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 .
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 4-10, 16-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 4-10 and 16-20 are recites the limitation "the intention model". There is insufficient antecedent basis for this limitation in the claim.
For the purpose of examination, intention model will correspond to the model claimed in claim 1 and 13.
Claim Rejections - 35 USC § 102
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 4-6, 8-13, 16-18 and 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Varro et al (US PUB. 20230281486, herein Varro).
Regarding claim 1, Varro teaches A support system for defining a model representing at least part of an industrial automation system, wherein the support system is configured to:
obtain a user-provided definition of at least part of the model (0014 “enhancing an engineering tool by incorporating an AI-based assistant feature that learns contextual classification of components in a computer aided design project and presents functionality information to a user via a graphical user interface (GUI) concurrently with operating an engineering tool that models an industrial system for the design project”
process the user-provided definition to verify correctness or completeness of the at least part of the model with reference to at least one knowledge base (0014 “Additional assistance may be presented in the form of recommendations to the user relative to design elements, such as a notification that elements may be missing, or for determining technical parameters for the design. An AI module and/or inference engine may analyze the knowledge graph, construct functional clusters that reflect elements related to one another by a common functionality, and formulate the recommendations to the user during the design process”);
and in response to determining that the user-provided definition comprises incorrect or incomplete information, provide system output, wherein the system output comprises a prompt to the user to provide correct or complete information for inclusion in the definition of the at least part of the model (0014 “Additional assistance may be presented in the form of recommendations to the user relative to design elements, such as a notification that elements may be missing, or for determining technical parameters for the design. An AI module and/or inference engine may analyze the knowledge graph, construct functional clusters that reflect elements related to one another by a common functionality, and formulate the recommendations to the user during the design process”).
Regarding claim 4, the cited prior art teach The support system of claim 1.
Varro teaches further configured to provide a user interface to enable the user to define the at least part of the intention model, wherein the user interface comprises a natural-language user interface or a graphical user interface (0017 “AI module 140 is configured to apply a classification technique (e.g., machine learning classification, natural language processing, pattern matching, data-flow analysis) to find interconnected components, classify the component functionality and tag the component accordingly. From this functionality classification, AI module 140 can provide functionality based recommendations to the user through the AI-based assistant. In an embodiment, the AI module 140 includes a machine learning-based network trained by supervised or unsupervised training techniques to generate a machine learning model that can recognize the different parts of the project based on the connections and the properties of elements in the knowledge graph 150 for the current project. A data-driven approach may involve a machine learning model that is trained on labeled historical data 155. Such a model can be trained using graph clustering, graph/node classification or other classification models. Once trained, AI module 140 is used to analyze knowledge graphs 150 to classify components in a design project with contextualization according to functionality. Functionality context information can be presented to a user in the AI-based assistant feature at GUI 151 during a design project. In an aspect, AI module 124 interfaces as a client with a server-based AI module 140 over network 130 to conserve local memory, operating as a cloud-based AI scheme. In some embodiments, local AI module 124 may be implemented as an independent machine learning-based network capable of locally performing the tasks as described for the AI module 140 either entirely or in part as a shared role for the AI-based assistant feature.”).
Regarding claim 5, the cited prior art teach The support system of claim 1.
Varro teaches further configured to execute an intention model verification algorithm for verifying the correctness or completeness of the at least part of the intention model (0019 “Inference engine 122 may also provide engineering validation feedback to the AI-based assistant displayed on GUI 151. In an embodiment, rules database 132 stores policies, standards and/or regulations for engineering design components. During configuration for a current design project, mapping engine 123 may be used to extract relevant policies, standards and/or regulations information, received from the user or extracted from the rules database 132 and/or documents of information sources, and map the extracted information to design elements of the historical data 155 to define rules-based relationships to project ontology, then stores the rules in rule database 132. During a validation for a running design project, inference engine 122 extracts relevant rules from the rules databased and applies the rules to new project data observed in knowledge graph 150 of the current project, seeking any discrepancy as a potential violation of policy, standards and/or regulations. Discrepancies may be reported to the user through the AI-based assistant feature”).
