portion of a valuation service in certain embodiments. It can be seen that a given circuit, controller, or device may be a service or a part of a service in certain embodiments, such as when the functions or capabilities of the circuit, controller, or device are configured to support a service or microservice as described herein, but may not be a service or part of a service for other embodiments (e.g., where the functions or capabilities of the circuit, controller, or device are not relevant to a service or microservice as described herein). In another example, a mobile device being operated by a user may form a portion of a service as described herein at a first point in time (e.g., when the user accesses a feature of the service through an application or other communication from the mobile device, and/or when a monitoring function is being performed via the mobile device), but may not form a portion of the service at a second point in time (e.g., after a transaction is completed, after the user uninstalls an application, and/or when a monitoring function is stopped and/or passed to another device). Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes or systems, and any such processes or systems may be considered a service (or a part of a service) herein."; Paragraph: 385, "In embodiments the set of data collection and monitoring services includes services selected from among a set of Internet of Things systems that monitor the entities, a set of cameras that monitor the entities, a set of software services that pull information related to the entities from publicly available information sites, a set of mobile devices that report on information related to the entities, a set of wearable devices worn by human entities, a set of user interfaces by which entities provide information about the entities and a set of crowdsourcing services configured to solicit and report information related to the entities."; Paragraph 481, "Referring to FIG. 6, a platform-operated marketplace crowdsourcing system 500 may be configured, such as in a crowdsourcing dashboard interface 618 or other user interface for an operator of the platform-operated marketplace crowdsourcing system 500, using the various enabling capabilities of the lending enablement platform 100 described throughout this disclosure. The operator may use the user interface or dashboard 514 to undertake a series of steps to perform or undertake an algorithm to create a crowdsourcing request for information 518 as described in connection with FIG. 5. In embodiments, one or more of the steps of the algorithm to create a reward 512 within the dashboard 514 may include, at a step 602, identifying potential rewards 512, such as what information 518 is likely to be of value in a given situation (such as may be indicated through various communication channels by stakeholders or representatives of an entity, such as an individual or enterprise, such as attorneys, agents, investigators, parties, auditors, detectives, underwriters, inspectors, and many others)."; Paragraph 2530, “In embodiments, the digital twin management system 12902 creates new digital twins, maintains/updates existing digital twins, and/or renders digital twins. The digital twin management system 12902 may receive user input, uploaded data, and/or sensor data to create and maintain existing digital twins. Upon creating a new digital twin, the digital twin management system 12902 may store the digital twin in the digital twin datastore 12916. Creating, updating, and rendering digital twins are discussed in greater detail throughout the disclosure.”; Paragraph 2560, "In embodiments, the digital twin 131302d may be a digital representation of one or more real-world elements 131302r. The digital twins 131302d are configured to mimic, copy, and/or model behaviors and responses of the real-world elements 131302r in response to inputs, outputs, and/or conditions of the surrounding or ambient environment. Data related to physical properties and responses of the real-world elements 131302r may be obtained, for example, via user input, sensor input, and/or physical modeling (e.g., thermodynamic models, electrodynamic models, mechanodynamic models, etc.). Information for the digital twin 131302d may correspond to and be obtained from the one or more real-world elements 131302r corresponding to the digital twin 131302d. For example, in some embodiments, the digital twin 131302d may correspond to one real-world element 131302r that is a fixed digital vibration sensor 12936 on a machine component, and vibration data for the digital twin 131302d may be obtained by polling or fetching vibration data measured by the fixed digital vibration sensor on the machine component. In a further example, the digital twin 131302d may correspond to a plurality of real-world elements 131302r such that each of the elements can be a fixed digital vibration sensor on a machine component, and vibration data for the digital twin 131302d may be obtained by polling or fetching vibration data measured by each of the fixed digital vibration sensors on the plurality of real-world elements 131302r. Additionally or alternatively, vibration data of a first digital twin 131302d may be obtained by fetching vibration data of a second digital twin 131302d that is embedded within the first digital twin 131302d, and vibration data for the first digital twin 131302d may include or be derived from vibration data for the second digital twin 131302d. For example, the first digital twin may be a digital twin 131302d of an environment 12920 (alternatively referred to as an "environmental digital twin") and the second digital twin 131302d may be a digital twin 131302d corresponding to a vibration sensor disposed within the environment 12920 such that the vibration data for the first digital twin 131302d is obtained from or calculated based on data including the vibration data for the second digital twin 131302d.")
extract a first set of attributes from the one or more input parameters, the first set of attributes pertaining to predefined constraints provided by the one or more users for the entity; (Cella: Paragraph 329, "One set of solutions to these challenges is an artificial intelligence store 157 that is configured to enable collection, organization, recommendation and presentation of relevant sets of artificial intelligence systems based on one or more attributes of a domain and/or a domain-related problem. In embodiments, an artificial intelligence store 157 may include a set of interfaces to artificial intelligence systems, such as enabling the download of relevant artificial intelligence applications, establishment of links or other connections to artificial intelligence systems (such as links to clouddeployed artificial intelligence systems via APis, ports, connectors, or other interfaces) and the like. The artificial intelligence store 157 may include descriptive content with respect to each of a variety of artificial intelligence systems, such as metadata or other descriptive material indicating suitability of a system for solving particular types of problems (e.g., forecasting, NLP, image recognition, pattern recognition, motion detection, route optimization, or many others) and/or for operating on domain-specific inputs, data or other entities. In embodiments, the artificial intelligence store 157 may be organized by category, such as domain, input types, processing types, output types, computational requirements and capabilities, cost, energy usage, and other factors. In embodiments, an interface to the application store 157 may take input from a developer and/or from the platform (such as from an opportunity miner 153) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer's domain-specific problem."; Paragraph 330, "In embodiments, a criteria for determining the recommendation may include level of anticipated human oversight. This may include, among others, understanding the level and types of decisions delegated to human workers (such as a decision to purchase a security, taking a market decision, taking a license on Intellectual property, financial limits on actions and ordering (e.g. is the RPA able to order or commit to transactions below a certain amount, above which a human is involved), the level and type of anticipated human supervision of robotic process automation operations, anticipated extent of human supervision and/or governance of model training and training data set selection. A further consideration may be the level and type of anticipated human involvement in the curation of model versions (such as identifying historical break points where input data should be discarded); and others."; Paragraph 331, "In embodiments, criteria for determining the recommendation may include security considerations such as adversarial training and complex environments such as network attacks, viruses, and the like. Additional security considerations may include the security and management of historic training datasets, including audit trails. Security considerations may include the model traceability and accuracy-how will the model or controlling parameters be updated, who will have authority to update the model, how will the updates be documented, how will results be correlated with model updates, and the like. How will version control be implemented and documented. Another security consideration will be documentation of the results of the AI for audit trails including financial results and performance results."; Paragraph 335, "In embodiments, criteria may include the ability of the client to access a given type or model due to license requirements and limitations, client policies (described elsewhere herein), regulations (including in the client's jurisdiction, the jurisdiction of the data source (e.g. European data privacy laws and Safe Harbor), a jurisdiction governing a particular model, algorithm, or the like ( e.g. export controls on technology), permissions (e.g. training data or operational data), and the like. Additionally, the recommendation may be influenced by the type of problem to be solved and whether there are specialized algorithms or methods that are optimized for the type of problem (e.g. quantum annealing based traveling salesperson solver or even classic heuristic methods that provide for reasonable baseline results)."; Paragraph 337, "In embodiments, the criteria for recommending an AI solution may include criteria regarding data availability such as the availability of data sources of adequate size, granularity, quality, reliability, location, time zones, accuracy, or the like for effective model training. Additional criteria regarding data availability may include the cost of data for: inputs for the model training, input for model operation. Additional criteria may include the availability of data for operation of the AI solution, and the like. Criteria for AI selection may further include upstream data processing requirements, master data management considerations such as dimensional cleanup and data validation, and the like.")
extract a second set of attributes based on the first set of attributes, the second set of attributes pertaining to one or more techniques associated with the problem, elected by the one or more users for the entity; (Cella: Paragraph 329, "One set of solutions to these challenges is an artificial intelligence store 157 that is configured to enable collection, organization, recommendation and presentation of relevant sets of artificial intelligence systems based on one or more attributes of a domain and/or a domain-related problem. In embodiments, an artificial intelligence store 157 may include a set of interfaces to artificial intelligence systems, such as enabling the download of relevant artificial intelligence applications, establishment of links or other connections to artificial intelligence systems (such as links to cloud-deployed artificial intelligence systems via APis, ports, connectors, or other interfaces) and the like. The artificial intelligence store 157 may include descriptive content with respect to each of a variety of artificial intelligence systems, such as metadata or other descriptive material indicating suitability of a system for solving particular types of problems (e.g., forecasting, NLP, image recognition, pattern recognition, motion detection, route optimization, or many others) and/or for operating on domain-specific inputs, data or other entities. In embodiments, the artificial intelligence store 157 may be organized by category, such as domain, input types, processing types, output types, computational requirements and capabilities, cost, energy usage, and other factors. In embodiments, an interface to the application store 157 may take input from a developer and/or from the platform (such as from an opportunity miner 153) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer's domain-specific problem."; Paragraph 330, "In embodiments, a criteria for determining the recommendation may include level of anticipated human oversight. This may include, among others, understanding the level and types of decisions delegated to human workers (such as a decision to purchase a security, taking a market decision, taking a license on Intellectual property, financial limits on actions and ordering (e.g. is the RPA able to order or commit to transactions below a certain amount, above which a human is involved), the level and type of anticipated human supervision of robotic process automation operations, anticipated extent of human supervision and/or governance of model training and training data set selection. A further consideration may be the level and type of anticipated human involvement in the curation of model versions (such as identifying historical break points where input data should be discarded); and others."; Paragraph 331, "In embodiments, criteria for determining the recommendation may include security considerations such as adversarial training and complex environments such as network attacks, viruses, and the like. Additional security considerations may include the security and management of historic training datasets, including audit trails. Security considerations may include the model traceability and accuracy-how will the model or controlling parameters be updated, who will have authority to update the model, how will the updates be documented, how will results be correlated with model updates, and the like. How will version control be implemented and documented. Another security consideration will be documentation of the results of the AI for audit trails including financial results and performance results."; Paragraph 335, "In embodiments, criteria may include the ability of the client to access a given type or model due to license requirements and limitations, client policies (described elsewhere herein), regulations (including in the client's jurisdiction, the jurisdiction of the data source (e.g. European data privacy laws and Safe Harbor), a jurisdiction governing a particular model, algorithm, or the like (e.g. export controls on technology), permissions (e.g. training data or operational data), and the like. Additionally, the recommendation may be influenced by the type of problem to be solved and whether there are specialized algorithms or methods that are optimized for the type of problem (e.g. quantum annealing based traveling salesperson solver or even classic heuristic methods that provide for reasonable baseline results)."; Paragraph 337, "In embodiments, the criteria for recommending an AI solution may include criteria regarding data availability such as the availability of data sources of adequate size, granularity, quality, reliability, location, time zones, accuracy, or the like for effective model training. Additional criteria regarding data availability may include the cost of data for: inputs for the model training, input for model operation. Additional criteria may include the availability of data for operation of the AI solution, and the like. Criteria for AI selection may further include upstream data processing requirements, master data management considerations such as dimensional cleanup and data validation, and the like."; Paragraph 2610, "In embodiments, the digital twin system 12900 includes design specification information for representing a real-world element 131302r using a digital twin 131302d. The digital may correspond to an existing real-world element 131302r or a potential real-world element 131302r. The design specification information may be received from one or more sources. For example, the design specification information may include design parameters set by user input, determined by the digital twin system 12900 (e.g., the via digital twin simulation system 12906), optimized by users or the digital twin simulation system 12906, combinations thereof, and the like. The digital twin simulation system 12906 may represent the design specification information for the component to users, for example, via a display device or a wearable device. The design specification information may be displayed schematically (e.g., as part of a process diagram or table of information) or as part of an augmented reality or virtual reality display. The design specification information may be displayed, for example, in response to a user interaction with the digital twin system 12900 (e.g., via user selection of the element or user selection to generally include design specification information within displays). Additionally or alternatively, the design specification information may be displayed automatically, for example, upon the element coming within view of an augmented reality or virtual reality device. In embodiments, the displayed design specification information may further include indicia of information source (e.g., different displayed colors indicate user input versus digital twin system 12900 determination), indicia of mismatches (e.g., between design specification information and operational information), combinations thereof, and the like."; Paragraph 2770, “In embodiments, the digital twin system 12900 includes design specification information for representing a real-world element 131302r using a digital twin 131302d. The digital may correspond to an existing real-world element 131302r or a potential real-world element 131302r. The design specification information may be received from one or more sources. For example, the design specification information may include design parameters set by user input, determined by the digital twin system 12900 (e.g., the via digital twin simulation system 12906), optimized by users or the digital twin simulation system 12906, combinations thereof, and the like. The digital twin simulation system 12906 may represent the design specification information for the component to users, for example, via a display device or a wearable device. The design specification information may be displayed schematically (e.g., as part of a process diagram or table of information) or as part of an augmented reality or virtual reality display. The design specification information may be displayed, for example, in response to a user interaction with the digital twin system 12900 (e.g., via user selection of the element or user selection to generally include design specification information within displays). Additionally or alternatively, the design specification information may be displayed automatically, for example, upon the element coming within view of an augmented reality or virtual reality device. In embodiments, the displayed design specification information may further include indicia of information source (e.g., different displayed colors indicate user input versus digital twin system 12900 determination), indicia of mismatches (e.g., between design specification information and operational information), combinations thereof, and the like.”)
