DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Responsive to the communication dated June 2, 2023.
Claims 1-12 are presented for examination.
Claims 1-12 are rejected.
Priority
The ADS does not claim any international or domestic priority. The effective filing date is June 2, 2023.
Information Disclosure Statement
IDS dated 06/02/2023 has been reviewed. See attached.
Drawings
The drawings dated 06/02/2023 have been reviewed. They are accepted.
Specification
The abstract dated 06/02/2023 has not been reviewed. The abstract is objected to because it has more than 150 words.
The disclosure is objected to because of the following informalities:
In paragraph 0004, line 5, an unnecessary comma exists after “bi-directional”.
In paragraph 0006, line 7, “the” should read “them”.
In paragraph 0007, line 5, there is an extra space between “all” and “the”.
In paragraph 0007, line 7, there is an extra space between “representing” and “many”.
In paragraph 0010, line 9, there is an extra space after the period.
In paragraph 0011, line 8, “vessels” should read “vessel’s”.
In paragraph 0019, line 7, the word “a” or “the” is missing.
In paragraph 0019, line 11, the word “a” or “the” is missing.
In paragraph 0021, line 5, “block 305” does not exist in the drawings.
Appropriate correction is required.
Claim Objections
A review of the claim found the following objections:
Claim 5 objected to because of the following informalities: "and one or processing units" should read "and one or more processing units". The phrase is used correctly later, therefore for the purposes of examination, this examiner has interpreted the limitation with its grammatical correction.
Claim 9 is objected to because of the following informalities: "unit to perform model generation method" should read "unit to perform a model generation".
Claim 9 is objected to because of the following informalities: there is an extra space in line 7 between "function" and "of".
Claim 11 is objected to because of the following informalities: "settings" should read "setting". For examination purposes, the examiner has interpreted the correction to be singular based on the specification. A “group of settings” or other corrections may be possible depending on the applicant’s choice of correction.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 5-8 is rejected under 35 U.S.C. 112(b), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 5 repeatedly recites the limitation “the vessel” in lines 5-6. There is insufficient antecedent basis for this limitation in the claim. For examination purposes, the examiner has interpreted the first instance of “the vessel” as “a vessel”.
Claims 6-8 are rejected because they are dependent on claim 5 and as such, inherit its deficiencies.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-12 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Claim 1.
STEP 1: YES. The claim recites: “A model generation method”.
STEP 2A PRONG ONE: YES.
The claim is annotated with brackets to identify the mental concepts and mathematical concepts.
The claim recites “A model generation method [grouping opinions/assigning semantic types] for digital twin prediction [theoretical mathematical calculations]
loading [observing] a knowledge graph of nodes [mental representation of relationships] intovariables] encapsulating an identifier [label] for a model representing a specific system or function [mental sub-representation] variables] specifying axes [variable common to two nodes] to related others of the nodes;
applying a reasoner [evaluation] to the nodes to infer additional axes [variables common to two nodes] between selected ones of the nodes;
generating a model hierarchy [schematization/categorization] from the knowledge graph [mental representation of relationships] by parsing [understanding/observing] the knowledge graph [mental representation of relationships] to generate [form opinion] an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes [sensemaking/externalization], the data structure defining the model hierarchy [schematization/categorization] which are method steps that perform a mental process of generating an opinion of relationships between variables for building a digital twin. A mental process is a judicial exception.
executing a digital twin [digitized modeling] theoretical mathematical calculations] simulating [modeling] variables] schematization/categorization], which is a series of mathematical calculations that calculate numeric values for a concept mentally derived (in mental processes) from interpreting meaning from a categorization and grouping of hierarchically related data. The interpretation of the digital twin as a mathematical model is supported by Paragraph 6 of the instant specification, which discloses, “modelling approaches for digital twins include both white box approaches based upon mathematical models…”. Mathematical models/concepts are abstract and are a judicial exception.
Schematization/categorization is the grouping of disparate concepts and assigning semantic types or hierarchies to them. Sensemaking is structuring information, defining entities, and connecting ambiguous data into a coherent framework. Externalization is the act of moving internal mental concepts, nodes, and relationships out of working mental space and onto a structured, visual medium. Mental processes are abstract and are a judicial exception.
Therefore, due to the reasoning and annotation above, the claim is found to be directed to one or more abstract ideas.
STEP 2A PRONG TWO: NO.
The claim does not recite additional elements that integrate the exception into a practical application of the exception because the claim does not have additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The claim gathers data for variables, uses the nodes (i.e. variables) in a mental process such as drawing relationships between variables, does a second mental process, then the claim does not apply any specific findings to the following mathematical calculations. The claim uses mental processes to imagine relationships between variables then does math with copies of those variables, but once the relationships are formed and the math has concluded, nothing is done. The claim is nothing more than a mental process and mathematical concept.
While the claim recites “prediction of vessel emissions”, “a specific system or function of a vessel”, “for the vessel”, and “of the vessel”, such limitations are not indicative of a practical application. These elements merely link the use of the judicial exception to a particular technological environment. Merely linking the claim to the technological environment of vessel emissions is not indicative of a practical application because the judicial exception is merely utilized to draw relationships between hypothetical ideas, and the results of drawing relationships are not used or relied upon by any other elements in the claim to perform any application beyond simulating a hypothetical scenario.
While the claim recites, “loading a knowledge graph of nodes into memory of a host computer”, “the data structure”, and “executing a digital twin in the memory of a host computer”, these are merely the recitation of a general computer recited at a high level of generality. See MPEP 2106.05(f). The computer is invoked merely as a tool for implementing the abstract idea of a mental process (or mathematical calculations). Such limitations are not indicative of a practical application.
STEP 2B: NO.
Does the claim as a whole amount to significantly more than an abstract idea?
Claims can recite a mental process even if they are claimed as being performed on a computer. The Supreme Court concluded that an algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." Using a computer as a tool to perform a mental process, the claim does not recite any additional elements that amount to an inventive concept (“significantly more”) than the recited abstract idea because the claim as a whole simply performs a mental process. An example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes.
Therefore, the claim cannot be considered as significantly more than an abstract idea.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 2.
STEP 1: YES. The claim recites: “The method of claim 1, further comprising:”.
STEP 2A PRONG ONE: YES.