Regarding claim 6, the cited prior art teach The support system of claim 5.
Varro teaches wherein the intention model verification algorithm comprises traversing a graph structure of the at least part of the intention model to identify incorrect or incomplete information (0016 “knowledge graph 150 represents the ontology as nodes and edges that correspond to a set of elements of the ontology and element relationships, respectively. An archive of knowledge graphs 150 may be accumulated over the course of many engineering design projects and stored as historical data 155. In an aspect, the ontologies used to create the knowledge graphs may be stored as indexed tables in historical data”).
Regarding claim 8, the cited prior art teach The support system of claim 1.
Varro teaches further configured to obtain supplementary input from the user comprising the correct or complete information, and to incorporate the supplementary information into the definition of the at least part of the intention model (0027 “the system configuration phase involves a mapping engine 423 performing an automatic extraction of industry standards, regulations and policies from documents 407 and/or rules database 232. Such information can be fed to mapping engine 423 in a configuration phase by determining standards and policies that are relevant to planned engineering projects. For example, extraction of information from documents 407 may be implemented by text mining, optical character recognition (OCR), NLP, and/or other similar language processing algorithms (e.g., processing scanned documents or electronic file versions of documents). In an aspect, the mapping of standards, policy, and/or regulation information is based on extracted historical project data 405. In an aspect, mapping engine 423 may receive input 431 from user interface 230 in the form of modifications or addition of industry standards, policies or regulation information stored in rules database 232 which can be stored in the rules database 232 based on the ontology. In some embodiments, rule-sets can be predefined and included with the ontology acquired when configuring the knowledge graph 250 during the configuration phase as described for classification in FIG. 2”).
Regarding claim 9, the cited prior art teach The support system of claim 1.
Varro teaches further comprising autosuggest functionality configured to suggest information to be included in the definition of the at least part of the intention model (0014 “Additional assistance may be presented in the form of recommendations to the user relative to design elements, such as a notification that elements may be missing, or for determining technical parameters for the design. An AI module and/or inference engine may analyze the knowledge graph, construct functional clusters that reflect elements related to one another by a common functionality, and formulate the recommendations to the user during the design process”).
Regarding claim 10, the cited prior art teach The support system of claim 9.
Varro teaches wherein the autosuggest functionality is configured to use historical data to provide suggestions (0027 “the system configuration phase involves a mapping engine 423 performing an automatic extraction of industry standards, regulations and policies from documents 407 and/or rules database 232. Such information can be fed to mapping engine 423 in a configuration phase by determining standards and policies that are relevant to planned engineering projects. For example, extraction of information from documents 407 may be implemented by text mining, optical character recognition (OCR), NLP, and/or other similar language processing algorithms (e.g., processing scanned documents or electronic file versions of documents). In an aspect, the mapping of standards, policy, and/or regulation information is based on extracted historical project data 405. In an aspect, mapping engine 423 may receive input 431 from user interface 230 in the form of modifications or addition of industry standards, policies or regulation information stored in rules database 232 which can be stored in the rules database 232 based on the ontology. In some embodiments, rule-sets can be predefined and included with the ontology acquired when configuring the knowledge graph 250 during the configuration phase as described for classification in FIG. 2”, 0014 “Additional assistance may be presented in the form of recommendations to the user relative to design elements, such as a notification that elements may be missing, or for determining technical parameters for the design. An AI module and/or inference engine may analyze the knowledge graph, construct functional clusters that reflect elements related to one another by a common functionality, and formulate the recommendations to the user during the design process”).
Regarding claim 11, the cited prior art teach The support system of claim 1.
Varro teaches further comprising a machine learned model trained to identify incorrect or incomplete information in the definition of the at least part of the intention model (0024).
Regarding claim 12, the cited prior art teach The support system of claim 1.
Varro teaches further comprising a machine learned model trained to converse with the user via a natural language user interface (0021 0024).
Claims 13, 16-18 and 20 are rejected using similar reasoning as the rejection of claims 1, 4-6, 8-12 due to reciting similar limitations but directed towards a method.
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.