extract a third set of attributes based on the second set of attributes, the third set of attributes pertaining to one or more tools associated with the one or more techniques, selected by the one or more users for the entity; (Cella: Paragraph 329, "One set of solutions to these challenges is an artificial intelligence store 157 that is configured to enable collection, organization, recommendation and presentation of relevant sets of artificial intelligence systems based on one or more attributes of a domain and/or a domain-related problem. In embodiments, an artificial intelligence store 157 may include a set of interfaces to artificial intelligence systems, such as enabling the download of relevant artificial intelligence applications, establishment of links or other connections to artificial intelligence systems (such as links to cloud-deployed artificial intelligence systems via APis, ports, connectors, or other interfaces) and the like. The artificial intelligence store 157 may include descriptive content with respect to each of a variety of artificial intelligence systems, such as metadata or other descriptive material indicating suitability of a system for solving particular types of problems (e.g., forecasting, NLP, image recognition, pattern recognition, motion detection, route optimization, or many others) and/or for operating on domain-specific inputs, data or other entities. In embodiments, the artificial intelligence store 157 may be organized by category, such as domain, input types, processing types, output types, computational requirements and capabilities, cost, energy usage, and other factors. In embodiments, an interface to the application store 157 may take input from a developer and/or from the platform (such as from an opportunity miner 153) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer's domain-specific problem."; Paragraph 330, "In embodiments, a criteria for determining the recommendation may include level of anticipated human oversight. This may include, among others, understanding the level and types of decisions delegated to human workers (such as a decision to purchase a security, taking a market decision, taking a license on Intellectual property, financial limits on actions and ordering (e.g. is the RPA able to order or commit to transactions below a certain amount, above which a human is involved), the level and type of anticipated human supervision of robotic process automation operations, anticipated extent of human supervision and/or governance of model training and training data set selection. A further consideration may be the level and type of anticipated human involvement in the curation of model versions (such as identifying historical break points where input data should be discarded); and others."; Paragraph 331, "In embodiments, criteria for determining the recommendation may include security considerations such as adversarial training and complex environments such as network attacks, viruses, and the like. Additional security considerations may include the security and management of historic training datasets, including audit trails. Security considerations may include the model traceability and accuracy-how will the model or controlling parameters be updated, who will have authority to update the model, how will the updates be documented, how will results be correlated with model updates, and the like. How will version control be implemented and documented. Another security consideration will be documentation of the results of the AI for audit trails including financial results and performance results."; Paragraph 335, "In embodiments, criteria may include the ability of the client to access a given type or model due to license requirements and limitations, client policies (described elsewhere herein), regulations (including in the client's jurisdiction, the jurisdiction of the data source (e.g. European data privacy laws and Safe Harbor), a jurisdiction governing a particular model, algorithm, or the like (e.g. export controls on technology), permissions (e.g. training data or operational data), and the like. Additionally, the recommendation may be influenced by the type of problem to be solved and whether there are specialized algorithms or methods that are optimized for the type of problem (e.g. quantum annealing based traveling salesperson solver or even classic heuristic methods that provide for reasonable baseline results)."; Paragraph 332, "In embodiments, criteria for determining the recommendation may include the availability of different AI types, models, algorithms, or systems (including heuristic/modelbased Al, neural networks, and others). Availability may be limited by the computational environment that the user intends to use such as a given cloud platform, an on-premises IT system, or in a network (edge or other network), and the like and whether a given type, model, or algorithm will run in the client's environment. In embodiments, computational factors and configurations may be criteria. For example, the available processor types for running the AI solution in the client's environment may be a factor including: chipsets, modules, device, cloud components, number, and architecture of processor types (e.g. multi-core processor availability, GPU availability, CPU availability, FPGA availability, custom ASIC availability, and the like), and the like. Additionally, computational factors, which may be expressed as minimum capability criteria, may include available processing capacity, both for solution training (for example utilizing a cloud computing resource) and solution operation deployment environment/capacity (e.g. loT, in-vehicle, edge, mesh network, on-premises IT solution, stand along, or other deployment environments). Additional criteria may include software and interface criteria such as software environment such as operating systems (Linux, Mac, PC, and the like), languages and protocols used for APis for access to input data sources for solution training as well as access to runtime data and data integration and output."; Paragraph 337, "In embodiments, the criteria for recommending an AI solution may include criteria regarding data availability such as the availability of data sources of adequate size, granularity, quality, reliability, location, time zones, accuracy, or the like for effective model training. Additional criteria regarding data availability may include the cost of data for: inputs for the model training, input for model operation. Additional criteria may include the availability of data for operation of the AI solution, and the like. Criteria for AI selection may further include upstream data processing requirements, master data management considerations such as dimensional cleanup and data validation, and the like."; Paragraph 2610, "In embodiments, the digital twin system 12900 includes design specification information for representing a real-world element 131302r using a digital twin 131302d. The digital may correspond to an existing real-world element 131302r or a potential real-world element 131302r. The design specification information may be received from one or more sources. For example, the design specification information may include design parameters set by user input, determined by the digital twin system 12900 (e.g., the via digital twin simulation system 12906), optimized by users or the digital twin simulation system 12906, combinations thereof, and the like. The digital twin simulation system 12906 may represent the design specification information for the component to users, for example, via a display device or a wearable device. The design specification information may be displayed schematically (e.g., as part of a process diagram or table of information) or as part of an augmented reality or virtual reality display. The design specification information may be displayed, for example, in response to a user interaction with the digital twin system 12900 (e.g., via user selection of the element or user selection to generally include design specification information within displays). Additionally or alternatively, the design specification information may be displayed automatically, for example, upon the element coming within view of an augmented reality or virtual reality device. In embodiments, the displayed design specification information may further include indicia of information source (e.g., different displayed colors indicate user input versus digital twin system 12900 determination), indicia of mismatches (e.g., between design specification information and operational information), combinations thereof, and the like.")
based on the first set of attributes, the second set of attributes and the third set of attributes, generate an optimized model through an Al engine, wherein the Al engine is configured to use the one or more tools and the one or more techniques; and (Cella: Paragraph 335, "In embodiments, criteria may include the ability of the client to access a given type or model due to license requirements and limitations, client policies (described elsewhere herein), regulations (including in the client's jurisdiction, the jurisdiction of the data source (e.g. European data privacy laws and Safe Harbor), a jurisdiction governing a particular model, algorithm, or the like (e.g. export controls on technology), permissions (e.g. training data or operational data), and the like. Additionally, the recommendation may be influenced by the type of problem to be solved and whether there are specialized algorithms or methods that are optimized for the type of problem (e.g. quantum annealing based traveling salesperson solver or even classic heuristic methods that provide for reasonable baseline results)."; Paragraph 2418, "In some embodiments, the machine learning model 11102 may be defined via supervised learning, i.e. one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs. The training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e. a supervisory signal. Each of the training examples may be represented in the machine learning model 11102 by an array and/or a vector, i.e. a feature vector. The training data may be represented in the machine learning model 11102 by a matrix. The machine learning model 11102 may learn one or more functions via terative optimization of an objective function, thereby learning to predict an output associated with new inputs. Once optimized, the objective function may provide the machine learning model 11102 with the ability to accurately determine an output for inputs other than inputs included in the training data. In some embodiments, the machine learning model 11102 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model 11102, a user and/or an information source to label new data points with desired outputs. Statistical classification may include identifying, by the machine learning model 11102, to which a set of subcategories, i.e. subpopulations, a new observation belongs based on a training set of data containing observations having known categories. Regression analysis may include estimating, by the machine learning model 11102 relationships between a dependent variable, i.e. an outcome variable, and one or more independent variables, i.e. predictors, covariates, and/or features. Similarity learning may include learning, by the machine learning model 11102, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are."; Paragraph 2485, "As described elsewhere herein, this disclosure concerns systems and methods for the discovery of opportunities for increased automation and intelligence, including solutions to domain-specific problems. Further, this disclosure also concerns selection and configuration of an artificial intelligence solution (e.g. neural networks, machine learning systems, expert systems, etc.) once opportunities are discovered."; Paragraph 2487, "The input 12702 may be processed by the opportunity mining module 153 to determine whether an artificial intelligence system can be applied to the task or the domain. For example, the attribute input 12702 may include an attribute of a task, domain or problem, such as a negotiating task, a drafting task, a data entry task, an email response task, a data analysis task, a document review task, an equipment operation task, a forecasting task, an NLP task, an image recognition task, a pattern recognition task, a motion detection task, a route optimization task, and the like. The opportunity mining module 153 may determine if one or more attributes of the task are similar to other tasks that have been automated or to which an intelligence has been applied, or based on the attribute of the task, if the task is potentially automatable or suitable to have an intelligence applied to it regardless of whether it has been done previously. For example, attributes of a drafting task may include articulating a first idea, articulating a second idea, articulating a plurality of ideas, combining the plurality of ideas in a pairwise fashion, and combining the ideas in a triplicate fashion. Articulating ideas may not be suitable for automation, but the task of combining ideas pairwise or in triplicate form may be suitable for automation or to have an intelligence applied to the task."; Paragraph 2492, "Once an artificial intelligence model 12718 or components thereof are identified by the artificial intelligence search engine 12710, either by searching with the input 12702 alone or with both the input 12702 and a selection criteria 12714, an artificial intelligence configuration module 12704 may configure one or more data inputs 12720 to use with the at least one artificial intelligence model 12718 or model component. The artificial intelligence configuration module 12704 may, in certain embodiments, be operative in discovering and selecting what inputs 12720 may enable effective and efficient use of artificial intelligence for a given problem. In embodiments, the artificial intelligence configuration module 12704 may further configure the at least one artificial intelligence model 12718 or model component(s) in accordance with at least one configuration criteria 12722. In embodiments, individual data inputs and model components may be configured via one or more configuration criteria, while in other embodiments, a single configuration criteria governs configuration of data input, AI component assembly, and the like."; Paragraph 2501, "The method may further include ranking one or more results of the search according to a strength or a weakness of the at least one artificial intelligence model relative to the at least one selection criteria 12812. The method may further include configuring the at least one artificial intelligence model or model component in accordance with at least one configuration criteria 12814. The method may further include collaborative filtering search results comprising the at least one artificial intelligence model using an element of the at least one artificial intelligence model selected or model component by a user 12816. The method may further include clustering search results comprising the at least one artificial intelligence model or model component with a clustering engine 12818.")
auto-recommend, the customized solution for the predefined problem, based on the optimized model. (Cella: Paragraph 329, "One set of solutions to these challenges is an artificial intelligence store 157 that is configured to enable collection, organization, recommendation and presentation of relevant sets of artificial intelligence systems based on one or more attributes of a domain and/or a domain-related problem. In embodiments, an artificial intelligence store 157 may include a set of interfaces to artificial intelligence systems, such as enabling the download of relevant artificial intelligence applications, establishment of links or other connections to artificial intelligence systems (such as links to clouddeployed artificial intelligence systems via APis, ports, connectors, or other interfaces) and the like. The artificial intelligence store 157 may include descriptive content with respect to each of a variety of artificial intelligence systems, such as metadata or other descriptive material indicating suitability of a system for solving particular types of problems (e.g., forecasting, NLP, image recognition, pattern recognition, motion detection, route optimization, or many others) and/or for operating on domain-specific inputs, data or other entities. In embodiments, the artificial intelligence store 157 may be organized by category, such as domain, input types, processing types, output types, computational requirements and capabilities, cost, energy usage, and other factors. In embodiments, an interface to the application store 157 may take input from a developer and/or from the platform (such as from an opportunity miner 153) that indicates one or more attributes of a problem that may be addressed through artificial intelligence and may provide a set of recommendations, such as via an artificial intelligence attribute search engine, for a subset of artificial intelligence solutions that may represent favorable candidates based on the developer's domain-specific problem."; Paragraph 335, "In embodiments, criteria may include the ability of the client to access a given type or model due to license requirements and limitations, client policies (described elsewhere herein), regulations (including in the client's jurisdiction, the jurisdiction of the data source (e.g. European data privacy laws and Safe Harbor), a jurisdiction governing a particular model, algorithm, or the like (e.g. export controls on technology), permissions (e.g. training data or operational data), and the like. Additionally, the recommendation may be influenced by the type of problem to be solved and whether there are specialized algorithms or methods that are optimized for the type of problem (e.g. quantum annealing based traveling salesperson solver or even classic heuristic methods that provide for reasonable baseline results)."; 340, "Search results or recommendations may, in embodiments, be based at least in part on collaborative filtering, such as by asking developers to indicate or select elements of favorable models, as well as by clustering, such as by using similarity matrices, k-means clustering, or other clustering techniques that associate similar developers, similar domain-specific problems, and/or similar artificial intelligence solutions. The artificial intelligence store 157 may include e-commerce features, such as ratings, reviews, links to relevant content, and mechanisms for provisioning, licensing, delivery and payment (including allocation of payments to affiliates and or contributors), including ones that operate using smart contract and/or blockchain features to automate purchasing, licensing, payment tracking, settlement of transactions, or other features.")