The claim recites “changing [re-evaluating] the knowledge graph [variables] by changing [forming an opinion of]
regenerating [re-evaluating] the model hierarchy [variables] which are method steps that perform mental processes.
re-executing the digital twin ” This limitation is the abstract idea of mathematical concepts taking place in a host computer.
STEP 2A PRONG TWO: NO.
The claim does not recite additional elements that integrate the exception into a practical application of the exception because the claim does not have additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The claim changes knowledge (i.e. variables) in a mental process such as drawing relationships between variables, then the claim does not apply any findings. The claim uses mental processes to reimagine relationships between variables but once the relationships are reformed, nothing beyond a judicial exception is done. The claim is nothing more than mental processes and mathematical calculations.
STEP 2B: NO.
The claim inherits the analysis of Step 2B of Claim 1.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 3.
STEP 1: YES. The claim recites: “The method of claim 1”.
STEP 2A PRONG ONE: YES.
The claim recites “an application of a setting to [judgment/opinion] one or more nodes [variables] ”, which articulates additional mental processes for the method of Claim 1. An application of a setting to some values within a hierarchy performs a step that represents the mental process of setting some variables. Therefore, the claim is directed towards a judicial exception: mental processes.
STEP 2A PRONG TWO: NO.
The claim does not recite additional elements that integrate the exception into a practical application of the exception because the claim does not have additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
While the claim recites “the model hierarchy affecting the estimation of an emissions value produced in operation of the vessel”, such limitations are not indicative of a practical application. These elements merely link the use of the judicial exception to a particular technological environment. Merely linking the claim to the technological environment of estimation of vessel emissions is not indicative of a practical application because the judicial exception is merely utilized to draw relationships between hypothetical ideas, and the results of drawing relationships are not used or relied upon by any other elements in the claim to perform any application beyond simulating a hypothetical scenario.
STEP 2B: NO.
The claim inherits the analysis of Step 2B of Claim 1.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 4.
STEP 1: YES. The claim recites: “The method of claim 3”.
STEP 2A PRONG ONE: YES.
The claim recites “simulates a cost [variable] ”, which is directed towards “simulating” abstract variables, which is mathematical calculations, regardless of whether a computer (i.e. the computer of Claim 1) is involved in completing the calculations or not. Therefore, the claim is directed towards an abstract idea.
STEP 2A PRONG TWO: NO.
The claim does not recite additional elements that integrate the exception into a practical application of the exception because the claim does not have additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
While the claim recites “a cost of achieving the emissions value”, such limitations are not indicative of a practical application. These elements merely link the use of the judicial exception to a particular technological environment. Merely linking the claim to the technological environment of cost of achieving vessel emissions is not indicative of a practical application, and the mathematical simulation is not used or relied upon by any other elements in the claim to perform any application beyond simulating a hypothetical scenario.
STEP 2B: NO.
The claim inherits the analysis of Step 2B of Claim 3, which inherits the analysis of Step 2B of Claim 1.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 5.
STEP 1: YES. The claim recites: “A data processing system”.
STEP 2A PRONG ONE: YES.
The claim recites “theoretical mathematical calculations]
a digital twin executing [digitized modeling] modeling] variables] schematization/categorization];
a knowledge graph of nodes [mental representation of relationships] variables] encapsulating an identifier [label] for a model representing a specific system or function [mental sub-representation] variables] specifying axes [variable common to two nodes] to related others of the nodes; and,
applying a reasoner [evaluation] to the nodes to infer additional axes [variables common to two nodes] between selected ones of the nodes; and,
generating the model hierarchy [schematization/categorization] from the knowledge graph [mental representation of relationships] by parsing [understanding/observing] the knowledge graph [mental representation of relationships] to generate [form opinion] an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes [sensemaking/externalization], the data structure defining the model hierarchy [schematization/categorization] , which is a series of mathematical calculations that calculate numeric values for a concept mentally derived (in mental processes) from interpreting meaning from a categorization and grouping of hierarchically related data. The interpretation of the digital twin as a mathematical model is supported by Paragraph 6 of the instant specification, which discloses, “modelling approaches for digital twins include both white box approaches based upon mathematical models…”. Mathematical models/concepts are abstract and are a judicial exception.
Schematization/categorization is the grouping of disparate concepts and assigning semantic types or hierarchies to them. Sensemaking is structuring information, defining entities, and connecting ambiguous data into a coherent framework. Externalization is the act of moving internal mental concepts, nodes, and relationships out of working mental space and onto a structured, visual medium. Mental processes are abstract and are a judicial exception.
Therefore, due to the reasoning and annotation above, the claim is found to be directed to one or more abstract ideas.
STEP 2A PRONG TWO: NO.
The claim does not recite additional elements that integrate the exception into a practical application of the exception because the claim does not have additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The claim gathers data for variables, uses the nodes (i.e. variables) in a mental process such as drawing relationships between variables, does a second mental process, then the claim does not apply any specific findings to the following mathematical calculations. The claim uses mental processes to imagine relationships between variables then does math with copies of those variables, but once the relationships are formed and the math has concluded, nothing is done. The claim is nothing more than a mental process and mathematical concept.
While the claim recites “prediction of vessel emissions”, “a specific system or function of a vessel”, “of the vessel”, and “for the vessel”, such limitations are not indicative of a practical application. These elements merely link the use of the judicial exception to a particular technological environment. Merely linking the claim to the technological environment of vessel emissions is not indicative of a practical application because the judicial exception is merely utilized to draw relationships between hypothetical ideas, and the results of drawing relationships are not used or relied upon by any other elements in the claim to perform any application beyond simulating a hypothetical scenario.
While the claim recites, “a data processing system”, “a host computing platform comprising one or more computers, each with memory and one or processing units including one or more processing cores”, “a digital twin executing in the memory of a host computer”, “a knowledge graph of nodes stored in the memory”, “a model generation module comprising computer program instructions enabled while executing in the memory of at least one of the processing units of the host computing platform to perform”, and “the data structure”, these are merely the recitation of a general computer recited at a high level of generality. See MPEP 2106.05(f). The computer is invoked merely as a tool for implementing the abstract idea of a mathematical concept or mental process. Such limitations are not indicative of a practical application.
STEP 2B: NO.
Does the claim as a whole amount to significantly more than an abstract idea?
