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 preliminary amendment filed 5/08/2025 is entered. Claims 1-12 are pending and have been examined on the merits set forth below.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 5/02/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claim(s) 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is not eligible for patenting. There are two criteria for determining subject matter eligibility:
(a) first, a claimed invention must fall within one of the four statutory categories of invention set forth in 35 U.S.C. 101, i.e., process, machine, manufacture, or composition of matter (Step 1); and
(b) second, a claimed invention must be directed to patent-eligible subject matter and not a judicial exception (unless the claim as a whole includes additional limitations amounting to significantly more than the exception) (Step 2).
Step 1:
Claim(s) 1-12 is/are within the four potentially eligible categories of invention (a process, a machine and an article of manufacture, respectively), satisfying Step 1 of the Subject Matter Eligibility (SME) test.
Step 2:
As per Prong One of Step 2A of the §101 eligibility analysis set forth in MPEP 2106, the Examiner notes that the claims recite mental processes. More specifically, the independent claims recite:
In claim 1:
A system comprising a database and an interface for inputting and outputting data, a data processing unit and an execution unit, wherein the database is a graph database configured to store data according to a data model that maps a well-defined ontology; [additional element]
wherein the data processing unit is configured to [additional element]
unify, supplement, and/or enrich the data stored in the graph database [evaluation- mental process]
identify statistical and/or causal relationships between data in the database and model these relationships by means of statistical models and/or physico-chemical models and/or identify research and development goals in the stored data and/or generate and/or adapt suitable workflows and/or work steps to achieve research and development goals; [mental process – evaluation/observation]
wherein the execution unit is configured to [additional element]
select a workflow for achieving a development goal as a function of the development goal and/or select a next work step from a set of work steps of a selected workflow, in particular as a function of a preceding work step and/or a result of a preceding work setup [mental process – evaluation].
In claims 8 and 12:
a) storing data in a graph database according to a data model that represents a well- defined ontology; [additional element]
b) unifying, supplementing, and/or enriching data stored in the graph database; [evaluation – mental process]
c) identifying statistical and/or causal relationships between data stored in the graph database and/or modeling these relationships using statistical models and/or physico-chemical models; [evaluation – mental process]
d) identifying research and development goals included in the stored data and/or generating workflows and/or work steps suitable for achieving the research and development goals; [evaluation/observation – mental process]
e) selecting a workflow for achieving a development goal as a function of the development goal and/or selecting a work step from a set of work steps of a selected workflow, in particular as a function of a preceding work step and/or a result of a preceding work step; [observation/evaluation – mental process]
f) selecting a terminal for performing a work step, in particular as a function on the type, scope and/or time of the work step to be performed and/or the type, scope, time and/or status of fulfillment of the conditions of availability of the terminal; [evaluation – mental process]
g) analyzing a result of a work step by an edge computing unit and/or a fog computing unit and/or forwarding the analysis result as the result of the work step; [evaluation – mental process – analyzing the work product; the computing units are not functionally claimed]
h) capturing, labeling with respect to formation and origin and/or storing in the graph database a result of a work step; [evaluation – mental process]
i) localizing, capturing and/or storing in the graph database information relevant to a research goal, in particular specialist articles, publications, test series, lectures, comments and/or other relevant records and/or documentation; [mental process – pen and paper – recording data]
j) localizing, capturing and/or storing information in the graph database about the type, scope, time of availability and/or non-availability of a terminal, about technical, administrative, legal, contractual and/or other conditions for the availability of a terminal and/or about the status of fulfillment of the conditions for availability; [mental process – pen and paper – recording data]
k) graphically displaying a result of a work step, several work steps of a workflow, a workflow, a work step and/or the availability of a terminal for performing a work step, [mental process – pen and paper] and
l) receiving a selection from a human user of a workflow or work step to be performed and/or terminal for performing a work step. [mental process]
The claims recite data analysis steps to select a workflow or work step to be performed. Overall, the claims are certain method of organizing human activity, specifically managing personal behavior or relationships or interactions between people, as the claim limitations represent the instructions or rules one would follow to carry out the method. Certain limitations of the claim are considered mental processes as they recite observations and evaluations that can be done in the mind or with pen and paper. These limitation are indicated as such in the reproduced claim above. The additional elements and nominal recitation of computer elements in claims 1, 8 and 12 do not necessarily preclude the claim from reciting an abstract idea as evidenced by the analysis at Prong 2 of Step 2A.