wherein the optimized platform is further configured to: enable the one or more users to add an additional set of parameters with the one or more input parameters to generate a customized template, wherein the additional set of parameters pertain to additional constraints provided by the one or more users for the entity. (Cella: Paragraph 331, “In embodiments, criteria for determining the recommendation may include security considerations such as adversarial training and complex environments such as network attacks, viruses, and the like. Additional security considerations may include the security and management of historic training datasets, including audit trails. Security considerations may include the model traceability and accuracy—how will the model or controlling parameters be updated, who will have authority to update the model, how will the updates be documented, how will results be correlated with model updates, and the like. How will version control be implemented and documented. Another security consideration will be documentation of the results of the AI for audit trails including financial results and performance results.”; Paragraph 2418, “In some embodiments, the machine learning model 11102 may be defined via supervised learning, i.e. one or more algorithms configured to build a mathematical model of a set of training data containing one or more inputs and desired outputs. The training data may consist of a set of training examples, each of the training examples having one or more inputs and desired outputs, i.e. a supervisory signal. Each of the training examples may be represented in the machine learning model 11102 by an array and/or a vector, i.e. a feature vector. The training data may be represented in the machine learning model 11102 by a matrix. The machine learning model 11102 may learn one or more functions via iterative optimization of an objective function, thereby learning to predict an output associated with new inputs. Once optimized, the objective function may provide the machine learning model 11102 with the ability to accurately determine an output for inputs other than inputs included in the training data. In some embodiments, the machine learning model 11102 may be defined via one or more supervised learning algorithms such as active learning, statistical classification, regression analysis, and similarity learning. Active learning may include interactively querying, by the machine learning model 11102, a user and/or an information source to label new data points with desired outputs. Statistical classification may include identifying, by the machine learning model 11102, to which a set of subcategories, i.e. subpopulations, a new observation belongs based on a training set of data containing observations having known categories. Regression analysis may include estimating, by the machine learning model 11102 relationships between a dependent variable, i.e. an outcome variable, and one or more independent variables, i.e. predictors, covariates, and/or features. Similarity learning may include learning, by the machine learning model 11102, from examples using a similarity function, the similarity function being designed to measure how similar or related two objects are.”; Paragraph 2530, “In embodiments, the digital twin management system 12902 creates new digital twins, maintains/updates existing digital twins, and/or renders digital twins. The digital twin management system 12902 may receive user input, uploaded data, and/or sensor data to create and maintain existing digital twins. Upon creating a new digital twin, the digital twin management system 12902 may store the digital twin in the digital twin datastore 12916. Creating, updating, and rendering digital twins are discussed in greater detail throughout the disclosure.”; Paragraph 2560, “In embodiments, the digital twin 131302d may be a digital representation of one or more real-world elements 131302r. The digital twins 131302d are configured to mimic, copy, and/or model behaviors and responses of the real-world elements 131302r in response to inputs, outputs, and/or conditions of the surrounding or ambient environment. Data related to physical properties and responses of the real-world elements 131302r may be obtained, for example, via user input, sensor input, and/or physical modeling (e.g., thermodynamic models, electrodynamic models, mechanodynamic models, etc.). Information for the digital twin 131302d may correspond to and be obtained from the one or more real-world elements 131302r corresponding to the digital twin 131302d. For example, in some embodiments, the digital twin 131302d may correspond to one real-world element 131302r that is a fixed digital vibration sensor 12936 on a machine component, and vibration data for the digital twin 131302d may be obtained by polling or fetching vibration data measured by the fixed digital vibration sensor on the machine component. In a further example, the digital twin 131302d may correspond to a plurality of real-world elements 131302r such that each of the elements can be a fixed digital vibration sensor on a machine component, and vibration data for the digital twin 131302d may be obtained by polling or fetching vibration data measured by each of the fixed digital vibration sensors on the plurality of real-world elements 131302r. Additionally or alternatively, vibration data of a first digital twin 131302d may be obtained by fetching vibration data of a second digital twin 131302d that is embedded within the first digital twin 131302d, and vibration data for the first digital twin 131302d may include or be derived from vibration data for the second digital twin 131302d. For example, the first digital twin may be a digital twin 131302d of an environment 12920 (alternatively referred to as an “environmental digital twin”) and the second digital twin 131302d may be a digital twin 131302d corresponding to a vibration sensor disposed within the environment 12920 such that the vibration data for the first digital twin 131302d is obtained from or calculated based on data including the vibration data for the second digital twin 131302d.”; Paragraph 2607, “The digital twin system 12900 may alter traffic patterns (e.g., by providing updated navigational data to one or more of the mobile elements) to achieve one or more predetermined criteria. The predetermined criteria may include, for example, increasing process efficiency, decreasing interactions between laden workers and mobile equipment assets, minimizing worker path length, routing mobile equipment around paths or potential paths of persons, combinations thereof, and the like.”; Paragraph 2627, “In some embodiments, the set of properties of a digital twin of an industrial worker that may be updated by the digital twin dynamic model system 12908 using dynamic models 129100 may include the status of the worker, the location of the worker, a stress measure for the worker, a task being performed by the worker, a performance measure for the worker, and the like. Dynamic models associated with a digital twin of an industrial worker can be configured to calculate or output values for such properties based on input data, which then may be used to populate industrial worker digital twin. In embodiments, industrial worker dynamic models (e.g., psychometric models) can be configured to predict reactions to stimuli, such as cues that are given to workers to direct them to undertake tasks and/or alerts or warnings that are intended to induce safe behavior. In embodiments, industrial worker dynamic models may be workflow models (Gantt charts and the like), failure mode effects analysis models (FMEA), biophysical models (such as to model worker fatigue, energy utilization, hunger), and the like.”; Paragraph 2634, “In embodiments, the digital twin dynamic model system 12908 may receive requests to update one or more properties of digital twins of industrial entities and/or environments such that the digital twins represent the industrial entities and/or environments in real-time. At 133100, the digital twin dynamic model system 12908 receives a request to update one or more properties of one or more of the digital twins of industrial entities and/or environments. For example, the digital twin dynamic model system 12908 may receive the request from a client application 12970 or from another process executed by the digital twin system 12900 (e.g., a predictive maintenance process). The request may indicate the one or more properties and the digital twin or digital twins implicated by the request. In step 133102, the digital twin dynamic model system 12908 determines the one or more digital twins required to fulfill the request and retrieves the one or more required digital twins, including any embedded digital twins, from digital twin datastore 12916. At 133104, digital twin dynamic model system 12908 determines one or more dynamic models required to fulfill the request and retrieves the one or more required dynamic models from digital twin dynamic model datastore 129102. At 133106, the digital twin dynamic model system 12908 selects one or more sensors from sensor system 12930, data collected from Internet of Things connected devices 12924, and/or other data sources from digital twin I/O system 12904 based on available data sources and the one or more required inputs of the dynamic model(s). In embodiments, the data sources may be defined in the inputs required by the one or more dynamic models or may be selected using a lookup table. At 133108, the digital twin dynamic model system 12908 retrieves the selected data from digital twin I/O system 12904. At 133110, digital twin dynamic model system 12908 runs the dynamic model(s) using the retrieved input data (e.g., vibration sensor data, Industrial Internet of Things device data, and the like) as inputs and determines one or more output values based on the dynamic model(s) and the input data. At 133112, the digital twin dynamic model system 12908 updates the values of one or more properties of the one or more digital twins based on the one or more outputs of the dynamic model(s).”)
Claim(s) 2 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
generate an authentication token to enable the one or more users to access the predefined template. (Cella: Paragraph 193, "The terms ledger and distributed ledger (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a ledger may be a document, file, computer file, database, book, and the like which maintains a record of transactions. Ledgers may be physical or digital. Ledgers may include records related to sales, accounts, purchases, transactions, assets, liabilities, incomes, expenses, capital, and the like. Ledgers may provide a history of transactions that may be associated with time. Ledgers may be centralized or decentralized/distributed. A centralized ledger may be a document that is controlled, updated, or viewable by one or more selected entities or a clearinghouse and wherein changes or updates to the ledger are governed or controlled by the entity or clearinghouse. A distributed ledger may be a ledger that is distributed across a plurality of entities, participants or regions which may independently, concurrently, or consensually, update, or modify their copies of the ledger. Ledgers and distributed ledgers may include security measures and cryptographic functions for signing, concealing, or verifying content. In the case of distributed ledgers, blockchain technology may be used. In the case of distributed ledgers implemented using blockchain, the ledger may be Merkle trees comprising a linked list of nodes in which each node contains hashed or encrypted transactional data of the previous nodes. Certain records of transactions may not be considered ledgers. A file, computer file, database, or book may or may not be a ledger depending on what data it stores, how the data is organized, maintained, or secured. For example, a list of transactions may not be considered a ledger if it cannot be trusted or verified, and/or if it is based on inconsistent, fraudulent, or incomplete data. Data in ledgers may be organized in any format such as tables, lists, binary streams of data, or the like which may depend on convenience, source of data, type of data, environment, applications, and the like. A ledger that is shared among various entities may not be a distributed ledger, but the distinction of distributed may be based on which entities are authorized to make changes to the ledger and/or how the changes are shared and processed among the different entities. Accordingly, the benefits of the present disclosure may be applied in a wide variety of data, and any such data may be considered ledgers herein, while in certain embodiments a given data may not be considered a ledger herein. One of skill in the art, having the benefit of the disclosure herein and knowledge about contemplated ledgers and distributed ledger ordinarily available to that person, can readily determine which aspects of the present disclosure can be utilized to implement, and/or will benefit a particular ledger. Certain considerations for the person of skill in the art, in determining whether a contemplated data is a ledger and/or whether aspects of the present disclosure can benefit or enhance the contemplated ledger include, without limitation: the security of the data in the ledger (can the data be tampered or modified), the time associated with making changes to the data in the ledger, cost of making changes ( computationally and monetarily), detail of data, organization of data (does the data need to be processed for use in an application), who controls the ledger ( can the party be trusted or relied to manage the ledger), confidentiality of the data (who can see or track the data in the ledger), size of the infrastructure, communication requirements ( distributed ledgers may require a communication interface or specific infrastructure), resiliency. While specific examples of blockchain services and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure."; Paragraph 220, "Without limitation to any other aspect or description of the present disclosure, a token includes any token including, without limitation, a token of value, such as collateral, an asset, a reward, such as in a token serving as representation of value, such as a value holding voucher that can be exchanged for goods or services. Certain components may not be considered tokens individually, but may be considered tokens in an aggregated system-for example, a value placed on an asset may not be in itself be a token, but the value of an asset may be placed in a token of value, such as to be stored, exchanged, traded, and the like. For instance, in a non-limiting example, a blockchain circuit may be structured to provide lenders a mechanism to store the value of assets, where the value attributed to the token is stored in a distributed ledger of the blockchain circuit, but the token itself, assigned the value, may be exchanged or traded such as through a token marketplace. In certain embodiments, a toke may be considered a token for some purposes but not for other purposes-for example, a token may be used to as an indication of ownership of an asset, but this use of a token would not be traded as a value where a token including the value of the asset might. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a token herein, while in certain embodiments a given system may not be considered a token herein. One of skill in the art, having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is a token and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation, access data such as relating to rights of access, tickets, and tokens; use in an investment application such as for investment in shares, interests, and tokens; a token-trading application; a token-based marketplace; forms of consideration such as monetary rewards and tokens; translating the value of a resources in tokens; a cryptocurrency token; indications of ownership such as identity information, event information, and token information; a blockchainbased access token traded in a marketplace application; pricing application such as for setting and monitoring pricing for contingent access rights, underlying access rights, tokens, and fees; trading applications such as for trading or exchanging contingent access rights or underlying access rights or tokens; tokens created and stored on a blockchain for contingent access rights resulting in an ownership (e.g., a ticket); and the like."; Paragraph 231, "The term user interface as utilized herein may be understood as a type of interface to allow a user to interact with a system, computer, or other apparatus, in which interaction or communication is achieved through graphical devices or representations. A user interface may be a component of a computer, which may be embodied in software, hardware, or a combination thereof. The user interface may be stored on a medium or in memory. User interfaces may include drop-down menus, tables, forms, or the like with default, templated, recommended, or pre-configured conditions. In certain embodiments, a user interface may include voice interaction. Without limitation to any other aspect or description of the present disclosure, a user interface may be used in conjunction with applications, circuits, controllers, processes, modules, services, layers, devices, components, machines, products, sub-systems, interfaces, connections, or as part of a system. User interfaces may serve a number of different purposes or be configured for different applications or contexts. For example, a lender-side user interface may include features to view a plurality of customer profiles, but may be restricted from making certain changes. A debtor-side user interface may include features to view details and make changes to a user account. A 3rd party neutral-side interface (e.g. a 3.sup.rd party not having an interest in an underlying transaction, such as a regulator, auditor, etc.) may have features that enable a view of company oversight and anonymized user data without the ability to manipulate any data, and may have scheduled access depending upon the 3.sup.rd party and the purpose for the access. A 3rd party interested-side interface (e.g. a 3.sup.rd party that may have an interest in an underlying transaction, such as a collector, debtor advocate, investigator, partial owner, etc.) may include features enabling a view of particular user data with restrictions on making changes. Many more features of these user interfaces may be available to implement embodiments of the systems and/or procedures described throughout the present disclosure. Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes and systems, and any such processes or systems may be considered a service herein. One of skill in the art, having the benefit of the disclosure herein and knowledge about a user interface, can readily determine the purposes and use of a user interface in various embodiments and contexts disclosed herein. Certain considerations for the person of skill in the art, in determining whether a contemplated interface is a user interface and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: configurable views, ability to restrict manipulation or views, report functions, ability to manipulate user profile and data, implement regulatory requirements, provide the desired user features for borrowers, lenders, and 3.sup.rd parties, and the like."; Paragraph 289, "The term contingencies (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a contingency includes any contingency including, without limitation, any action that is dependent upon a second action. For instance, a service may be provided as contingent on a certain parameter value, such as collecting data as condition upon an asset tag indication from an Internet of Things circuit. In another instance, an accommodation such as a hotel reservation may be contingent upon a concert (local to the hotel and at the same time as the reservation) proceeding as scheduled. Certain components may not be considered as relating to a contingency individually, but may be considered related to a contingency in an aggregated system-for example, a data input collected from a data collection service circuit may be stored, analyzed, processed, and the like, and not be considered with respect to a contingency, however a smart contracts service circuit may apply a contract term as being contingent upon the collected data. For instance, the data may indicate a collateral status with respect to a loan transaction, and the smart contracts service circuit may apply that data to a term of contract that depends upon the collateral. In certain embodiments, a contingency may be considered contingency for some purposes but not for other purposes-for example, a delivery of contingent access rights for a future event may be contingent upon a loan condition being satisfied, but the loan condition on its own may not be considered a contingency in the absence of the contingency linkage between the condition and the access rights. Additionally, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether such systems are related to a contingency, and/or which type of contingency. For example, two algorithms may both create a forward market event access right token, but where the first algorithm creates the token free of contingencies and the second algorithm creates a token with a contingency for delivery of the token. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a contingency herein, while in certain embodiments a given system may not be considered a contingency herein. One of skill in the art, having the benefit of the disclosure herein and know ledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is a contingency and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation a forward market operated within or by the platform may be a contingent forward market, such as one where a future right is vested, is triggered, or emerges based on the occurrence of an event, satisfaction of a condition, or the like; a blockchain used to make a contingent market in any form of event or access token by securely storing access rights on a distributed ledger; setting and monitoring pricing for contingent access rights, underlying access rights, tokens, fees and the like; optimizing offerings, timing, pricing, or the like, to recognize and predict patterns, to establish rules and contingencies; exchanging contingent access rights or underlying access rights or tokens access tokens and/or contingent access tokens; creating a contingent forward market event access right token where a token may be created and stored on a blockchain for contingent access right that could result in the ownership of a ticket; discovery and delivery of contingent access rights to future events; contingencies that influence or represent future demand for an offering, such as including a set of products, services, or the like; pre-defined contingencies; optimized offerings, timing, pricing, or the like, to recognize and predict patterns, to establish rules and contingencies; creation of a contingent future offering within the dashboard; contingent access rights that may result in the ownership of the virtual good or each smart contract to purchase the virtual good if and when it becomes available under defined conditions; and the like.")