Claims can recite a mathematical concept even if they are claimed as being performed on a computer. The Supreme Court concluded that an algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." Using a computer as a tool to perform math to predict vessel emissions does not recite any additional elements that amount to an inventive concept (“significantly more”) than the recited abstract idea because the claim as a whole simply performs mathematical calculations and occasionally performs a mental process. An example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes.
Therefore, the claim cannot be considered as significantly more than an abstract idea.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 6.
STEP 1: YES. The claim recites: “The system of claim 5, wherein”.
STEP 2A PRONG ONE: YES.
The claim shares the same claim language and analysis of Claim 2.
STEP 2A PRONG TWO: NO.
The claim shares the same claim language and Prong Two analysis of Claim 2.
STEP 2B: NO.
The claim shares the same claim language and Step 2B analysis of Claim 2.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 7.
STEP 1: YES. The claim recites: “The system of claim 5, wherein”.
STEP 2A PRONG ONE: YES.
The claim shares the same claim language and analysis of Claim 3.
STEP 2A PRONG TWO: NO.
The claim shares the same claim language and Prong Two analysis of Claim 3.
STEP 2B: NO.
The claim shares the same claim language and Step 2B analysis of Claim 2.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 8.
STEP 1: YES. The claim recites: “The system of claim 7, wherein”.
STEP 2A PRONG ONE: YES.
The claim shares the same claim language and Prong One analysis of Claim 4.
STEP 2A PRONG TWO: NO.
The claim shares the same claim language and Prong Two analysis of Claim 4.
STEP 2B: NO.
The claim shares the same claim language and Step 2B analysis of Claim 4.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 9.
STEP 1: YES. The claim recites: “A computing device”.
STEP 2A PRONG ONE: YES.
The claim recites “”
loading [observing] a knowledge graph of nodes [mental representation of relationships] variables] encapsulating an identifier [label] for a model representing a specific system or function [mental sub-representation] variables] specifying axes [variable common to two nodes] to related others of the nodes;
applying a reasoner [evaluation] to the nodes to infer additional axes [variables common to two nodes] between selected ones of the nodes; and,
generating the model hierarchy [schematization/categorization] from the knowledge graph [mental representation of relationships] by parsing [understanding/observing] the knowledge graph [mental representation of relationships] to generate [form opinion] an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes [sensemaking/externalization], the data structure defining the model hierarchy [schematization/categorization]
executing a digital twin [digitized modeling] modeling] variables] [schematization/categorization], which is a series of mathematical calculations that calculate numeric values for a concept mentally derived (in mental processes) from interpreting meaning from a categorization and grouping of hierarchically related data. The interpretation of the digital twin as a mathematical model is supported by Paragraph 6 of the instant specification, which discloses, “modelling approaches for digital twins include both white box approaches based upon mathematical models…”. Mathematical models/concepts are abstract and are a judicial exception.
Schematization/categorization is the grouping of disparate concepts and assigning semantic types or hierarchies to them. Sensemaking is structuring information, defining entities, and connecting ambiguous data into a coherent framework. Externalization is the act of moving internal mental concepts, nodes, and relationships out of working mental space and onto a structured, visual medium. Mental processes are abstract and are a judicial exception.
Therefore, due to the reasoning and annotation above, the claim is found to be directed to one or more abstract ideas.
STEP 2A PRONG TWO: NO.
As stated in Step 2A Prong One, the claim does not recite additional elements that integrate the exception into a practical application of the exception because the claim does not have additional elements or a combination of additional elements that apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception.
The claim gathers data for variables, uses the nodes (i.e. variables) in a mental process such as drawing relationships between variables, does a second mental process, then the claim does not apply any specific findings to the following mathematical calculations. The claim uses mental processes to imagine relationships between variables then does math with copies of those variables, but once the relationships are formed and the math has concluded, nothing is done. The claim is nothing more than a mental process and mathematical concept.
While the claim recites “prediction of vessel emissions”, “a specific system or function of a vessel”, “for the vessel”, and “of the vessel”, such limitations are not indicative of a practical application. These elements merely link the use of the judicial exception to a particular technological environment. Merely linking the claim to the technological environment of vessel emissions is not indicative of a practical application because the judicial exception is merely utilized to draw relationships between hypothetical ideas, and the results of drawing relationships are not used or relied upon by any other elements in the claim to perform any application beyond simulating a hypothetical scenario.
While the claim recites, “A computing device comprising a non-transitory computer readable storage medium having program instructions stored therein, the instructions being executable by at least one processing core of a processing unit to cause the processing unit to perform model generation for digital twin prediction of vessel emissions by”, “a knowledge graph of into memory of a host computer”, “a data structure”, and “executing a digital twin in the memory of a host computer”, these are merely the recitation of a general computing device recited at a high level of generality. See MPEP 2106.05(f). The computing device is invoked merely as a tool for implementing the abstract idea of a mathematical concept or mental process. Such limitations are not indicative of a practical application.
STEP 2B: NO.
Does the claim as a whole amount to significantly more than an abstract idea?
Claims can recite a mathematical concept even if they are claimed as being performed on a computer. The Supreme Court concluded that an algorithm could be performed purely mentally even though the claimed procedures "can be carried out in existing computers long in use, no new machinery being necessary." Using a computer as a tool to perform math to predict vessel emissions does not recite any additional elements that amount to an inventive concept (“significantly more”) than the recited abstract idea because the claim as a whole simply performs mathematical calculations and occasionally performs a mental process. An example is Berkheimer v. HP, Inc., 881 F.3d 1360, 125 USPQ2d 1649 (Fed. Cir. 2018), in which the patentee claimed methods for parsing and evaluating data using a computer processing system. The Federal Circuit determined that these claims were directed to mental processes of parsing and comparing data, because the steps were recited at a high level of generality and merely used computers as a tool to perform the processes.
Therefore, the claim cannot be considered as significantly more than an abstract idea.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 10.
STEP 1: YES. The claim recites: “The device of claim 9”.
STEP 2A PRONG ONE: YES.
The claim shares the same claim language and analysis of Claim 2.
STEP 2A PRONG TWO: NO.
The claim shares the same claim language and Prong Two analysis of Claim 2.
STEP 2B: NO.
The claim shares the same claim language and Step 2B analysis of Claim 2.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 11.
STEP 1: YES. The claim recites: “The device of claim 9”.
STEP 2A PRONG ONE: YES.