Regarding Prong Two of Step 2A, a claim reciting an abstract idea must be analyzed to determine whether any additional elements in the claim integrate the judicial exception into a practical application. Limitations that are indicative of integration into a practical application include: Improvements to the functioning of a computer, or to any other technology or technical field, as discussed in MPEP 2106.05(a); Applying or using a judicial exception to effect a particular treatment or prophylaxis for disease or medical condition – see Vanda Memo; Applying the judicial exception with, or by use of, a particular machine, as discussed in MPEP 2106.05(b); Effecting a transformation or reduction of a particular article to a different state or thing, as discussed in MPEP 2106.05(c); and Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception, as discussed in MPEP 2106.05(e) and the Vanda Memo issued in June 2018.
In this case, the independent claims do not include limitations that meet the criteria listed above, thus the abstract idea is not integrated into a practical application. Independent claim 1 recites a system comprising a database and an interface, a data processing unit and an execution unit. These amount to using a computer as a tool to perform the abstract idea and do not integrate the abstract idea into a practical application. Independent claims 8 and 12 recite storing and processing data in a graph database. This amounts to using a computer as a tool to perform the abstract idea and does not integrate the abstract idea into a practical application.
The dependent claims further limit the abstract idea and some recite additional elements that do not integrate the abstract idea into a practical application. Dependent claims 2 recites the execution unit selects a terminal. This amounts to using a computer as a tool to perform an abstract selection step. There is no integration into a practical application. Dependent claims 3 and 4 recite a data acquisition unit configured to localize, capture and/or store information in the graph database. Localize, capture and/or store is abstract and the unit and database amount using a computer as a tool to perform the abstract idea. There is no integration into a practical application. Dependent claim 5 recites an interactive human-machine interface configured to display data and/or receive input. This amounts to using a computer as a tool to perform an abstract idea. There is no integration into a practical application. Dependent claim 6 recite details the system comprising at least one of a server, a cloud system, a terminal, an edge computing unit and a fog computing unit. These additional elements amount to using a computer as a tool to perform the abstract idea. There is no integration into a practical application. Dependent claim 7, 9 and 10 recite applying methods of artificial intelligence. This amount to adding the words apply it or instructions to implement an abstract algorithm on a computer. There is no integration into a practical application. Dependent claim 11 recites criteria for selecting a terminal which is an abstract mental process (observation/evaluation). Using the system to perform the selection amounts to using a computer as a tool to perform the abstract idea. There is no integration into a practical application.
The claims do not include limitations beyond generally linking the use of the abstract idea to a particular technological environment. When considered individually and in combination, the system/software claim elements only contribute generic recitations of technical elements to the claims. It is readily apparent, for example, that the claim is not directed to any specific improvements of these elements. The invention is not directed to a technical improvement. When the claims are considered individually and as a whole, the additional elements noted above appear to merely apply the abstract concept to a technical environment in a very general sense.
Lastly and in accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, and when considered individually and in combination, the additional elements amount to no more than mere instruction to apply the exception using generic computer component. Mere instruction to apply an exception using generic computer components cannot provide an inventive concept.
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.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-12 is/are rejected under 35 U.S.C. 102(a)(1) and 102(a)(2) as being anticipated by Sreekumar et al, US 2026/01.