Claim(s) 2-3, is/are rejected under 35 U.S.C. 103 as being unpatentable over Varro et al (US PUB. 20230281486, herein Varro) in view of Johnson et al (US PUB. 20090216982, herein Johnson).
Regarding claim 2, the cited prior art teach The support system of claim 1.
The cited prior art do not teach further configured to trigger the verification in response to the absence of any user input over a predetermined time period.
Johnson teaches further configured to trigger the verification in response to the absence of any user input over a predetermined time period (0027 “Read/write operations to the storage media 104 are blocked until the self-locking mass-storage system 100 receives an authentication 404 such as a password entered through the software application 302, which sets the self-locking mass-storage system 100 into an unlocked state 406. In alternate embodiments of the invention, the authentication 404 may be a Personal Identification Number (PIN) entered through an input device, or a biometric signature or pattern entered through a biometric reader”, 0030 “providing storage media and an inactivity timer in a block 502, timing a period of read/write inactivity of the storage media using the inactivity timer in a block 504, comparing the period of read/write inactivity against a preset maximum idle time in a block 506, locking access to the storage media when the period of read/write inactivity exceeds a preset maximum idle time in a block 508, and resetting the period of read/write inactivity following read/write activity while the self-locking mass-storage system is in an unlocked state in a block 510.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Varro with the teachings of Johnson since Johnson teaches a means for secure computer use (0003 0004).
Regarding claim 3, the cited prior art teach The support system of claim 1.
The cited prior art do not teach further configured to trigger the verification sporadically or periodically.
Johnson teaches further configured to trigger the verification sporadically or periodically (0027 “Read/write operations to the storage media 104 are blocked until the self-locking mass-storage system 100 receives an authentication 404 such as a password entered through the software application 302, which sets the self-locking mass-storage system 100 into an unlocked state 406. In alternate embodiments of the invention, the authentication 404 may be a Personal Identification Number (PIN) entered through an input device, or a biometric signature or pattern entered through a biometric reader”, 0030 “providing storage media and an inactivity timer in a block 502, timing a period of read/write inactivity of the storage media using the inactivity timer in a block 504, comparing the period of read/write inactivity against a preset maximum idle time in a block 506, locking access to the storage media when the period of read/write inactivity exceeds a preset maximum idle time in a block 508, and resetting the period of read/write inactivity following read/write activity while the self-locking mass-storage system is in an unlocked state in a block 510.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Varro with the teachings of Johnson since Johnson teaches a means for secure computer use (0003 0004).
Claims 14-15 are rejected using similar reasoning as the rejection of claims 2-3 due to reciting similar limitations but directed towards a method.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Varro et al (US PUB. 20230281486, herein Varro) in view of Karanam et al (US PUB. 20200074334).
Regarding claim 7, the cited prior art teach The support system of claim 5.
Karanam teaches wherein the intention model verification algorithm is configured to identify incorrect or incomplete information by comparing an ABox representation of the intention model provided by the user with a TBox representation (0033, 0036 “Consistency checking includes checking whether all of the terminological (TBox) and assertion (ABox) axioms are consistent with the symbolic knowledge base 145. Identifying consistency of TBox axioms includes checking if all of the terminological concepts and their relationships do not contradict each other. Identifying consistency of ABox axioms includes determining that the ABox axioms are adhering to the TBox definitions and not creating any contradictions. For example, assuming there is a TBox definition that states that only females can be mothers, and a person X is defined as being male (Male(X)), if a fact is added which states that X is a mother of Y (Mother(X,Y)), then this fact cannot be deemed consistent since a male person cannot be a mother as per the TBox definition. Consistency checks discover these and other kinds of discrepancies in the terminological and instance (assertion) data.”).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the instant application to have modified the teachings of Varro with the teachings of Karanam since Karanam teaches a means for ensuring consistency (0036 0033).
Claims 19 is rejected using similar reasoning as the rejection of claims 7 due to reciting similar limitations but directed towards a method.
Conclusion
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/TAMEEM D SIDDIQUEE/
Primary Examiner
Art Unit 2116
Prosecution Insights