The claim shares the same claim language and analysis of Claim 3.
STEP 2A PRONG TWO: NO.
The claim shares the same claim language and Prong Two analysis of Claim 3.
STEP 2B: NO.
The claim shares the same claim language and Step 2B analysis of Claim 3.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim 12.
STEP 1: YES. The claim recites: “The device of claim 11”.
STEP 2A PRONG ONE: YES.
The claim shares the same claim language and Prong One analysis of Claim 4.
STEP 2A PRONG TWO: NO.
The claim shares the same claim language and Prong Two analysis of Claim 4.
STEP 2B: NO.
The claim shares the same claim language and Step 2B analysis of Claim 4.
Therefore, the claim is not found eligible under 35 U.S.C. 101.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 5, and 9 are rejected under 35 U.S.C. 103 as being unpatentable over Ma_2023 (A Knowledge Graph-Based Approach to Recommending Low-Carbon Construction Schemes of Bridges, Buildings, 22 May 2023) in view of Dong_2022 (Process knowledge graph modeling techniques and application methods for ship heterogeneous models, Nature, 2022) in view of Loesch_2022 (US 2022/0076151 A1).
Claim 1. Ma_2023 makes obvious “loading a knowledge graph of nodes into a memory of a host computer, each one of the nodes of the knowledge graph encapsulating an identifier for a model representing a specific system or function ” (p. 6, ¶ 3: “the widely used Neo4j graph database… whose data storage structure usually contains nodes and relationships, where nodes are entities in the knowledge graph and each node corresponds to a label to distinguish different entity types, while entities also have their own attributes. Edges are semantic relationships between entities, which are also distinguished by a label and have their own properties”). EXAMINER NOTE: The Neo4j graph database’s data is stored using software on one or more host computer’s memory. EXAMINER NOTE: The nodes of the two knowledge graphs are both used to label different systems or functions, also known as entity types. EXAMINER NOTE: One definition of “axes” in the specification is “a variable common to two nodes” in the field of semantic modeling, defining semantic relationships between entities. “Edges” are semantic relationships between entities, also in the field of semantic modeling. Axes are interpreted as a more generalized term for edges. Therefore, the prior art teaches a specific form of “axes” used on a host computer in the same field.
Ma_2023 makes obvious “applying a reasoner to the nodes to infer additional axes between selected ones of the nodes;” (p. 11, ¶ 2: “The similarity-based construction case recommendation process determines the project engineering information and extracts the key features; retrieves the corresponding construction scheme cases; considers the key features of the scheme corresponding to the entity class; and measures the similarity of the key features of the construction scheme, which calculates the comprehensive similarity of the construction solution based on the constructed bridge construction scheme knowledge graph… when the similarity reaches a threshold, similar cases will be recommended according to their similarity values, a new construction scheme will be generated by modifying and reusing the cases, and the construction scheme knowledge graph can be updated”). NOTE: The entity class is an example of an axis/edge that can be inferred. Additional relationships between nodes are being used by Ma_2023 to update the knowledge graph in the same manner as the reasoner.
Ma_2023 makes obvious “generating a model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a parent or a child dependency upon another of the nodes,” (p. 9, ¶ 3-4: “The comprehensive similarity calculation of construction schemes based on knowledge graphs calculates the similarity of entities and relationships of different construction schemes read from the knowledge graph. The basic process is to obtain the key features of the construction scheme; use the entity matching methods such as attribute matching and neighbor information matching to calculate the entity similarity; obtain the influence weight of each entity class by setting different weight combinations…”).
While Ma_2023 teaches the knowledge graph, reasoner, and model hierarchy within the field of emission reduction in civil engineering, and while the teaching may properly imply “a data structure for each one of the nodes” for the model hierarchy, Ma_2023 does not explicitly teach that the model hierarchy generates a data structure for each node. Ma_2023 does not teach “of vessel emissions” or “for the vessel”.
Dong_2022, however, makes obvious “of vessel emissions” and “for the vessel”. The equivalent of a model hierarchy for the vessel, “(p. 6, ¶ 7: analytic hierarchy”) for a ship, is created from “(p. 6, ¶ 5: the unified semantic expression of the ship heterogeneous model… realized in the process knowledge graph”). NOTE: The ship is considered a vessel.
Dong_2022, however, makes obvious “a data structure for each one of the nodes”. NOTE: Dong_2022 refers to the term ‘reasoner’ as the equivalent term ‘decision-maker’, as seen below. Dong_2022 teaches “generating a model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes, the data structure defining the model hierarchy” (p. 6, ¶ 7: Through the analytic hierarchy process, according to the importance of each factor to the case, each factor is assigned a weight, and then all the local similarities and their respective weights are considered… In the process of using the analytic hierarchy process, a comparison matrix is created based on each input of the decision-maker, which gives the relative importance between the two influencing factors.)” NOTE: Knowledge graphs are defined by sets of nodes in relationships to each other, in this case by axes, which are often called edges. It is common to use “parent-child” relationships to define the relationship as “directed”. The local similarities of Dong_2022 define the knowledge graph with directly related nodes, which means that parent-child relationships are defined. NOTE: This section also teaches “applying a reasoner to the nodes to infer additional axes between selected ones of the nodes” since the construction scheme can be analyzed in different dimensions such as construction environment or construction materials. These dimensions are by definition “variable[s] common to two nodes”, the definition of axes.
Ma_2023 and Dong_2022 are analogous art because they are from the same field of
endeavor of knowledge graphs. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ma_2023 and Dong_2022. The rationale for doing so would have been that it would have been obvious to one of ordinary skill in the art to simply substitute the known element of a ship/vessel as taught by Dong_2022 in substitution of the construction of bridges taught by Ma_2023 to obtain predictable results. See MPEP 2143. The findings in support of this conclusion are:
The prior art contained a method which differed from the claimed device by the substitution of some component with other components as evidenced by the teachings of Ma_2023. Ma_2023 teaches to predict emissions for construction of bridges where systems and functions of the bridge are represented using a knowledge graph. Ma_2023 differs from the claimed invention by teaching a bridge knowledge graph rather than a vessel knowledge graph.
Dong_2022 teaches to represent vessel systems and functions using a knowledge graph. A knowledge graph for a vessel was known in the art.