Claim(s) 5 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
classify the one or more input parameters into a problem type associated with the problem using the one or more tools and the one or more techniques. (Cella: Paragraph 277, "The terms classify, classifying, classification, categorization, categorizing, categorize ( and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, classifying a condition or item may include actions to sort the condition or item into a group or category based on some aspect, attribute, or characteristic of the condition or item where the condition or item is common or similar for all the items placed in that classification, despite divergent classifications or categories based on other aspects or conditions at the time. Classification may include recognition of one or more parameters, features, characteristics, or phenomena associated with a condition or parameter of an item, entity, person, process, item, financial construct, or the like. Conditions classified by a condition classifying system may include a default condition, a foreclosure condition, a condition indicating violation of a covenant, a financial risk condition, a behavioral risk condition, a contractual performance condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition, and/or an entity health condition. A classification model may automatically classify or categorize items, entities, process, items, financial constructs or the like based on data received from a variety of sources. The classification model may classify items based on a single attribute or a combination of attributes, and/or may utilize data regarding the items to be classified and a model. The classification model may classify individual items, entities, financial constructs or groups of the same. A bond may be classified based on the type of bond ( ( e.g. municipal bonds, corporate bonds, performance bonds, and the like), rate of return, bond rating (3.sup.rd party indicator of bond quality with respect to bond issuer's financial strength, and/or ability to bap bond's principal and interest, and the like. Lenders or bond issuers may be classified based on the type of lender or issuer, permitted attributes (e.g. based on income, wealth, location (domestic or foreign), various risk factors, status of issuers, and the like. Borrowers may be classified based on permitted attributes (e.g. income, wealth, total assets, location, credit history), risk factors, current status (e.g. employed, a student), behaviors of parties (such as behaviors indicating preferences, reliability, and the like), and the like. A condition classifying system may classify a student recipient of a loan based on progress of the student toward a degree, the student's grades or standing in their classes, student's status at the school (matriculated, on probation and the like), the participation of a student in a non-profit activity, a deferment status of the student, and the participation of the student in a public interest activity. Conditions classified by a condition classifying system may include a state of a set of collateral for a loan or a state of an entity relevant to a guarantee for a loan. Conditions classified by a condition classifying system may include a medical condition of a borrower, guarantor, subsidizer or the like. Conditions classified by a condition classifying system may include compliance with at least one of a law, a regulation, or a policy related to a lending transaction or lending institute. Conditions classified by a condition classifying system may include a condition of an issuer for a bond, a condition of a bond, a rating of a loan-related entity, and the like. Conditions classified by a condition classifying system may include an identify of a machine, a component, or an operational mode. Conditions classified by a condition classifying system may include a state or context (such as a state of a machine, a process, a workflow, a marketplace, a storage system, a network, a data collector, or the like). A condition classifying system may classify a process involving a state or context (e.g., a data storage process, a network coding process, a network selection process, a data marketplace process, a power generation process, a manufacturing process, a refining process, a digging process, a boring process, and/or other process described herein. A condition classifying system may classify a set of loan refinancing actions based on a predicted outcome of the set of loan refinancing actions. A condition classifying system may classify a set of loans as candidates for consolidation based on attributes such as identity of a party, an interest rate, a payment balance, payment terms, payment schedule, a type of loan, a type of collateral, a financial condition of party, a payment status, a condition of collateral, a value of collateral, and the like. A condition classifying system may classify the entities involved in a set of factoring loans, bond issuance activities, mortgage loans, and the like. A condition classifying system may classify a set of entities based on projected outcomes from various loan management activities. A condition classifying system may classify a condition of a set of issuers based on information from Internet of Things data collection and monitoring services, a set of parameters associated with an issuer, a set of social network monitoring and analytic services, and the like. A condition classifying system may classify a set of loan collection actions, loan consolidation actions, loan negotiation actions, loan refinancing actions and the like based on a set of projected outcomes for those activities and entities.")
Claim(s) 7 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
wherein the optimized platform is further configured to use the customized solution to optimize a set of routes for a fleet of heterogeneous vehicles for the entity. (Cella: Paragraph 2487, "The input 12702 may be processed by the opportunity mining module 153 to determine whether an artificial intelligence system can be applied to the task or the domain. For example, the attribute input 12702 may include an attribute of a task, domain or problem, such as a negotiating task, a drafting task, a data entry task, an email response task, a data analysis task, a document review task, an equipment operation task, a forecasting task, an NLP task, an image recognition task, a pattern recognition task, a motion detection task, a route optimization task, and the like. The opportunity mining module 153 may determine if one or more attributes of the task are similar to other tasks that have been automated or to which an intelligence has been applied, or based on the attribute of the task, if the task is potentially automatable or suitable to have an intelligence applied to it regardless of whether it has been done previously. For example, attributes of a drafting task may include articulating a first idea, articulating a second idea, articulating a plurality of ideas, combining the plurality of ideas in a pairwise fashion, and combining the ideas in a triplicate fashion. Articulating ideas may not be suitable for automation, but the task of combining ideas pairwise or in triplicate form may be suitable for automation or to have an intelligence applied to the task."; Paragraph 2764, "Additionally or alternatively, the navigational data may be collected or estimated using guidance data of the worker. The guidance data may include, for example, a calculated route provided to a device of the worker (e.g., a mobile device or the wearable wherein the optimized platform is further configured to use the customized solution to optimize a set of routes for a fleet of heterogeneous vehicles for the entity (120). (Cella: Paragraph 2487, "The input 12702 may be processed by the opportunity mining module 153 to determine whether an artificial intelligence system can be applied to the task or the domain. For example, the attribute input 12702 may include an attribute of a task, domain or problem, such as a negotiating task, a drafting task, a data entry task, an email response task, a data analysis task, a document review task, an equipment operation task, a forecasting task, an NLP task, an image recognition task, a pattern recognition task, a motion detection task, a route optimization task, and the like. The opportunity mining module 153 may determine if one or more attributes of the task are similar to other tasks that have been automated or to which an intelligence has been applied, or based on the attribute of the task, if the task is potentially automatable or suitable to have an intelligence applied to it regardless of whether it has been done previously. For example, attributes of a drafting task may include articulating a first idea, articulating a second idea, articulating a plurality of ideas, combining the plurality of ideas in a pairwise fashion, and combining the ideas in a triplicate fashion. Articulating ideas may not be suitable for automation, but the task of combining ideas pairwise or in triplicate form may be suitable for automation or to have an intelligence applied to the task."; Paragraph 2764, "Additionally or alternatively, the navigational data may be collected or estimated using guidance data of the worker. The guidance data may include, for example, a calculated route provided to a device of the worker (e.g., a mobile device or the wearable processors are configured to maintain, via the digital twin datastore, the industrial-environment digital twin to include contemporaneous positions for the set of workers within the industrial environment, monitor movement of each worker in the set of workers via a sensor array, determine, in response to detecting movement of the respective worker, navigational route data for the respective worker, update the industrial-environment digital twin to include indicia of the navigational route data for the respective worker, and move the worker digital twin along a route of the navigational route data."; Paragraph 3059, "In embodiments, the mobile elements include autonomous vehicles and nonautonomous vehicles, and the optimized path reduces interactions of the autonomous vehicles with the non-autonomous vehicles.")
Claim(s) 8 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
wherein the optimized platform is further configured to use the customized solution to allocate resources for the entity. (Cella: Paragraph 293, "The term route (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a route includes any route including, without limitation, a way or course taken in getting from a starting point to a destination, to send or direct along a specified course, and the like. Certain components may not be considered with respect to a route individually, but may be considered a route in an aggregated system-for example a mobile data collector may specify a requirement for a route for collecting data based on an input from a monitoring circuit, but only in receiving that input does the mobile data collector determine what route to take and begin traveling along the route. In certain embodiments, a route may be considered a route for some purposes but not for other purposes-for example possible routes through a road system may be considered differently than specific routes taken through from one location to another location. Additionally, in certain embodiments, otherwise similar looking systems may be differentiated in determining whether such system are specified with respect to a location, and/or which types of locations. For example, routes depicted on a map may indicate possible routes or actual routes taken by individuals. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered with respect to a route herein, while in certain embodiments a given system may not be considered with respect to a route herein. One of skill in the art, having the benefit of the disclosure herein and know ledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is utilizing a route and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation delivery routes; routes taken through a location; heat map showing routes traveled by customers or workers within an environment; determining what resources are deployed to what routes or types of travel; direct route or multi-stop route, such as from the destination of the consumer to a specific location or to wherever an event takes place; a route for a mobile data collector; and the like."; Paragraph 2945, "In embodiments, the cognitive intelligence system is configured to determine resources for the industrial environment identified by the response actions and alter the resources in response thereto.")
Claim(s) 9 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
customized solution to schedule a task for the entity based on the additional constraints through the customized template. (Cella: Paragraph 236, "The term robotic process automation system as utilized herein may be understood broadly to describe a system capable of performing tasks or providing needs for a system of the present disclosure. For example, a robotic process automation system, without limitation, can be configured for: negotiation of a set of terms and conditions for a loan, negotiation of refinancing of a loan, loan collection, consolidating a set of loans, managing a factoring loan, brokering a mortgage loan, training for foreclosure negotiations, configuring a crowdsourcing request based on a set of attributes for a loan, setting a reward, determining a set of domains to which a request will be published, configuring the content of a request, configuring a data collection and monitoring action based on a set of attributes of a loan, determining a set of domains to which the Internet of Things data collection and monitoring services will be applied, and iteratively training and improving based on a set of outcomes. A robotic process automation system may include: a set of data collection and monitoring services, an artificial intelligence system, and another robotic process automation system which is a component of the higher level robotic process automation system. The robotic process automation system may include: at least one of the set of mortgage loan activities and the set of mortgage loan interactions includes activities among marketing activity, identification of a set of prospective borrowers, identification of property, identification of collateral, qualification of borrower, title search, title verification, property assessment, property inspection, property valuation, income verification, borrower demographic analysis, identification of capital providers, determination of available interest rates, determination of available payment terms and conditions, analysis of existing mortgage, comparative analysis of existing and new mortgage terms, completion of application workflow, population of fields of application, preparation of mortgage agreement, completion of schedule to mortgage agreement, negotiation of mortgage terms and conditions with capital provider, negotiation of mortgage terms and conditions with borrower, transfer of title, placement of lien and closing of mortgage agreement. Example and non-limiting robotic process automation systems may include one or more user interfaces, interfaces with circuits and/or controllers throughout the system to provide, request, and/or share data, and/or one or more artificial intelligence circuits configured to iteratively improve one or more operations of the robotic process automation system. One of skill in the art, having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated robotic process automation system, can readily determine the circuits, controllers, and/or devices to include to implement a robotic process automation system performing the selected functions for the contemplated system. While specific examples of robotic process automation systems are described herein for purposes of illustration, any embodiment benefitting from the disclosures herein, and any considerations understood."; Paragraph 240, "The term loan-related action, events, and activities, as noted herein, may also more specifically be utilized to describe a context for a payment schedule or alternative payment schedule. Typically in transactions involving loans, without limitation, a loan is repaid on a payment schedule, which may be modified over time. Or, such a payment schedule may be developed and agreed in the alternative, with an alternative payment schedule. Various actions may be taken in the context of a payment schedule or alternate payment schedule for the lender or the borrower, such as: the amount of such payments, when such payments are due, what penalties or fees may attach to late payments, or other terms. For example, if a borrower makes an early payment on the loan, a loan-related action for payment schedule and alternative payment schedule of the loan may occur; in such case, perhaps the payment is applied as principal, with the regular payment still being due. Without limitation, loan-related actions for a payment schedule and alternative payment schedule may comprise several actions that may occur with respect to the payment on the loan, such as: the payment being tendered to the lender, the loan ledger or accounting reflecting that a payment has been made, a receipt provided to the borrower of the payment made, a calculation if any fees are attached or due, and the next payment being requested of the borrower. In certain embodiments, an activity to determine a payment schedule or alternative payment schedule may be a loan-related action, event, or activity. In certain embodiments, an activity to communicate the payment schedule or alternative payment schedule (e.g., to the borrower, the lender, or a 3.sup.rd party) may be a loan-related action, event, or activity. In some circumstances a smart contract circuit or robotic process automation system may initiate, administrate, or process such loan-related actions for payment schedule and alternative payment schedule, which without limitation, may include providing notice to the lender, researching and collecting payment history, providing a receipt to the borrower, calculating the next due date, calculating the final payment amount and date, providing notice of the next payment due to the borrower, determining the payment schedule or an alternate payment schedule, communicating the payment scheduler or an alternate payment schedule, or other actions associated with payment of the loan. One of skill in the art, having the benefit of the disclosure herein and knowledge about loan-related actions for payment schedule and alternative payment schedule, or other forms of the term and its various forms, can readily determine the purposes and use of this term in the context of an event or other various embodiments and contexts disclosed herein.")