As per claim 1, Sreekumar et al discloses a research and development system suitable for researching and/or developing products and manufacturing methods for products, in particular energy materials, which comprises a database and an interface for inputting and outputting data, a data processing unit and an execution unit,
wherein the database is a graph database configured to store data according to a data model that maps a well-defined ontology; ([0074] – system elements; [0056] – graph database with ontology-guided graph traversal)
wherein the data processing unit is configured to
unify, supplement, and/or enrich the data stored in the graph database, ([0045, 0048, 0087, 00174] - standardization and semantic harmonization processes that ensure consistent data formats, unified measurement units, and compatible data structures across diverse enterprise system architectures while maintaining data lineage and provenance tracking for all integrated information sources)
identify statistical and/or causal relationships between data in the database and model these relationships by means of statistical models and/or physico-chemical models and/or ([0102, 0127] - maintaining semantic relationships that capture causal, temporal, spatial, and quantitative dependencies across all sustainability concepts and ontology-guided graph traversal and multi-hop reasoning capabilities that enable complex knowledge discovery and relationship analysis across interconnected sustainability domains)
identify research and development goals in the stored data and/or generate and/or adapt suitable workflows and/or work steps to achieve research and development goals; ([0062, 0134, 0137-0139] - Process enables systematic achievement of sustainability goals across all ESG topics while capturing verified business value through coordinated operation of Universal Sustainability Intelligence Module, Multi-Dimensional Value Quantification Engine, Trust Enhancement and Verification Framework, Advanced RAG Implementation Controller, Multi-Agent Orchestration System, and Enterprise Integration and Optimization Platform)
wherein the execution unit is configured to select a workflow for achieving a development goal as a function of the development goal and/or select a next work step from a set of work steps of a selected workflow, in particular as a function of a preceding work step and/or a result of a preceding work step. ([0141-0145] - orchestrator coordination where Workflow implements task decomposition and agent assignment based on query requirements and specialized agent capabilities).
As per claim 2, Sreekumar et al discloses the research and development system of claim 1, wherein the execution unit is configured to select a terminal for performing a work step ([0057, 0072. 0094, 0143] - task decomposition and agent assignment based on query requirements and specialized agent capabilities).
As per claim 3, Sreekumar et al discloses the research and development system of claim 2, wherein the research and development system comprises a data acquisition unit which is configured to
localize, capture and/or store in the graph database information relevant to a selected research goal, in particular specialist articles, publications, test series, lectures, comments and/or other relevant records and/or documentation; ([0075, 0087] - harmonize enterprise systems, regulatory databases, scientific literature, industry benchmarks, corporate disclosures, patent databases, and satellite data into unified sustainability data models) and/or
localize a result of an implementation of a work step by a terminal capture it, label it with respect to formation and origin and/or store it in the graph database. ([0119] - graph database architectures that capture complex relationship networks across sustainability domains while maintaining document database systems for comprehensive sustainability document storage and indexing)
As per claim 4, Sreekumar et al discloses the research and development system of claim 1, wherein the research and development system comprises a data acquisition unit configured to capture information on the type, scope and/or time of availability and/or non-availability of a terminal, and/or information on technical, administrative, legal, contractual and/or other conditions of availability of a terminal and on the status of fulfillment of the conditions of availability and/or to store it in the graph database. ([0175] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
As per claim 5, Sreekumar et al discloses the research and development system of claim 1, wherein the research and development system comprises an interactive human-machine interface configured to display the results of a work step and/or of the work steps of a workflow graphically, display a workflow, a work step, the availability of a terminal for performing a work step and/or receive an input from a human user for selecting a workflow to be performed, a work step to be performed and/or terminal (170) for performing a work step. ([0057] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
As per claim 6, Sreekumar et al discloses the research and development system of claim 1, wherein the research and development system comprises at least one of a server, a cloud system, a terminal, an edge computing unit and/or a fog computing unit. ([0052, 0058, 0257] – platform, software)
As per claim 7, Sreekumar et al discloses the research and development system of claim 1, wherein the graph database, the data processing unit, the execution unit, the data acquisition unit, the terminal, the edge computing unit, the fog computing unit and/or the interactive human-machine interface are configured to apply methods of artificial intelligence results in real time ([0013, 0037, 0091] - real time AI learning methods).