One of ordinary skill in the art could have substituted the systems and functions of a vessel represented by a knowledge graph for the systems and functions of a bridge represented by a knowledge graph by simply substituting one known element for another. The results would have been predictable: calculated emissions of a vessel rather than a bridge because the substitution simply changes the data contained in a knowledge graph from bridge data to vessel data. Ma_2023 teaches to use data contained in a knowledge graph to calculate emissions for the type of data contained in the knowledge graph. Therefore, it would have been obvious to combine Ma_2023 and Dong_2022 to have the knowledge graph contain vessel data then it would predictably calculate vessel emissions instead.
Ma_2023 and Dong_2022 do not explicitly teach a “digital twin”.
Loesch_2022 teaches “a model generation method for digital twin prediction of vessel emissions, the method comprising:” and “executing a digital twin in the memory of the host computer, the digital twin simulating (¶ 11: heterogeneous data from various interfaces can be integrated and abstracted, and graph-based structure 20 can act as a basis for a preparation of data for digital twin 40.”) NOTE: Heterogeneous data from various interfaces can include elements modeled within the model hierarchy and can include vessel emissions or estimated elements of a modeled vessel. Loesch_2022 teaches the digital twin “based upon values sensed or estimated for elements (¶ 0004: “The graph-based structure is designed to receive data from the interface and is designed to integrate received data into the conceptual model and/or into the data instances… In addition, the computer-implemented system includes at least one digital twin that is designed to draw data from the graph-based structure and/or to provide data to the graph-based structure.”).
In addition to Ma_2023’s teaching of a memory of a host computer, Loesch_2022 also teaches the method of “loading… into memory of a host computer” (¶ 0030: “The described method logic can be stored in the form of executable code in at least one memory and executed by the at least one processor”).
Dong_2022 and Loesch_2022 are analogous art because they are from the same field of endeavor called semantic modeling. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Dong_2022 and Loesch_2022. The rationale for doing so would have been that Dong_2022 teaches “the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship” while Loesch_2022 teaches a “computer-implemented system and… computer-implemented method for instantiating at least one digital twin in industrial environments”, such as vessel emissions. Therefore, it would have been obvious to combine Dong_2022 and Loesch_2022 for the benefit of generating a model for digital twin prediction of vessel emissions to obtain the invention specified in the claims.
Claim 5. Ma_2023 makes obvious “A data processing system adapted for model generation for digital twin prediction of vessel emissions, the system comprising: a host computing platform comprising one or more computers, each with memory and one or more processing units including one or more processing cores; … a knowledge graph of nodes stored in the memory, each one of the nodes of the knowledge graph encapsulating an identifier for a model representing a specific system or function ” (p. 6, ¶ 3: “the widely used Neo4j graph database… whose data storage structure usually contains nodes and relationships, where nodes are entities in the knowledge graph and each node corresponds to a label to distinguish different entity types, while entities also have their own attributes. Edges are semantic relationships between entities, which are also distinguished by a label and have their own properties”). EXAMINER NOTE: The Neo4j graph database’s data is stored using software on one or more host computer’s memory. The Neo4j graph database is comprised of computer program instructions using a data processing system or host computing platform with at least one processing core. EXAMINER NOTE: The nodes of the two knowledge graphs are both used to label different systems or functions, also known as entity types. EXAMINER NOTE: One definition of “axes” in the specification is “a variable common to two nodes” in the field of semantic modeling, defining semantic relationships between entities. “Edges” are semantic relationships between entities, also in the field of semantic modeling. Axes are interpreted as a more generalized term for edges. Therefore, the prior art teaches a specific form of “axes” used on a host computer in the same field.
Ma_2023 makes obvious “applying a reasoner to the nodes to infer additional axes between selected ones of the nodes; and,” (p. 11, ¶ 2: “The similarity-based construction case recommendation process determines the project engineering information and extracts the key features; retrieves the corresponding construction scheme cases; considers the key features of the scheme corresponding to the entity class; and measures the similarity of the key features of the construction scheme, which calculates the comprehensive similarity of the construction solution based on the constructed bridge construction scheme knowledge graph… when the similarity reaches a threshold, similar cases will be recommended according to their similarity values, a new construction scheme will be generated by modifying and reusing the cases, and the construction scheme knowledge graph can be updated”). NOTE: The entity class is an example of an axis/edge that can be inferred. Additional relationships between nodes are being used by Ma_2023 to update the knowledge graph in the same manner as the reasoner.
Ma_2023 makes obvious “generating the model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a parent or a child dependency upon another of the nodes,” (p. 9, ¶ 3-4: “The comprehensive similarity calculation of construction schemes based on knowledge graphs calculates the similarity of entities and relationships of different construction schemes read from the knowledge graph. The basic process is to obtain the key features of the construction scheme; use the entity matching methods such as attribute matching and neighbor information matching to calculate the entity similarity; obtain the influence weight of each entity class by setting different weight combinations…”).
While Ma_2023 teaches the knowledge graph, reasoner, and model hierarchy within the field of emission reduction in civil engineering, and while the teaching may properly imply “the data structure defining the model hierarchy”, Ma_2023 does not explicitly teach that the model hierarchy generates a data structure for each node. Ma_2023 does not teach “of vessel emissions” or “for the vessel”.
Dong_2022, however, makes obvious “of vessel emissions” and “for the vessel”. The equivalent of a model hierarchy for the vessel, “(p. 6, ¶ 7: analytic hierarchy”) for a ship, is created from “(p. 6, ¶ 5: the unified semantic expression of the ship heterogeneous model… realized in the process knowledge graph”). NOTE: The ship is considered a vessel.
Dong_2022, however, makes obvious “a data structure for each one of the nodes… the data structure defining the model hierarchy”. NOTE: Dong_2022 refers to the term ‘reasoner’ as the equivalent term ‘decision-maker’, as seen below. Dong_2022 teaches “generating a model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes, the data structure defining the model hierarchy” (p. 6, ¶ 7: Through the analytic hierarchy process, according to the importance of each factor to the case, each factor is assigned a weight, and then all the local similarities and their respective weights are considered… In the process of using the analytic hierarchy process, a comparison matrix is created based on each input of the decision-maker, which gives the relative importance between the two influencing factors.)” NOTE: Knowledge graphs are defined by sets of nodes in relationships to each other, in this case by axes, which are often called edges. It is common to use “parent-child” relationships to define the relationship as “directed”. The local similarities of Dong_2022 define the knowledge graph with directly related nodes, which means that parent-child relationships are defined. NOTE: This section also teaches “applying a reasoner to the nodes to infer additional axes between selected ones of the nodes” since the construction scheme can be analyzed in different dimensions such as construction environment or construction materials. These dimensions are by definition “variable[s] common to two nodes”, the definition of axes.