Claim(s) 13 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
enable the one or more users to access the predefined template and generate the customized solution, through an API; and (Cella: Paragraph 339, "In embodiments, criteria may include model deployment considerations such as requirements for model updates (e.g. frequency and requirement for retirement of models), management of historic models and maintaining historical decision engine, potential for distributed decision making capabilities, model curation rules (e.g. how long a model or input data are considered valid for training), and the like."; Paragraph 478, "The platform 500 may include a crowdsourcing interface 520, which may be included in or provided in coordination with a website, application, dashboard, communications system (such as for sending emails, texts, voice messages, advertisements, broadcast messages, or other message), by which a message may be presented in the crowdsourcing interface 520 or sent to relevant individuals (whether targeted, such as in the case of a request to a particular individual, or broadcast, such as to individuals in a given location, company, organization, or the like) with an appropriate link to the smart contract and associated blockchain 136, such that a reply message submitting information 518, with relevant attachments, links, or other information, can be automatically associated (such as via an API 112 or data integration system) with the blockchain 136, such that the blockchain 136, and any optionally associated distributed ledger, maintains a secure, definitive record of information 518 submitted in enable the one or more users (102) to access the predefined template and generate the customized solution, through an API; and (Cella: Paragraph 339, "In embodiments, criteria may include model deployment considerations such as requirements for model updates (e.g. frequency and requirement for retirement of models), management of historic models and maintaining historical decision engine, potential for distributed decision making capabilities, model curation rules (e.g. how long a model or input data are considered valid for training), and the like."; Paragraph 478, "The platform 500 may include a crowdsourcing interface 520, which may be included in or provided in coordination with a website, application, dashboard, communications system (such as for sending emails, texts, voice messages, advertisements, broadcast messages, or other message), by which a message may be presented in the crowdsourcing interface 520 or sent to relevant individuals (whether targeted, such as in the case of a request to a particular individual, or broadcast, such as to individuals in a given location, company, organization, or the like) with an appropriate link to the smart contract and associated blockchain 136, such that a reply message submitting information 518, with relevant attachments, links, or other information, can be automatically associated (such as via an API 112 or data integration system) with the blockchain 136, such that the blockchain 136, and any optionally associated distributed ledger, maintains a secure, definitive record of information 518 submitted in other consideration, via distribution of rewards 512 through the smart contract, blockchain 136 and any distributed ledger. Thus, the platform 500 may be used for a wide variety of fact-gathering and information-gathering purposes, to facilitate validation of collateral, to validate representations about behavior, to validate occurrence of conditions of compliance, to validate occurrence of conditions of default, to deter improper behavior or misrepresentations, to reduce uncertainty, to reduce asymmetries of information, or the like."; Paragraph 1275, "The corresponding API components of the circuits may further include user interfaces structured to interact with a plurality of users of the system."; Paragraph 1554, "In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities of a transactional, financial and marketplace enablement. An example platform or system may include a blockchain service circuit structured to interpret a plurality of access control features corresponding to a plurality of parties associated with a loan; a data collection circuit structured to interpret entity information corresponding to a plurality of entities related to a lending transaction corresponding to the loan; a smart contract circuit structured to specify loan terms and conditions relating to the loan; and a loan management circuit structured to: interpret loan related events in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan related events are associated with the loan; implement loan related activities in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan related activities are associated with the loan; and wherein each of the blockchain service circuit, the data collection circuit, the smart contract circuit, and the loan management circuit further comprise a corresponding application programming interface (API) component structured to facilitate communication among the circuits of the system."; Paragraph 2350, "An example system may include wherein each of the asset identification service circuit, identity management service circuit, blockchain service circuit, and the financial management circuit further comprise a corresponding application programming interface (API) component structured to facilitate communication among the circuits of the system. The corresponding API components of the circuits further include user interfaces structured to interact with a plurality of users of the system.")
enable the one or more users to access the customized template and generate a modified customized solution, through the APL (Cella: Paragraph 339, "In embodiments, criteria may include model deployment considerations such as requirements for model updates (e.g. frequency and requirement for retirement of models), management of historic models and maintaining historical decision engine, potential for distributed decision making capabilities, model curation rules (e.g. how long a model or input data are considered valid for training), and the like."; Paragraph 478, "The platform 500 may include a crowdsourcing interface 520, which may be included in or provided in coordination with a website, application, dashboard, communications system (such as for sending emails, texts, voice messages, advertisements, broadcast messages, or other message), by which a message may be presented in the crowdsourcing interface 520 or sent to relevant individuals (whether targeted, such as in the case of a request to a particular individual, or broadcast, such as to individuals in a given location, company, organization, or the like) with an appropriate link to the smart contract and associated blockchain 136, such that a reply message submitting information 518, with relevant attachments, links, or other information, can be automatically associated (such as via an API 112 or data integration system) with the blockchain 136, such that the blockchain 136, and any optionally associated distributed ledger, maintains a secure, definitive record of information 518 submitted in response to the request. Where a reward 512 is offered, the blockchain 136 and/or smart contract may be used to record time of submission, the nature of the submission, and the party submitting, such that at such time as a submission satisfies the conditions for a reward 512 (such as, for example, upon completion of a loan transaction in which the information 518 was useful), the blockchain 136 and any distributed ledger stored thereby can be used to identify the submitter and, by execution of the smart contract, convey the reward 512 ( which may take any of the forms of consideration noted throughout this disclosure. In embodiments, the blockchain 136 and any associated ledger may include identifying information for submissions of information 518 without containing actual information 518, such that information may be maintained secret (such as being encrypted or being stored separately with only identifying information), subject to satisfying or verifying conditions for access (such as identification or verification of a person who has legitimate access rights, such as by an identity or security application 148). Rewards 512 may be provided based on outcomes of cases or situations to which information 518 relates, based on a set of rules (which may be automatically applied in some cases, such as using a smart contract in concert with an automation system, a rule processing system, an artificial intelligence system 156 or other expert system, which in embodiments may comprise one that is trained on a training data set created with human experts. For example, a machine vision system may be used to evaluate evidence of the existence and/or condition of collateral based on images of items, and parties submitting information about collateral may be rewarded, such as via tokens or other consideration, via distribution of rewards 512 through the smart contract, blockchain 136 and any distributed ledger. Thus, the platform 500 may be used for a wide variety of fact-gathering and information-gathering purposes, to facilitate validation of collateral, to validate representations about behavior, to validate occurrence of conditions of compliance, to validate occurrence of conditions of default, to deter improper behavior or misrepresentations, to reduce uncertainty, to reduce asymmetries of information, or the like."; Paragraph 1275, "The corresponding API components of the circuits may further include user interfaces structured to interact with a plurality of users of the system."; Paragraph 1554, "In embodiments, provided herein is a system for adaptive intelligence and robotic process automation capabilities of a transactional, financial and marketplace enablement. An example platform or system may include a blockchain service circuit structured to interpret a plurality of access control features corresponding to a plurality of parties associated with a loan; a data collection circuit structured to interpret entity information corresponding to a plurality of entities related to a lending transaction corresponding to the loan; a smart contract circuit structured to specify loan terms and conditions relating to the loan; and a loan management circuit structured to: interpret loan related events in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan related events are associated with the loan; implement loan related activities in response to the entity information, the plurality of access control features, and the loan terms and conditions, wherein the loan related activities are associated with the loan; and wherein each of the blockchain service circuit, the data collection circuit, the smart contract circuit, and the loan management circuit further comprise a corresponding application programming interface (API) component structured to facilitate communication among the circuits of the system."; Paragraph 2350, "An example system may include wherein each of the asset identification service circuit, identity management service circuit, blockchain service circuit, and the financial management circuit further comprise a corresponding application programming interface (API) component structured to facilitate communication among the circuits of the system. The corresponding API components of the circuits further include user interfaces structured to interact with a plurality of users of the system.")
Claim(s) 14 –
Cella discloses the limitations of claim 1
Cella further discloses the following:
record the one or more input parameters; (Cella: Paragraph 214, "The term sensor as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a sensor may be a device, module, machine, or subsystem that detects or measures a physical quality, event, or change. In embodiments, may record, indicate, transmit, or otherwise respond to the detection or measurement. Examples of sensors may be sensors for sensing movement of entities, for sensing temperatures, pressures or other attributes about entities or their environments, cameras that capture still or video images of entities, sensors that collect data about collateral or assets, such as, for example, regarding the location, condition (health, physical, or otherwise), quality, security, possession, or the like. In embodiments, sensors may be sensitive to, but not influential on, the property to be measured but insensitive to other properties. Sensors may be analog or digital. Sensors may include processors, transmitters, transceivers, memory, power, sensing circuit, electrochemical fluid reservoirs, light sources, and the like. Further examples of sensors contemplated for use in the system include biosensors, chemical sensors, black silicon sensor, IR sensor, acoustic sensor, induction sensor, motion sensor, optical sensor, opacity sensor, proximity sensor, inductive sensor, Eddy-current sensor, passive infrared proximity sensor, radar, capacitance sensor, capacitive displacement sensor, halleffect sensor, magnetic sensor, GPS sensor, thermal imaging sensor, thermocouple, thermistor, photoelectric sensor, ultrasonic sensor, infrared laser sensor, inertial motion sensor, MEMS internal motion sensor, ultrasonic 3D motion sensor, accelerometer, inclinometer, force sensor, piezoelectric sensor, rotary encoders, linear encoders, ozone sensor, smoke sensor, heat sensor, magnetometer, carbon dioxide detector, carbon monoxide detector, oxygen sensor, glucose sensor, smoke detector, metal detector, rain sensor, altimeter, GPS, detection of being outside, detection of context, detection of activity, object detector (e.g. collateral), marker detector (e.g. geo-location marker), laser rangefinder, sonar, capacitance, optical response, heart rate sensor, or an RF/micropower impulse radio (MIR) sensor. In certain embodiments, a sensor may be a virtual sensor-for example determining a parameter of interest as a calculation based on other sensed parameters in the system. In certain embodiments, a sensor may be a smart sensor-for example reporting a sensed value as an abstracted communication (e.g., as a network communication) of the sensed value. In certain embodiments, a sensor may provide a sensed value directly (e.g., as a voltage level, frequency parameter, etc.) to a circuit, controller, or other device in the system. One of skill in the art, having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit from a sensor. Certain considerations for the person of skill in the art, in determining whether a contemplated device is a sensor and/or whether aspects of the present disclosure can benefit from or be enhanced by the contemplated sensor include, without limitation: the conditioning of an activation/deactivation of a system to an environmental quality; the conversion of electrical output into measured quantities; the ability to enforce a geofence; the automatic modification of a loan in response to change in collateral; and the like. While specific examples of sensors and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.")
enforce validation, authentication, rate limiting and logging metrics for the one or more input parameters; (Cella: Paragraph 211, "The term data collection services (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a data collection service includes any service that collects data or information, including any circuit, controller, device, or application that may store, transmit, transfer, share, process, organize, compare, report on and/or aggregate data. The data collection service may include data collection devices (e.g., sensors) and/or may be in communication with data collection devices. The data collection service may monitor entities, such as to identify data or information for collection. The data collection service may be event-driven, run on a periodic basis, or retrieve data from an application at particular points in the application's execution. Certain processes may not be considered to be a data collection service individually, but may be considered a data collection service in an aggregated system-for example, a networked storage device may be a component of a data collection service in one instance, but in another instance, may have stand-alone functionality. Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes systems, and any such processes or systems may be considered a data collection service herein, while in certain embodiments a given service may not be considered a data collection service herein. One of skill in the art, having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system and how to combine processes and systems from the present disclosure implement a data collection service and/or to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is a data collection service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: ability to modify a business rule on the fly and alter a data collection protocol; perform real-time monitoring of events; connection of a device for data collection to a monitoring infrastructure, execution of computer readable instructions that cause a processor to log or track events; use of an automated inspection system; occurrence of sales at a networked point-of-sale; need for data from one or more distributed sensors or cameras; and the like. While specific examples of data collection services and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure."; Paragraph 282, "Without limitation to any other aspect or description of the present disclosure, validation includes any validating system including, without limitation, validating title for collateral or security for a loan, validating conditions of collateral for security or a loan, validating conditions of a guarantee for a loan, and the like. For instance, a validation service may provide lenders a mechanism to deliver loans with more certainty, such as through validating loan or security information components (e.g., income, employment, title, conditions for a loan, conditions of collateral, and conditions of an asset). In a non-limiting example, a validation service circuit may be structured to validate a plurality of loan information components with respect to a financial entity configured to determine a loan condition for an asset. Certain components may not be considered a validating system individually, but may be considered validating in an aggregated system-for example, an Internet of Things component may not be considered a validating component on its own, however an Internet of Things component utilized for asset data collection and monitoring may be considered a validating component when applied to validating a reliability parameter of a personal guarantee for a load when the Internet of Things component is associated with a collateralized asset. In certain embodiments, otherwise similar looking systems may be differentiated in determining whether such systems are for validation. For example, a blockchain-based ledger may be used to validate identities in one instance and to maintain confidential information in another instance. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a system for validation herein, while in certain embodiments a given system may not be considered a validating system herein. One of skill in the art, having the benefit of the disclosure herein and know ledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is a validating system and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: a lending platform having a social network monitoring system for validating the reliability of a guarantee for a loan; a lending platform having an Internet of Things data collection and monitoring system for validating reliability of a guarantee for a loan; a lending platform having a crowdsourcing and automated classification system for validating conditions of an issuer for a bond; a crowdsourcing system for validating quality, title, or other conditions of collateral for a loan; a biometric identify validation application such as utilizing DNA or fingerprints; IoT devices utilized to collectively validate location and identity of a fixed asset that is tagged by a virtual asset tag; validation systems utilizing voting or consensus protocols; artificial intelligence systems trained to recognize and validate events; validating information such as title records, video footage, photographs, or witnessed statements; validation representations related to behavior, such as to validate occurrence of conditions of compliance, to validate occurrence of conditions of default, to deter improper behavior or misrepresentations, to reduce uncertainty, or to reduce asymmetries of information; and the like.")