As per claim 8, Sreekumar et al discloses a method for operating a research and development system according to claim 1, the method comprising at least one of steps a) to k):
a) storing data in a graph database according to a data model that represents a well-defined ontology; ([0074] – system elements; [0056] – graph database with ontology-guided graph traversal)
b) unifying, supplementing, and/or enriching data stored in the graph database; ([0045, 0048, 0087, 00174] - standardization and semantic harmonization processes that ensure consistent data formats, unified measurement units, and compatible data structures across diverse enterprise system architectures while maintaining data lineage and provenance tracking for all integrated information sources)
c) identifying statistical and/or causal relationships between data stored in the graph database and/or modeling these relationships using statistical models and/or physico-chemical models; ([0102, 0127] - maintaining semantic relationships that capture causal, temporal, spatial, and quantitative dependencies across all sustainability concepts and ontology-guided graph traversal and multi-hop reasoning capabilities that enable complex knowledge discovery and relationship analysis across interconnected sustainability domains)
d) identifying research and development goals included in the stored data and/or generating workflows and/or work steps suitable for achieving the research and development goals; ([0062, 0134, 0137-0139] - Process enables systematic achievement of sustainability goals across all ESG topics while capturing verified business value through coordinated operation of Universal Sustainability Intelligence Module, Multi-Dimensional Value Quantification Engine, Trust Enhancement and Verification Framework, Advanced RAG Implementation Controller, Multi-Agent Orchestration System, and Enterprise Integration and Optimization Platform)
e) selecting a workflow for achieving a development goal as a function of the development goal and/or selecting a work step from a set of work steps of a selected workflow, in particular as a function of a preceding work step and/or a result of a preceding work step; . ([0141-0145] - orchestrator coordination where Workflow implements task decomposition and agent assignment based on query requirements and specialized agent capabilities).
f) selecting a terminal for performing a work step, in particular as a function on the type, scope and/or time of the work step to be performed and/or the type, scope, time and/or status of fulfillment of the conditions of availability of the terminal; ([0175] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
g) analyzing a result of a work step by an edge computing unit and/or a fog computing unit and/or forwarding the analysis result as the result of the work step; ([0057] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
h) capturing, labeling with respect to formation and origin and/or storing in the graph database a result of a work step; ([0119] - graph database architectures that capture complex relationship networks across sustainability domains while maintaining document database systems for comprehensive sustainability document storage and indexing)
i) localizing, capturing and/or storing in the graph database information relevant to a research goal, in particular specialist articles, publications, test series, lectures, comments and/or other relevant records and/or documentation; ([0075, 0087] - harmonize enterprise systems, regulatory databases, scientific literature, industry benchmarks, corporate disclosures, patent databases, and satellite data into unified sustainability data models)
j) localizing, capturing and/or storing information in the graph database about the type, scope, time of availability and/or non-availability of a terminal, about technical, administrative, legal, contractual and/or other conditions for the availability of a terminal and/or about the status of fulfillment of the conditions for availability; ([0175] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
k) graphically displaying a result of a work step, several work steps of a workflow, a workflow, a work step and/or the availability of a terminal for performing a work step, and/or receiving a selection from a human user of a workflow or work step to be performed and/or terminal for performing a work step. ([0057] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
As per claim 9, Sreekumar et al discloses the method of claim 8, wherein at least one of steps a)-k) is executed using methods of artificial intelligence. ([0013, 0037, 0091] - real time AI learning methods).
As per claim 10, Sreekumar et al discloses the method of claim 9, wherein the at least one step executed using methods of artificial intelligence is executed in real time. ([0013, 0037, 0091] - real time AI learning methods).