Ma_2023 and Dong_2022 are analogous art because they are from the same field of
endeavor of knowledge graphs. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ma_2023 and Dong_2022. The rationale for doing so would have been that it would have been obvious to one of ordinary skill in the art to simply substitute the known element of a ship/vessel as taught by Dong_2022 in substitution of the construction of bridges taught by Ma_2023 to obtain predictable results. See MPEP 2143. The findings in support of this conclusion are:
The prior art contained a method which differed from the claimed device by the substitution of some component with other components as evidenced by the teachings of Ma_2023. Ma_2023 teaches to predict emissions for construction of bridges where systems and functions of the bridge are represented using a knowledge graph. Ma_2023 differs from the claimed invention by teaching a bridge knowledge graph rather than a vessel knowledge graph.
Dong_2022 teaches to represent vessel systems and functions using a knowledge graph. A knowledge graph for a vessel was known in the art.
One of ordinary skill in the art could have substituted the systems and functions of a vessel represented by a knowledge graph for the systems and functions of a bridge represented by a knowledge graph by simply substituting one known element for another. The results would have been predictable: calculated emissions of a vessel rather than a bridge because the substitution simply changes the data contained in a knowledge graph from bridge data to vessel data. Ma_2023 teaches to use data contained in a knowledge graph to calculate emissions for the type of data contained in the knowledge graph. Therefore, it would have been obvious to combine Ma_2023 and Dong_2022 to have the knowledge graph contain vessel data then it would predictably calculate vessel emissions instead.
Ma_2023 and Dong_2022 do not explicitly teach a “digital twin”.
Loesch_2022 teaches “a data processing system adapted for model generation for digital twin prediction of vessel emissions, the system comprising:” and “a digital twin executing in the memory and simulating (¶ 11: heterogeneous data from various interfaces can be integrated and abstracted, and graph-based structure 20 can act as a basis for a preparation of data for digital twin 40.”) NOTE: Heterogeneous data from various interfaces can include elements modeled within the model hierarchy and can include vessel emissions or estimated elements of a modeled vessel. Loesch_2022 teaches the digital twin “based upon values sensed or estimated for elements model hierarchy (¶ 0004: “The graph-based structure is designed to receive data from the interface and is designed to integrate received data into the conceptual model and/or into the data instances… In addition, the computer-implemented system includes at least one digital twin that is designed to draw data from the graph-based structure and/or to provide data to the graph-based structure.”).
In addition to Ma_2023’s teaching of a memory of a host computing platform, Loesch_2022 also teaches the method of “loading… into memory of a host computer” (¶ 0030: “The described method logic can be stored in the form of executable code in at least one memory and executed by the at least one processor”).
Dong_2022 and Loesch_2022 are analogous art because they are from the same field of endeavor called semantic modeling. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Dong_2022 and Loesch_2022. The rationale for doing so would have been that Dong_2022 teaches “the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship” while Loesch_2022 teaches a “computer-implemented system and… computer-implemented method for instantiating at least one digital twin in industrial environments”, such as vessel emissions. Therefore, it would have been obvious to combine Dong_2022 and Loesch_2022 for the benefit of generating a model for digital twin prediction of vessel emissions to obtain the invention specified in the claims.
Claim 9. Ma_2023 makes obvious “A computing device comprising a non-transitory machine readable storage medium having program instructions therein, the instructions being executable by at least one processing core of a processing unit to cause the processing unit to perform a model generation for digital twin prediction of vessel emissions by: loading a knowledge graph of nodes into memory of a host computer, each one of the nodes of the knowledge graph encapsulating an identifier for a model representing a specific system or function (p. 6, ¶ 3: “the widely used Neo4j graph database… whose data storage structure usually contains nodes and relationships, where nodes are entities in the knowledge graph and each node corresponds to a label to distinguish different entity types, while entities also have their own attributes. Edges are semantic relationships between entities, which are also distinguished by a label and have their own properties”). EXAMINER NOTE: The Neo4j graph database’s data is stored using software on one or more host computer’s memory. The Neo4j graph database is comprised of computer program instructions using a data processing system or host computing platform with at least one processing core. EXAMINER NOTE: It is assumed that “memory of a host computer” is referring to the non-transitory computer readable storage medium to give antecedent basis for the memory. EXAMINER NOTE: The nodes of the two knowledge graphs are both used to label different systems or functions, also known as entity types. EXAMINER NOTE: One definition of “axes” in the specification is “a variable common to two nodes” in the field of semantic modeling, defining semantic relationships between entities. “Edges” are semantic relationships between entities, also in the field of semantic modeling. Axes are interpreted as a more generalized term for edges. Therefore, the prior art teaches a specific form of “axes” used on a host computing device in the same field.
Ma_2023 makes obvious “applying a reasoner to the nodes to infer additional axes between selected ones of the nodes; and,” (p. 11, ¶ 2: “The similarity-based construction case recommendation process determines the project engineering information and extracts the key features; retrieves the corresponding construction scheme cases; considers the key features of the scheme corresponding to the entity class; and measures the similarity of the key features of the construction scheme, which calculates the comprehensive similarity of the construction solution based on the constructed bridge construction scheme knowledge graph… when the similarity reaches a threshold, similar cases will be recommended according to their similarity values, a new construction scheme will be generated by modifying and reusing the cases, and the construction scheme knowledge graph can be updated”). NOTE: The entity class is an example of an axis/edge that can be inferred. Additional relationships between nodes are being used by Ma_2023 to update the knowledge graph in the same manner as the reasoner.
Ma_2023 makes obvious “generating the model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a parent or a child dependency upon another of the nodes,” (p. 9, ¶ 3-4: “The comprehensive similarity calculation of construction schemes based on knowledge graphs calculates the similarity of entities and relationships of different construction schemes read from the knowledge graph. The basic process is to obtain the key features of the construction scheme; use the entity matching methods such as attribute matching and neighbor information matching to calculate the entity similarity; obtain the influence weight of each entity class by setting different weight combinations…”).