bifurcate the one or more input parameters and assign custom data structures to the one or more input parameters based on the problem type; (Cella: Paragraph 301, "Information that is a part of a loan or agreement may be separated from information presented in an access location. The term more specifically may relate to the characterization that information can be apportioned, split, restricted, or otherwise separated from other information within the context of a loan or agreement. Thus, information presented or received on an access location may not necessarily be the whole information available for a given context. For example, information provided to a borrower may be different information received by a lender from an external source, and may be different than information received or presented from an access location. In certain embodiments, a smart contract circuit or robotic process automation system may perform separation of information or other tasks for one or more of the parties and process appropriate tasks. One of skill in the art, having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the purposes and use of this term in various forms, embodiments and contexts disclosed herein.")
and create objects from the custom data structures to be integrated with the one or more tools to generate the customized solution. (Cella: Paragraph 223, "The term entity as utilized herein may be understood broadly to describe a party, a thirdparty (e.g., an auditor, regulator, service provider, etc.), and/or an identifiable related object such as an item of collateral related to a transaction. Example entities include an individual, partnership, corporation, limited liability company or other legal organization. Other example entities include an identifiable item of collateral, offset collateral, potential collateral, or the like. For example, an entity may be a given party, such as an individual, to an agreement or loan. Data or other terms herein may be characterized as having a context relating to an entity, such as entity-oriented data. An entity may be characterized with a specific context or application, such as a human entity, physical entity, transactional entity or a financial entity, without limitation. An entity may have representatives that represent or act on its behalf. Without limitation to any other aspect or description of the present disclosure, an entity may also be used in conjunction with other related entities or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default. An entity may have a set of attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition, a foreclosure status, a contractual default status, a regulatory violation status, a criminal status, an export controls status, an embargo status, a tariff status, a tax status, a credit report, a credit rating, a website rating, a set of customer reviews for a product of related object such as an item of collateral related to a transaction. Example entities include an individual, partnership, corporation, limited liability company or other legal organization. Other example entities include an identifiable item of collateral, offset collateral, potential collateral, or the like. For example, an entity may be a given party, such as an individual, to an agreement or loan. Data or other terms herein may be characterized as having a context relating to an entity, such as entity-oriented data. An entity may be characterized with a specific context or application, such as a human entity, physical entity, transactional entity or a financial entity, without limitation. An entity may have representatives that represent or act on its behalf. Without limitation to any other aspect or description of the present disclosure, an entity may also be used in conjunction with other related entities or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default. An entity may have a set of attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition, a foreclosure status, a contractual default status, a regulatory violation status, a criminal status, an export controls status, an embargo status, a tariff status, a tax status, a credit report, a credit rating, a website rating, a set of customer reviews for a product of an entity, a social network rating, a set of credentials, a set of referrals, a set of testimonials, a set of behavior, a location, and a geolocation, without limitation. In certain embodiments, a smart contract may calculate whether an entity has satisfied conditions or covenants and in cases where the entity has not satisfied such conditions or covenants, may enable automated action or trigger other conditions or terms. One of skill in the art, having the benefit of the disclosure herein and knowledge about entities, can readily determine the purposes and use of entities in various embodiments and contexts disclosed herein."; Paragraph 2503, "A digital twin may refer to a digital representation of one or more industrial entities, such as an industrial environment 12920, a physical object 12922, a device 12924, a sensor 12926, a human 12928, or any combination thereof. Examples of industrial environments 12920 include, but are not limited to, a factory, a power plant, a food production facility (which may include an inspection facility), a commercial kitchen, an indoor growing facility, a natural resources excavation site (e.g., a mine, an oil field, etc.), and the like. Depending on the type of environment, the types of objects, devices, and sensors that are found in the environments will differ. Non-limiting examples of physical objects 12922 include raw materials, manufactured products, excavated materials, containers (e.g., boxes, dumpsters, cooling towers, vats, pallets, barrels, palates, bins, and the like), furniture (e.g., tables, counters, workstations, shelving, etc.), and the like. Non-limiting examples of devices 12924 include robots, computers, vehicles (e.g., cars, trucks, tankers, trains, forklifts, cranes, etc.), machinery/equipment (e.g., tractors, tillers, drills, presses, assembly lines, conveyor belts, etc.), and the like. The sensors 12926 may be any sensor devices and/or sensor aggregation devices that are found in a sensor system 12930 within an environment. Non-limiting examples of sensors 12926 that may be implemented in a sensor system 12930 may include temperature sensors 12932, humidity sensors 12934, vibration sensors 12936, LIDAR sensors 12938, motion sensors 12940, chemical sensors 12942, audio sensors 12944, pressure sensors 12946, weight sensors 12948, radiation sensors 12950, video sensors 12952, wearable devices 12954, relays 12956, edge devices 12958, crosspoint switches 12960, and/or any other suitable sensors. Examples of different types of physical objects 12922, devices 12924, sensors 12926, and environments 12920 are referenced throughout the disclosure.")
Claim(s) 16 and 23 –
Cella discloses the method of claims 1 and 18
Cella further discloses the following:
wherein the entity includes any or a combination of goods transport, retail offline, online organization, telecom, hydrocarbons retail, manufacturing and polymer industry, and healthcare. (Cella: Paragraph 183, "The term marketplace information, market value and similar terms as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, marketplace information and market value describes a status or value of an asset, collateral, food, or service at a defined point or period in time. Market value may refer to the expected value placed on an item in a marketplace or auction setting, or pricing or financial data for items that are similar to the item, asset, or collateral in at least one public marketplace. For a company, market value may be the number of its outstanding shares multiplied by the current share price. Valuation services may include market value data collection services that monitor and report on marketplace information relevant to the value (e.g. market value) of collateral, the issuer, a set of bonds, and a set of assets. a set of subsidized loans, a party, and the like. Market values may be dynamic in nature because they depend on an assortment of factors, from physical operating conditions to economic climate to the dynamics of demand and supply. Market value may be affected by, and marketplace information may include, proximity to other assets, inventory or supply of assets, demand for assets, origin of items, history of items, underlying current value of item components, a bankruptcy condition of an entity, a foreclosure status of an entity, a contractual default status of an entity, a regulatory violation status of an entity, a criminal status of an entity, an export controls status of an entity, an embargo status of an entity, a tariff status of an entity, a tax status of an entity, a credit report of an entity, a credit rating of an entity, a website rating of an entity, a set of customer reviews for a product of an entity, a social network rating of an entity, a set of credentials of an entity, a set of referrals of an entity, a set of testimonials for an entity, a set of behavior of an entity, a location of an entity, and a geolocation of an entity. In certain embodiments, a market value may include information such as a volatility of a value, a sensitivity of a value (e.g., relative to other parameters having an uncertainty associated therewith), and/or a specific value of the valuated object to a particular party (e.g., an object may have more value as possessed by a first party than as possessed by a second party)."; Paragraph 233, "The term entity as utilized herein may be understood broadly to describe a party, a third-party (e.g., an auditor, regulator, service provider, etc.), and/or an identifiable related object such as an item of collateral related to a transaction. Example entities include an individual, partnership, corporation, limited liability company or other legal organization. Other example entities include an identifiable item of collateral, offset collateral, potential collateral, or the like. For example, an entity may be a given party, such as an individual, to an agreement or loan. Data or other terms herein may be characterized as having a context relating to an entity, such as entity-oriented data. An entity may be characterized with a specific context or application, such as a human entity, physical entity, transactional entity or a financial entity, without limitation. An entity may have representatives that represent or act on its behalf. Without limitation to any other aspect or description of the present disclosure, an entity may also be used in conjunction with other related entities or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default. An entity may have a set of attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of a particular party (e.g., an object may have more value as possessed by a first party than as possessed by a second party)."; Paragraph 233, "The term entity as utilized herein may be understood broadly to describe a party, a third-party (e.g., an auditor, regulator, service provider, etc.), and/or an identifiable related object such as an item of collateral related to a transaction. Example entities include an individual, partnership, corporation, limited liability company or other legal organization. Other example entities include an identifiable item of collateral, offset collateral, potential collateral, or the like. For example, an entity may be a given party, such as an individual, to an agreement or loan. Data or other terms herein may be characterized as having a context relating to an entity, such as entity-oriented data. An entity may be characterized with a specific context or application, such as a human entity, physical entity, transactional entity or a financial entity, without limitation. An entity may have representatives that represent or act on its behalf. Without limitation to any other aspect or description of the present disclosure, an entity may also be used in conjunction with other related entities or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default. An entity may have a set of attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of data may include behavior of parties to the loan, financial condition of parties, adherence to a parties to a term or condition of the loan, or bond, or the like. Parties to the loan may include issuers of a bond, related entities, lender, borrower, 3rd parties with an interest in the debt. Condition management may be discussed in connection with smart contract services which may include condition classification, data collection and monitoring, and bond, loan and debt transaction management. Data collection and monitoring services are also discussed in conjunction with classification and classification models which are related when classifying an issuer of a bond issuer, an asset or collateral asset related to the bond, collateral assets backing the bond, parties to the bond, and sets of the same. In some embodiments a classification model may be included when discussing bond types. Specific steps, factors or refinements may be considered a part of a classification model. In various embodiments, the classification model may change both in an embodiment, or in the same embodiment which is tied to a specific jurisdiction. Different classification models may use different data sets (e.g. based on the issuer, the borrower, the collateral assets, the bond type, the loan type, and the like) and multiple classification models may be used in a single classification. For example, one type of bond, such as a municipal bond, may allow a classification model that is based on bond data from municipalities of similar size and economic prosperity, whereas another classification model may emphasize data from IoT sensors associated with a collateral asset. Accordingly, different classification models will offer benefits or risks over other classification models, depending upon the embodiment and the specifics of the bond, loan or data may include behavior of parties to the loan, financial condition of parties, adherence to a parties to a term or condition of the loan, or bond, or the like. Parties to the loan may include issuers of a bond, related entities, lender, borrower, 3rd parties with an interest in the debt. Condition management may be discussed in connection with smart contract services which may include condition classification, data collection and monitoring, and bond, loan and debt transaction management. Data collection and monitoring services are also discussed in conjunction with classification and classification models which are related when classifying an issuer of a bond issuer, an asset or collateral asset related to the bond, collateral assets backing the bond, parties to the bond, and sets of the same. In some embodiments a classification model may be included when discussing bond types. Specific steps, factors or refinements may be considered a part of a classification model. In various embodiments, the classification model may change both in an embodiment, or in the same embodiment which is tied to a specific jurisdiction. Different classification models may use different data sets (e.g. based on the issuer, the borrower, the collateral assets, the bond type, the loan type, and the like) and multiple classification models may be used in a single classification. For example, one type of bond, such as a municipal bond, may allow a classification model that is based on bond data from municipalities of similar size and economic prosperity, whereas another classification model may emphasize data from IoT sensors associated with a collateral asset. Accordingly, different classification models will offer benefits or risks over other classification models, depending upon the embodiment and the specifics of the bond, loan or bond type where bond type may include a municipal bond, a government bond, a treasury bond, an asset-backed bond, and a corporate bond. Conditions classified may include a default condition, a foreclosure condition, a condition indicating violation of a covenant, a financial risk condition, a behavioral risk condition, a policy risk condition, a financial health condition, a physical defect condition, a physical health condition, an entity risk condition and an entity health condition. Conditions classified may include an environment where environment may include an environment selected from among a municipal environment, a corporate environment, a securities trading environment, a real property environment, a commercial facility, a warehousing facility, a transportation environment, a manufacturing environment, a storage environment, a home, and a vehicle. Actions based on the condition of an asset, issuer, borrower, loan, debt, bond, regulatory status and the like, may include managing, reporting on, syndicating, consolidating, or otherwise handling a set of bonds (such as municipal bonds, corporate bonds, performance bonds, and others), a set of loans (subsidized and unsubsidized, debt transactions and the like, monitoring, classifying, predicting, or otherwise handling the reliability, quality, status, health condition, financial condition, physical condition or other information about a guarantee, a guarantor, a set of collateral supporting a guarantee, a set of assets backing a guarantee, or the like. Bond transaction activities in response to a condition of the bond may include offering a debt transaction, underwriting a debt transaction, setting an interest rate, deferring a payment requirement, modifying an interest rate, validating title, managing inspection, recording a change in title, assessing the value of an asset, calling a loan, closing a transaction, setting terms and conditions for a transaction, providing notices required to be provided, foreclosing on a set of assets, modifying terms and conditions, setting a rating for an entity, syndicating debt, and/or consolidating debt.")