As per claim 11, Sreekumar et al discloses the research and development system of claim 2, wherein the terminal is selected as a function of at least one of (i) the type, scope and/or time of the work step to be performed and (ii) the type, scope, the time and/or the status of fulfillment of the conditions of availability of the terminal. ([0175] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
As per claim 12, Sreekumar et al discloses a method for operating a research and development system, the method comprising
a) storing data in a graph database according to a data model that represents a well-defined ontology; ([0074] – system elements; [0056] – graph database with ontology-guided graph traversal)
b) unifying, supplementing, and/or enriching data stored in the graph database; ([0045, 0048, 0087, 00174] - standardization and semantic harmonization processes that ensure consistent data formats, unified measurement units, and compatible data structures across diverse enterprise system architectures while maintaining data lineage and provenance tracking for all integrated information sources)
c) identifying statistical and/or causal relationships between data stored in the graph database and/or modeling these relationships using statistical models and/or physico-chemical models; ([0102, 0127] - maintaining semantic relationships that capture causal, temporal, spatial, and quantitative dependencies across all sustainability concepts and ontology-guided graph traversal and multi-hop reasoning capabilities that enable complex knowledge discovery and relationship analysis across interconnected sustainability domains)
d) identifying research and development goals included in the stored data and/or generating workflows and/or work steps suitable for achieving the research and development goals; ([0062, 0134, 0137-0139] - Process enables systematic achievement of sustainability goals across all ESG topics while capturing verified business value through coordinated operation of Universal Sustainability Intelligence Module, Multi-Dimensional Value Quantification Engine, Trust Enhancement and Verification Framework, Advanced RAG Implementation Controller, Multi-Agent Orchestration System, and Enterprise Integration and Optimization Platform)
e) selecting a workflow for achieving a development goal as a function of the development goal and/or selecting a work step from a set of work steps of a selected workflow, in particular as a function of a preceding work step and/or a result of a preceding work step; . ([0141-0145] - orchestrator coordination where Workflow implements task decomposition and agent assignment based on query requirements and specialized agent capabilities).
f) selecting a terminal for performing a work step, in particular as a function on the type, scope and/or time of the work step to be performed and/or the type, scope, time and/or status of fulfillment of the conditions of availability of the terminal; ([0175] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
g) analyzing a result of a work step by an edge computing unit and/or a fog computing unit and/or forwarding the analysis result as the result of the work step; ([0057] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
h) capturing, labeling with respect to formation and origin and/or storing in the graph database a result of a work step; ([0119] - graph database architectures that capture complex relationship networks across sustainability domains while maintaining document database systems for comprehensive sustainability document storage and indexing)
i) localizing, capturing and/or storing in the graph database information relevant to a research goal, in particular specialist articles, publications, test series, lectures, comments and/or other relevant records and/or documentation; ([0075, 0087] - harmonize enterprise systems, regulatory databases, scientific literature, industry benchmarks, corporate disclosures, patent databases, and satellite data into unified sustainability data models)
j) localizing, capturing and/or storing information in the graph database about the type, scope, time of availability and/or non-availability of a terminal, about technical, administrative, legal, contractual and/or other conditions for the availability of a terminal and/or about the status of fulfillment of the conditions for availability; ([0175] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
k) graphically displaying a result of a work step, several work steps of a workflow, a workflow, a work step and/or the availability of a terminal for performing a work step, and/or receiving a selection from a human user of a workflow or work step to be performed and/or terminal for performing a work step. ([0057] - engages continuous monitoring capabilities that capture real-time environmental conditions, production performance metrics, equipment operational status, and safety compliance indicators across all operational facilities and supply chain locations)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Baharloo et al, US 2007/0203747 - E-COMMERCE SYSTEMS AND METHODS - a set of biological applications designed to carry out a particular type of biological research goal, such as, for example, a workflow to study gene expression, protein expression, or to carry out cellular analysis
Chandrasekar et al, US 2022/0261662 - EXTENSIBLE INFORMATION SYSTEMS AND METHODS - knowledge base that captures and stores artifacts/information and maintains the various associations (goals to tasks, tasks to services) used in selecting a workflow to service a research query, knowledge graphs 110 are featured in an exemplary extensible information system and method
Jackson et al, ROBOT: A Tool for Automating Ontology Workflows - an open source library and command-line tool for automating ontology development tasks. The library can be called from any programming language that runs on the Java Virtual Machine (JVM). Most usage is through the command-line tool, which runs on macOS, Linux, and Windows. ROBOT provides ontology processing commands for a variety of tasks, including commands for converting formats, running a reasoner, creating import modules, running reports, and various other tasks. These commands can be combined into larger workflows using a separate task execution system such as GNU Make, and workflows can be automatically executed within continuous integration systems
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JOHNNA LOFTIS
Primary Examiner
Art Unit 3625
/JOHNNA R LOFTIS/ Primary Examiner, Art Unit 3625