While Ma_2023 teaches the knowledge graph, reasoner, and model hierarchy within the field of emission reduction in civil engineering, and while the teaching may properly imply “the data structure defining the model hierarchy”, Ma_2023 does not explicitly teach that the model hierarchy generates a data structure for each node. Ma_2023 does not teach “of vessel emissions” or “for the vessel”.
Dong_2022, however, makes obvious “of vessel emissions” and “for the vessel”. The equivalent of a model hierarchy for the vessel, “(p. 6, ¶ 7: analytic hierarchy”) for a ship, is created from “(p. 6, ¶ 5: the unified semantic expression of the ship heterogeneous model… realized in the process knowledge graph”). NOTE: The ship is considered a vessel.
Dong_2022, however, makes obvious “a data structure for each one of the nodes… the data structure defining the model hierarchy”. NOTE: Dong_2022 refers to the term ‘reasoner’ as the equivalent term ‘decision-maker’, as seen below. Dong_2022 teaches “generating a model hierarchy from the knowledge graph by parsing the knowledge graph to generate an entry in a data structure for each one of the nodes, and also an entry indicating a parent or a child dependency upon another of the nodes, the data structure defining the model hierarchy” (p. 6, ¶ 7: Through the analytic hierarchy process, according to the importance of each factor to the case, each factor is assigned a weight, and then all the local similarities and their respective weights are considered… In the process of using the analytic hierarchy process, a comparison matrix is created based on each input of the decision-maker, which gives the relative importance between the two influencing factors.)” NOTE: Knowledge graphs are defined by sets of nodes in relationships to each other, in this case by axes, which are often called edges. It is common to use “parent-child” relationships to define the relationship as “directed”. The local similarities of Dong_2022 define the knowledge graph with directly related nodes, which means that parent-child relationships are defined. NOTE: This section also teaches “applying a reasoner to the nodes to infer additional axes between selected ones of the nodes” since the construction scheme can be analyzed in different dimensions such as construction environment or construction materials. These dimensions are by definition “variable[s] common to two nodes”, the definition of axes.
Ma_2023 and Dong_2022 are analogous art because they are from the same field of
endeavor of knowledge graphs. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ma_2023 and Dong_2022. The rationale for doing so would have been that it would have been obvious to one of ordinary skill in the art to simply substitute the known element of a ship/vessel as taught by Dong_2022 in substitution of the construction of bridges taught by Ma_2023 to obtain predictable results. See MPEP 2143. The findings in support of this conclusion are:
The prior art contained a method which differed from the claimed device by the substitution of some component with other components as evidenced by the teachings of Ma_2023. Ma_2023 teaches to predict emissions for construction of bridges where systems and functions of the bridge are represented using a knowledge graph. Ma_2023 differs from the claimed invention by teaching a bridge knowledge graph rather than a vessel knowledge graph.
Dong_2022 teaches to represent vessel systems and functions using a knowledge graph. A knowledge graph for a vessel was known in the art.
One of ordinary skill in the art could have substituted the systems and functions of a vessel represented by a knowledge graph for the systems and functions of a bridge represented by a knowledge graph by simply substituting one known element for another. The results would have been predictable: calculated emissions of a vessel rather than a bridge because the substitution simply changes the data contained in a knowledge graph from bridge data to vessel data. Ma_2023 teaches to use data contained in a knowledge graph to calculate emissions for the type of data contained in the knowledge graph. Therefore, it would have been obvious to combine Ma_2023 and Dong_2022 to have the knowledge graph contain vessel data then it would predictably calculate vessel emissions instead.
Ma_2023 and Dong_2022 do not explicitly teach a “digital twin”.
Loesch_2022 teaches “a data processing system adapted for model generation for digital twin prediction of vessel emissions, the system comprising:” and “a digital twin executing in the memory and simulating (¶ 11: heterogeneous data from various interfaces can be integrated and abstracted, and graph-based structure 20 can act as a basis for a preparation of data for digital twin 40.”) NOTE: Heterogeneous data from various interfaces can include elements modeled within the model hierarchy and can include vessel emissions or estimated elements of a modeled vessel. Loesch_2022 teaches the digital twin “based upon values sensed or estimated for elements (¶ 0004: “The graph-based structure is designed to receive data from the interface and is designed to integrate received data into the conceptual model and/or into the data instances… In addition, the computer-implemented system includes at least one digital twin that is designed to draw data from the graph-based structure and/or to provide data to the graph-based structure.”).
In addition to Ma_2023’s teaching of a memory of a host computing platform, Loesch_2022 also teaches the method of “loading… into memory of a host computer” (¶ 0030: “The described method logic can be stored in the form of executable code in at least one memory and executed by the at least one processor”).
Dong_2022 and Loesch_2022 are analogous art because they are from the same field of endeavor called semantic modeling. Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Dong_2022 and Loesch_2022. The rationale for doing so would have been that Dong_2022 teaches “the method can effectively acquire the process knowledge in the design case and improve the efficiency and intelligence of knowledge reuse in the process design of the heterogeneous model of a ship” while Loesch_2022 teaches a “computer-implemented system and… computer-implemented method for instantiating at least one digital twin in industrial environments”, such as vessel emissions. Therefore, it would have been obvious to combine Dong_2022 and Loesch_2022 for the benefit of generating a model for digital twin prediction of vessel emissions to obtain the invention specified in the claims.
Claims 2, 6, and 10 are rejected under 35 U.S.C. 103 as being unpatentable over Ma_2023 in view of Dong_2022 in view of Loesch_2022 in view of Masuda_2019 (US 2019/0266295 A1).
Claim 2: Ma_2023 makes obvious “The method of claim 1, further comprising: changing the knowledge graph by changing at least one of the nodes and a corresponding one of the axes; regenerating the model hierarchy according to the changed knowledge graph;” (p. 11, ¶ 2: “a new construction scheme will be generated by modifying and reusing the cases, and the construction scheme knowledge graph can be updated. The specific process is shown in Figure 5”). NOTE: This demonstrates a loop to update the model (model hierarchy) based on the knowledge graph. Ma_2023 teaches this loop in Figure 5.
Ma_2023 does not explicitly teach “and, re-executing the digital twin with the regenerated model hierarchy”.