Claim(s) 19 –
Cella discloses the limitations of claims 18
Cella further discloses the following:
record the one or more input parameters; (Cella: Paragraph 214, "The term sensor as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a sensor may be a device, module, machine, or subsystem that detects or measures a physical quality, event, or change. In embodiments, may record, indicate, transmit, or otherwise respond to the detection or measurement. Examples of sensors may be sensors for sensing movement of entities, for sensing temperatures, pressures or other attributes about entities or their environments, cameras that capture still or video images of entities, sensors that collect data about collateral or assets, such as, for example, regarding the location, condition (health, physical, or otherwise), quality, security, possession, or the like. In embodiments, sensors may be sensitive to, but not influential on, the property to be measured but insensitive to other properties. Sensors may be analog or digital. Sensors may include processors, transmitters, transceivers, memory, power, sensing circuit, electrochemical fluid reservoirs, light sources, and the like. Further examples of sensors contemplated for use in the system include biosensors, chemical sensors, black silicon sensor, IR sensor, acoustic sensor, induction sensor, motion sensor, optical sensor, opacity sensor, proximity sensor, inductive sensor, Eddy-current sensor, passive infrared proximity sensor, radar, capacitance sensor, capacitive displacement sensor, halleffect sensor, magnetic sensor, GPS sensor, thermal imaging sensor, thermocouple, thermistor, photoelectric sensor, ultrasonic sensor, infrared laser sensor, inertial motion sensor, MEMS internal motion sensor, ultrasonic 3D motion sensor, accelerometer, inclinometer, force sensor, piezoelectric sensor, rotary encoders, linear encoders, ozone sensor, smoke sensor, heat sensor, magnetometer, carbon dioxide detector, carbon monoxide detector, oxygen sensor, glucose sensor, smoke detector, metal detector, rain sensor, altimeter, GPS, detection of being outside, detection of context, detection of activity, object detector (e.g. collateral), marker detector (e.g. geo-location marker), laser rangefinder, sonar, capacitance, optical response, heart rate sensor, or an RF/micropower impulse radio (MIR) sensor. In certain embodiments, a sensor may be a virtual sensor-for example determining a parameter of interest as a calculation based on other sensed parameters in the system. In certain embodiments, a sensor may be a smart sensor-for example reporting a sensed value as an abstracted communication (e.g., as a network communication) of the sensed value. In certain embodiments, a sensor may provide a sensed value directly (e.g., as a voltage level, frequency parameter, etc.) to a circuit, controller, or other device in the system. One of skill in the art, having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit from a sensor. Certain considerations for the person of skill in the art, in determining whether a contemplated device is a sensor and/or whether aspects of the present disclosure can benefit from or be enhanced by the contemplated sensor include, without limitation: the conditioning of an activation/deactivation of a system to an environmental quality; the conversion of electrical output into measured quantities; the ability to enforce a geofence; the automatic modification of a loan in response to change in collateral; and the like. While specific examples of sensors and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure.")
enforce validation, authentication, rate limiting and logging metrics for the one or more input parameters; (Cella: Paragraph 211, "The term data collection services (and similar terms) as utilized herein should be understood broadly. Without limitation to any other aspect or description of the present disclosure, a data collection service includes any service that collects data or information, including any circuit, controller, device, or application that may store, transmit, transfer, share, process, organize, compare, report on and/or aggregate data. The data collection service may include data collection devices (e.g., sensors) and/or may be in communication with data collection devices. The data collection service may monitor entities, such as to identify data or information for collection. The data collection service may be event-driven, run on a periodic basis, or retrieve data from an application at particular points in the application's execution. Certain processes may not be considered to be a data collection service individually, but may be considered a data collection service in an aggregated system-for example, a networked storage device may be a component of a data collection service in one instance, but in another instance, may have stand-alone functionality. Accordingly, the benefits of the present disclosure may be applied in a wide variety of processes systems, and any such processes or systems may be considered a data collection service herein, while in certain embodiments a given service may not be considered a data collection service herein. One of skill in the art, having the benefit of the disclosure herein and knowledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system and how to combine processes and systems from the present disclosure implement a data collection service and/or to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is a data collection service and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: ability to modify a business rule on the fly and alter a data collection protocol; perform real-time monitoring of events; connection of a device for data collection to a monitoring infrastructure, execution of computer readable instructions that cause a processor to log or track events; use of an automated inspection system; occurrence of sales at a networked point-of-sale; need for data from one or more distributed sensors or cameras; and the like. While specific examples of data collection services and considerations are described herein for purposes of illustration, any system benefitting from the disclosures herein, and any considerations understood to one of skill in the art having the benefit of the disclosures herein, are specifically contemplated within the scope of the present disclosure."; Paragraph 282, "Without limitation to any other aspect or description of the present disclosure, validation includes any validating system including, without limitation, validating title for collateral or security for a loan, validating conditions of collateral for security or a loan, validating conditions of a guarantee for a loan, and the like. For instance, a validation service may provide lenders a mechanism to deliver loans with more certainty, such as through validating loan or security information components (e.g., income, employment, title, conditions for a loan, conditions of collateral, and conditions of an asset). In a non-limiting example, a validation service circuit may be structured to validate a plurality of loan information components with respect to a financial entity configured to determine a loan condition for an asset. Certain components may not be considered a validating system individually, but may be considered validating in an aggregated system-for example, an Internet of Things component may not be considered a validating component on its own, however an Internet of Things component utilized for asset data collection and monitoring may be considered a validating component when applied to validating a reliability parameter of a personal guarantee for a load when the Internet of Things component is associated with a collateralized asset. In certain embodiments, otherwise similar looking systems may be differentiated in determining whether such systems are for validation. For example, a blockchain-based ledger may be used to validate identities in one instance and to maintain confidential information in another instance. Accordingly, the benefits of the present disclosure may be applied in a wide variety of systems, and any such systems may be considered a system for validation herein, while in certain embodiments a given system may not be considered a validating system herein. One of skill in the art, having the benefit of the disclosure herein and know ledge about a contemplated system ordinarily available to that person, can readily determine which aspects of the present disclosure will benefit a particular system, and/or how to combine processes and systems from the present disclosure to enhance operations of the contemplated system. Certain considerations for the person of skill in the art, in determining whether a contemplated system is a validating system and/or whether aspects of the present disclosure can benefit or enhance the contemplated system include, without limitation: a lending platform having a social network monitoring system for validating the reliability of a guarantee for a loan; a lending platform having an Internet of Things data collection and monitoring system for validating reliability of a guarantee for a loan; a lending platform having a crowdsourcing and automated classification system for validating conditions of an issuer for a bond; a crowdsourcing system for validating quality, title, or other conditions of collateral for a loan; a biometric identify validation application such as utilizing DNA or fingerprints; IoT devices utilized to collectively validate location and identity of a fixed asset that is tagged by a virtual asset tag; validation systems utilizing voting or consensus protocols; artificial intelligence systems trained to recognize and validate events; validating information such as title records, video footage, photographs, or witnessed statements; validation representations related to behavior, such as to validate occurrence of conditions of compliance, to validate occurrence of conditions of default, to deter improper behavior or misrepresentations, to reduce uncertainty, or to reduce asymmetries of information; and the like.")
bifurcate the one or more input parameters and assign custom data structures to the one or more input parameters based on the problem type; (Cella: Paragraph 301, "Information that is a part of a loan or agreement may be separated from information presented in an access location. The term more specifically may relate to the characterization that information can be apportioned, split, restricted, or otherwise separated from other information within the context of a loan or agreement. Thus, information presented or received on an access location may not necessarily be the whole information available for a given context. For example, information provided to a borrower may be different information received by a lender from an external source, and may be different than information received or presented from an access location. In certain embodiments, a smart contract circuit or robotic process automation system may perform separation of information or other tasks for one or more of the parties and process appropriate tasks. One of skill in the art, having the benefit of the disclosure herein and knowledge ordinarily available about a contemplated system, can readily determine the purposes and use of this term in various forms, embodiments and contexts disclosed herein.")
and create objects from the custom data structures to be integrated with the one or more tools to generate the customized solution. (Cella: Paragraph 223, "The term entity as utilized herein may be understood broadly to describe a party, a thirdparty (e.g., an auditor, regulator, service provider, etc.), and/or an identifiable related object such as an item of collateral related to a transaction. Example entities include an individual, partnership, corporation, limited liability company or other legal organization. Other example entities include an identifiable item of collateral, off set collateral, potential collateral, or the like. For example, an entity may be a given party, such as an individual, to an agreement or loan. Data or other terms herein may be characterized as having a context relating to an entity, such as entity-oriented data. An entity may be characterized with a specific context or application, such as a human entity, physical entity, transactional entity or a financial entity, without limitation. An entity may have representatives that represent or act on its behalf. Without limitation to any other aspect or description of the present disclosure, an entity may also be used in conjunction with other related entities or terms to an agreement or loan, such as a representation, a warranty, an indemnity, a covenant, a balance of debt, a fixed interest rate, a variable interest rate, a payment amount, a payment schedule, a balloon payment schedule, a specification of collateral, a specification of substitutability of collateral, a party, a guarantee, a guarantor, a security, a personal guarantee, a lien, a duration, a foreclose condition, a default condition, and a consequence of default. An entity may have a set of attributes such as: a publicly stated valuation, a set of property owned by the entity as indicated by public records, a valuation of a set of property owned by the entity, a bankruptcy condition, a foreclosure status, a contractual default status, a regulatory violation status, a criminal status, an export controls status, an embargo status, a tariff status, a tax status, a credit report, a credit rating, a website rating, a set of customer reviews for a product of an entity, a social network rating, a set of credentials, a set of referrals, a set of testimonials, a set of behavior, a location, and a geolocation, without limitation. In certain embodiments, a smart contract may calculate whether an entity has satisfied conditions or covenants and in cases where the entity has not satisfied such conditions or covenants, may enable automated action or trigger other conditions or terms. One of skill in the art, having the benefit of the disclosure herein and knowledge about entities, can readily determine the purposes and use of entities in various embodiments and contexts disclosed herein."; Paragraph 2503, "A digital twin may refer to a digital representation of one or more industrial entities, such as an industrial environment 12920, a physical object 12922, a device 12924, a sensor 12926, a human 12928, or any combination thereof. Examples of industrial environments 12920 include, but are not limited to, a factory, a power plant, a food production facility (which may include an inspection facility), a commercial kitchen, an indoor growing facility, a natural resources excavation site (e.g., a mine, an oil field, etc.), and the like. Depending on the type of environment, the types of objects, devices, and sensors that are found in the environments will differ. Non-limiting examples of physical objects 12922 include raw materials, manufactured products, excavated materials, containers (e.g., boxes, dumpsters, cooling towers, vats, pallets, barrels, palates, bins, and the like), furniture (e.g., tables, counters, workstations, shelving, etc.), and the like. Non-limiting examples of devices 12924 include robots, computers, vehicles (e.g., cars, trucks, tankers, trains, forklifts, cranes, etc.), machinery/equipment (e.g., tractors, tillers, drills, presses, assembly lines, conveyor belts, etc.), and the like. The sensors 12926 may be any sensor devices and/or sensor aggregation devices that are found in a sensor system 12930 within an environment. Non-limiting examples of sensors 12926 that may be implemented in a sensor system 12930 may include temperature sensors 12932, humidity sensors 12934, vibration sensors 12936, LIDAR sensors 12938, motion sensors 12940, chemical sensors 12942, audio sensors 12944, pressure sensors 12946, weight sensors 12948, radiation sensors 12950, video sensors 12952, wearable devices 12954, relays 12956, edge devices 12958, crosspoint switches 12960, and/or any other suitable sensors. Examples of different types of physical objects 12922, devices 12924, sensors 12926, and environments 12920 are referenced throughout the disclosure.")
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 3-4, 10-11, 15, and 20-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella (US 2021/0248514 Al) in view of Balasubramanian (US 2010/0332273 Al).
Claim(s) 3 and 20 –
Cella discloses the limitations of claims 1 and 18
Cella does not explicitly disclose the following, however, in analogous art of automated recommendations, Balasubramanian discloses the following:
wherein the one or more techniques used by the Al engine includes any or a combination of Linear program (LP), Mixed Integer Programming (MIP), Quadratic Programming (QP), and metaheuristics (MH). (Balasubramanian: Paragraph 291, "As an approach to solving the mixed-integer non-linear programming model (MINLP), the present invention may use any suitable relaxation and/or decomposition method known in the art. One such technique is to decompose the MINLP into a mixed-integer linear programming (MILP) subproblem and, optionally, a non-linear programming (NLP) subproblem. Where the MINLP is decomposed into both an MILP subproblem and an NLP subproblem, the resulting MILP and NLP subproblems can then be solved in a cooperative manner (e.g., iteratively)."; Paragraph 297, "In certain embodiments, the method may further comprise formulating a non-linear programming (NLP) subproblem by fixing the integer components of the MINLP (e.g., the binary decision variables) based on the solution obtained for the MILP subproblem. The NLP subproblem may be solved using any suitable NLP solver known in the art. In some cases, where the solution thus far includes the use of a blending tank, the NLP subproblem solution may be further improved by iterative bilinear fixing of the original MINLP as described above.")
Cella discloses a method for optimizing, selecting, and utilizing artificial intelligence to generate a recommendation specified to the attributes and nature of a predefined problem. Balasubramanian discloses a method for using various machine learning methods to generate recommendations for actions in a supply chain process. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the invention of Cella with the methods of Balasubramanian in order to further optimize the profits obtainable with the recommendations as disclosed by Balasubramanian (Balasubramanian: Paragraph 10, "There is a need in the art for an application that optimizes the total net profit associated with product allocation, transportation routing, transportation vehicle/route scheduling, and product blending.")
Claim(s) 4 and 21 –
Cella discloses the limitations of claims 1 and 18
Cella does not explicitly disclose the following, however, in analogous art of automated recommendations, Balasubramanian discloses the following:
wherein the one or more tools used by the AI engine includes any or a combination of Genetic Algorithms (GA) and Simulated Annealing (SA) to execute the one or more techniques. (Balasubramanian: Paragraph 294, "In the preferred embodiment, one or more improvement heuristics are used to improve the initial feasible solution found by the construction heuristic. Preferably, the improvement heuristics include one or more, and preferably multiple, large neighborhood searches. For example, the solution process may comprise a construction heuristic followed by multiple large neighborhood searches. Preferably, each large neighborhood search is employed in an iterative manner until no further improvements in the feasible solution are obtained."; Paragraph 295, "In some embodiments, the solution process uses two improvement heuristics, both of which comprise a large neighborhood search. In this embodiment, the first heuristic is a "Solution Polishing" functionality offered by CPLEX. Although the exact details of the CPLEX Solution Polishing are proprietary to CPLEX, it appears to be a combination of a genetic algorithm and a large neighborhood search. In this embodiment, the second heuristic relaxes the schedule of two vessels in the feasible solution and fixes the remaining vessel schedules in accordance with the feasible solution. Each improvement heuristic is solved by the solver. Each improvement heuristic can be utilized alone or in series. When operated in series, the answer from the first improvement heuristic is used in the next improvement heuristic. Preferably, each improvement heuristic is used multiple times, in an iterative manner, until no further improvement in the feasible solution is obtained.")
Cella discloses a method for optimizing, selecting, and utilizing artificial intelligence to generate a recommendation specified to the attributes and nature of a predefined problem. Balasubramanian discloses a method for using various machine learning methods to generate recommendations for actions in a supply chain process. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the invention of Cella with the methods of Balasubramanian in order to further optimize the profits obtainable with the recommendations as disclosed by Balasubramanian (Balasubramanian: Paragraph 10, "There is a need in the art for an application that optimizes the total net profit associated with product allocation, transportation routing, transportation vehicle/route scheduling, and product blending.")
Claim(s) 10 –
Cella disclose the limitations of claim 1
Cella does not explicitly disclose the following, however, in analogous art of automated recommendations, Balasubramanian discloses the following:
further configured to use the customized solution to minimize the cost and achieve the desired product blend for the entity. (Balasubramanian: Paragraph 8, "The modeling problem described therein includes the following assumptions/simplifications: (1) each cargo (i.e., crude oil to be shipped) moves between a single loading port and a single discharging port; (2) the cargo shipped must always be a full vessel load (i.e., the cargo must be of a fixed size); and (3) each vessel is the same size. In addition, the objective function of the model is to minimize cost as opposed to net profit margin."; Paragraph 21, "A mixed integer non-linear programming (MINLP) model is populated using the data set. The MINLP comprises an objective function for net profit margin and a plurality of constraints. The objective function for net profit margin comprises the sum of the monetary values of the bulk products discharged directly to the demand streams from the vehicles, the sum of the monetary values of the bulk products discharged from each blending tank to a demand stream, minus the sum of the monetary values of the bulk products loaded from the supply streams, minus costs related to the transportation of the bulk products between the supply locations and the demand locations, minus costs related to the use of each blending tank for receiving and discharging bulk products. In some cases, the objective function further comprises the sum of the inventory holding costs. The constraints include one or more non-linear terms (e.g., bilinear terms) relating to the quantity(ies) and/or property(ies) of blending tank content."; Paragraph 47, "he vehicles may be homogeneous or heterogeneous in capacity and cost. In one embodiment, the vehicles are heterogeneous in both capacity and cost. The vehicles utilized in the invention will typically contain multiple segregations to permit the transport of multiple products without unintentionally compromising the compositional integrity of the products. Accordingly, each bulk product that is loaded from each supply location is transported in one or more separate segregations of the same transportation vehicle."; Paragraph 68, "The first step is entering data into a database. The database may be integral to, or interfaces with, a computer application. The data typically comprises one or more, and preferably all, of the following: (i) information regarding each supply stream at each supply location to be considered and its properties, monetary valuation, accumulated inventory, storage constraints and production schedule; (ii) information regarding each demand stream at each demand location to be considered and its property range requirements, property based monetary valuation for actual bulk products that are delivered to meet those requirements, inventory, storage constraints and consumption schedule; (iii) information regarding the availability, cost, capacity and current cargo of each vehicle in an available fleet; (iv) information regarding the relative separation, in travel time and/or distance, of each supply location and demand location from one another and cost for traversing the same; (v) information regarding vehicle size restrictions, loading restrictions and discharge restrictions at each supply location and demand location; and (vi) information regarding holding costs, if any, for storing bulk product at the supply locations, demand locations and/or on-board the transportation vehicles; and (vii) information regarding the availability of spot market purchases to augment supply deficits and spot market sales to deplete supply overages. Additional data that may be contained in the database are explained in the worksheet descriptions below."; Paragraph 290, "In certain embodiments, the application is configured to provide a solution quickly enough (e.g., in less than thirty minutes) to support decision making in real-time scenarios where business parameters may change quickly and frequent re-optimizations or "what-if" case analysis are needed. A typical complex problem has at least 4 supply locations, at least 4 demand locations, a fleet of at least 10 vehicles, at least one production stream per supply location, at least one demand stream per demand location, and about a month of planning period. In some cases, the complex problem also has at least one spot purchase location and at least one spot sale location.")
Cella discloses a method for optimizing, selecting, and utilizing artificial intelligence to generate a recommendation specified to the attributes and nature of a predefined problem. Balasubramanian discloses a method for using various machine learning methods to generate recommendations for actions in a supply chain process. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the invention of Cella with the methods of Balasubramanian in order to further optimize the profits obtainable with the recommendations as disclosed by Balasubramanian (Balasubramanian: Paragraph 10, "There is a need in the art for an application that optimizes the total net profit associated with product allocation, transportation routing, transportation vehicle/route scheduling, and product blending.")
Claim(s) 11 –
Cella disclose the limitations of claim 1
Cella does not explicitly disclose the following, however, in analogous art of automated recommendations, Balasubramanian discloses the following:
wherein the optimized platform is configured to use the customized solution to maximize profitability while creating a product assortment for the entity (120). (Balasubramanian: Paragraph 22, "The MINLP model is solved for maximizing the objective function for net profit margin. Based on the solution obtained, one or more bulk products are physically transported to a demand location, or from a supply location, or both. In some cases, the method further comprises, based on the solution obtained, physically transferring a bulk product into a blending tank containing another bulk product, and blending the bulk products in the blending tank to form a new blended bulk product. The bulk product may be transferred into the blending tank from any of various sources, including a vehicle, pipeline, or another tank."; Paragraph 71, "The user may review the solution results to insure that the results are acceptable. If the results are not deemed satisfactory, or if the user wants to perform an additional what-if analysis, then the user can restart the process with an adjusted data set. Based on the solution obtained, one or more of the following may be determined or planned: bulk product allocations, transportation routing, transportation vehicle/route scheduling, and blending of bulk products within a within a planning horizon, in order to maximize total net profit margin."; Paragraph 74, "This example describes one particular embodiment of the invention and its use in finding a solution ( either optimal or near optimal) for the allocation, transportation routing, vessel/voyage scheduling and blending planning to maximize total net profit margin in the movement of VGO from supply ports to demand ports to feed FCC units within a given planning horizon. In this embodiment, each supply port produces one or more streams of VGO, each stream having an independent composition and/or property set, and each stream having an independent inventory and production schedule. Similarly, each demand port requires one or more streams of VGO for its FCC units, each stream having independent ranges of property requirements, and each stream having an independent inventory and consumption schedule. In addition, each load and discharge port has unique physical and temporal restrictions for vessel usage and each vessel has unique size, availability, capacity, and cost parameters. The allocation, transportation routing, voyage/vessel scheduling and blending are optimized, in view of all of these factors, to meet the demand consumption using the load port production in a manner that maximizes total net profit. For reference purposes, the particular computer application described in this embodiment is nicknamed "METEOROID."")
Cella discloses a method for optimizing, selecting, and utilizing artificial intelligence to generate a recommendation specified to the attributes and nature of a predefined problem. Balasubramanian discloses a method for using various machine learning methods to generate recommendations for actions in a supply chain process. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the invention of Cella with the methods of Balasubramanian in order to further optimize the profits obtainable with the recommendations as disclosed by Balasubramanian (Balasubramanian: Paragraph 10, "There is a need in the art for an application that optimizes the total net profit associated with product allocation, transportation routing, transportation vehicle/route scheduling, and product blending.")
Claim(s) 15 and 22 –
Cella disclose the limitations of claims 1 and 18
Cella does not explicitly disclose the following, however, in analogous art of automated recommendations, Balasubramanian discloses the following:
wherein the one or more input parameters pertain to any or a combination of supply chain, distribution network services, scheduling and delivery systems. (Balasubramanian: Paragraph 11, "The present invention provides a tool for determining bulk product allocation, transportation routing, vehicle/route scheduling, and/or blending operations. The tool is capable of handling a typical petroleum product transportation problem, which can be quite complex. A typical petroleum product transportation problem involves, inter alia, multiple supply locations each with multiple production products, each with different properties and different economic valuations, multiple demand locations each with multiple demand stream needs, each having different requirements and different price valuations for delivered products that meet the requirements, non-constant rates of supply and demand, and a heterogeneous fleet of transportation vehicles."; Paragraph 21, "A mixed integer non-linear programming (MINLP) model is populated using the data set. The MINLP comprises an objective function for net profit margin and a plurality of constraints. The objective function for net profit margin comprises the sum of the monetary values of the bulk products discharged directly to the demand streams from the vehicles, the sum of the monetary values of the bulk products discharged from each blending tank to a demand stream, minus the sum of the monetary values of the bulk products loaded from the supply streams, minus costs related to the transportation of the bulk products between the supply locations and the demand locations, minus costs related to the use of each blending tank for receiving and discharging bulk products. In some cases, the objective function further comprises the sum of the inventory holding costs. The constraints include one or more non-linear terms (e.g., bilinear terms) relating to the quantity(ies) and/or property(ies) of blending tank content.")
Cella discloses a method for optimizing, selecting, and utilizing artificial intelligence to generate a recommendation specified to the attributes and nature of a predefined problem. Balasubramanian discloses a method for using various machine learning methods to generate recommendations for actions in a supply chain process. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the invention of Cella with the methods of Balasubramanian in order to further optimize the profits obtainable with the recommendations as disclosed by Balasubramanian (Balasubramanian: Paragraph 10, "There is a need in the art for an application that optimizes the total net profit associated with product allocation, transportation routing, transportation vehicle/route scheduling, and product blending.")
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Cella
(US 2021/0248514 Al) in view of Cella'996 (US 2021/0182996 Al)
Claim(s) 12 –
Cella discloses the limitations of claim 1
Cella does not explicitly disclose the following, however, in analogous art of automated recommendations, Cella'996 discloses the following:
wherein the optimized platform is configured to use the customized solution to minimize the cost of packing materials for a given set of products for the entity. (Cella'996: Paragraph 55, "In embodiments, the supply factors are factors selected from the group consisting of Component availability, material availability, component location, material location, component pricing, material pricing, taxation, tariff, impost, duty, import regulation, export regulation, border control, trade regulation, customs, navigation, traffic, congestion, vehicle capacity, ship capacity, container capacity, package capacity, vehicle availability, ship availability, container availability, package availability, vehicle location, ship location, container location, port location, port availability, port capacity, storage availability, storage capacity, warehouse availability, warehouse capacity, fulfillment center location, fulfillment center availability, fulfillment center capacity, asset owner identity, system compatibility, worker availability, worker competency, worker location, goods pricing, fuel pricing, energy pricing, route availability, route distance, route cost, and route safety factors."; Paragraph 73, "In embodiments, the digital twin system outputs a graphical representation of the package digital twin to a display, whereby a user views the simulation via the display. In embodiments, the digital twin system outputs a graphical representation of the package digital twin in a graphical user interface, whereby a user edits the packaging design via the graphical user interface. In embodiments, the digital twin system outputs a simulation outcome of the simulation to the machine learning system, and the machine learning system reinforces the machine-learned model used to determine the packaging design recommendation based on the simulation outcome. In embodiments, the artificial intelligence system receives the request from a packaging design system that designs packaging for physical objects, wherein the request includes one or more packaging factors corresponding to a proposed packaging design for the physical objects. In embodiments, the packaging factors include one or more of: a type of the physical objects, dimensions of the physical objects, masses of the physical objects, and shipping methods of the physical objects. In embodiments, the packaging design system provides outcome data relating to the packaging design recommendation to the machine learning system, and the machine learning system reinforces the machine-learned model that are used to determine the packaging design recommendation based on the outcome data. In embodiments, the artificial intelligence system determines the packaging design recommendation to minimize damage. In embodiments, the artificial intelligence system determines the packaging design recommendation to minimize costs. In embodiments, the artificial intelligence system determines the packaging design recommendation to mitigate environmental impact.")
Cella discloses a method for optimizing, selecting, and utilizing artificial intelligence to generate a recommendation specified to the attributes and nature of a predefined problem. Cella'996 discloses a method for enterprise management and recommendations for supply chain and demand entities. At the time of Applicant's filed invention, one of ordinary skill in the art would have deemed it obvious to combine the methods of Cella with the teachings of Cella'996 in order to improve the ability for large data to be translated into insights and recommendations as disclosed in Cella'996 (Cella'996: Paragraph 6, "A need exists for methods and systems that allow enterprises not only to obtain data, but to convert the data into insights and to translate the insights into well-informed decisions and timely execution of efficient operations.")
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Philip N Warner whose telephone number is (571)270-7407. The examiner can normally be reached Monday-Friday 7am-4:00pm.
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/Philip N Warner/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624