Masuda_2019, however, makes obvious re-executing the digital twin as the underlying data changes (¶ 0024: “executing one or more digital simulations that include the digital twin operating in simulated conditions that are the same as or similar to those described by the route data (these simulations may be periodically or continually re-executed based on the receipt of one or more of the following: new onboard data; new measured data; and new route data)”). Masuda_2019 further illustrates (¶ 0009: The method where multiple instances of the digital data are received over time as part of a feedback loop and the digital twin is recursively updated based on the digital data received in the feedback loop”) in analogous means to the loop demonstrated in Claim 2.
Ma_2023 and Masuda_2019 are analogous art because they are from the same field of proposing design and application of recommendation systems in the engineering field. Before the effective filing date, it would have been obvious to one of ordinary skill in the art to combine Masuda_2019 and Ma_2023. One rationale for combining the two prior arts, as stated in Ma_2023, would have been to (p. 19, ¶ 3: “provide a reference for the design and application of recommendation systems in the engineering field”). Both references are concerned with repairs as well. Additionally, Masuda_2019 teaches that digital twins for vehicles, such as vessels, were known in the art. Therefore, it would have been obvious to combine regenerating model hierarchy and the changed knowledge graphs of Ma_2023 with re-executing the overlaid digital twin of Masuda_2019 for the benefit of simulating a vehicle (vessel) to make improvement predictions.
Claim 6. Regarding claim 6, the limitations described are substantially the same as the limitations described in Claim 2. As such, Claim 6 is rejected under the same rationale as Claim 2.
Claim 10. Regarding claim 10, the limitations described are substantially the same as the limitations described in Claim 2. As such, Claim 10 is rejected under the same rationale as Claim 2.
Claims 3-4, 7-8, and 11-12 are rejected under 35 U.S.C. 103 as being unpatentable over Ma_2023 in view of Dong_2022 in view of Loesch_2022 in view of Liu_2023 (Integrated Life Cycle Analysis of Cost and CO2 Emissions from Vehicles and Construction Work Activities in Highway Pavement Service Life, Atmosphere, 2023).
Claim 3. While Ma_2023 and Dong_2022 and Loesch_2022 teach “the method of claim 1, wherein the simulation of emissions comprises… in operation of the vessel”, they do not explicitly teach changing values or nodes in a vehicle model affecting the estimation of emissions value.
Liu_2023 makes obvious “a practical method to estimate the economic and environmental impact of vehicle… activities… To achieve this, we integrated two key life cycle analysis methods, life cycle assessment (LCA) and life cycle cost analysis (LCCA)” in the abstract. Liu_2023 makes obvious “(p. 7, ¶ 2: “For RRiwvoc, RDivoc, RRiqvoc and RNivoc, the fuel consumption rates of vehicle type i were obtained based on a meso-fleet-based model with advantages in the estimation of CO2 emissions…”). Table 1 of Liu_2023 illustrates setting some value [an application of a setting of one or more nodes of the model hierarchy] that affects the emissions value produced in operation of the vehicle [affecting the estimation of an emissions value produced in operation of the vessel]. For instance, changing the type of vehicle in the variable i affects estimated emission rate.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ma_2023, Dong_2022, Loesch_2022, and Liu_2023, which are of the same field of endeavor of modeling or simulating for efficiency improvement in civil engineering. The rationale for doing so would have been to narrow Liu_2023’s life cycle assessment (LCA) and life cycle cost analysis (LCCA) into vessels (instead of all vehicles). One motivation for combining these prior arts is given in Liu_2023: “(p. 3, ¶ 2: “An understanding of the relationship between monetary costs and greenhouse gas emission (GHGs) has been gradually developed in recent studies”). Therefore, it would have been obvious to combine Ma_2023, Dong_2022, Loesch_2022, and Liu_2023 to reduce emissions of a vessel or vehicle to reduce monetary costs.
Claim 4. While Ma_2023 in view of Dong_2022 in view of Loesch_2022 teaches all the limitations of claim 1, the method of claim 3, the digital twin and a model hierarchy, Liu_2023 makes obvious “” in “(p. 7: Table 1. The calculation methods of user costs and user CO2 emissions used in this study.” For instance, the Vehicle Operating Cost (VOC), represented by the variable VOCusage, which represents the emissions value, is achieved based upon a summation (i.e., an aggregation) of cost values of the model. Liu_2023 teaches achieving an emissions value based upon an aggregation of cost values of a model.
Before the effective filing date, it would have been obvious to a person of ordinary skill in the art to combine Ma_2023, Dong_2022, Loesch_2022, and Liu_2023 are of the same field of endeavor of modeling or simulating, with three of the four having the specific goal of reducing carbon emissions. The rationale for doing so would have been to narrow Liu_2023’s life cycle assessment (LCA) and life cycle cost analysis (LCCA) into vessels (instead of all vehicles). One motivation for combining these prior arts is given in Liu_2023: “(p. 3, ¶ 2: “An understanding of the relationship between monetary costs and greenhouse gas emission (GHGs) has been gradually developed in recent studies”). Therefore, it would have been obvious to combine Ma_2023 and Dong_2022 and Loesch_2022 with Liu_2023 for the benefit of simulating the cost of achieving an emissions value as specified in the claims to reduce emissions of a vessel or vehicle to reduce monetary costs.
Claim 7. Regarding claim 7, the limitations described are substantially the same as the limitations described in Claim 3. As such, Claim 7 is rejected under the same rationale as Claim 3.
Claim 8. Regarding claim 8, the limitations described are substantially the same as the limitations described in Claim 4. As such, Claim 8 is rejected under the same rationale as Claim 4.
Claim 11. Regarding claim 11, the limitations described are substantially the same as the limitations described in Claim 3. As such, Claim 11 is rejected under the same rationale as Claim 3.
Claim 12. Regarding claim 12, the limitations described are substantially the same as the limitations described in Claim 4. As such, Claim 12 is rejected under the same rationale as Claim 4.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
US 11,898,934 B1 is a patent filed Sep. 28, 2022, and granted on Feb. 13, 2024. The prior art creates a model, reasons into a best-performance model, and integrates the model into a computed digital twin with the goal of predicting vessel emissions. The new model is used in integrating into a new performance model, effectively regenerating and re-executing a digital twin..
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/B.R.B./Examiner, Art Unit 2187
/EMERSON C PUENTE/Supervisory Patent Examiner, Art Unit 2187
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