Detailed Action
Status of Claims
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
This Action is in reply to the Amendment filed on 3/12/2026. Claims 4-6, 8-13, 15-20, 22-23 are currently pending and have been examined. Claims 1-3, 7, 14, 21 stand cancelled. Claims 4, 11, and 18 have been amended. Claim amendments have overcome the 112 rejections.
Request for Continued Examination
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 3/12/2026 has been entered.
Priority
Applicant’s claim of priority to 16710561 and provisional 63270458 is acknowledged. These applications do not provide support for at least the steps of the machine learning model comprising a semantic knowledge graph representing interconnections between the plurality of regulations and the plurality of products; accessing, by the at least one processor, information associated with a product with which compliance to a regulation is not known, wherein the information indicates that the product (i) is a new model of an existing product and (ii) comprises at least one new component that is not included in the existing product; analyzing, by the at least one processor using the machine learning model, the information associated with the product to predict whether the product complies with the regulation, wherein the analyzing comprises: querying the semantic knowledge graph to match the product to the regulation based on the information associated with the product, and evaluating potential certification performance of the product based on past certification results associated with the existing product. The claims therefore are given an effective filing date of 9/19/2022.
Claim Rejection - 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 4-6, 8-13, 15-20, and 22-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
First, it is determined whether the claims are directed to a statutory category of invention. In the instant case, claims 4-6, 8-10 are directed to a process, claims 11-13, 15-17 are directed to a machine, and claims 18-20, 22-23 are directed to an article of manufacture. Therefore, claims 4-6, 8-13, 15-20, and 22-23 are directed to statutory subject matter under Step 1 as described in MPEP 2106 (Step 1: YES).
The claims are then analyzed to determine whether the claims are directed to a judicial exception. In determining whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), as well as analyzed to evaluate whether the claims recite additional elements that integrate the judicial exception into a practical application of the judicial exception (Prong Two of Step 2A).
Claims 4, 11, and 18 at least the following limitations that are believed to recite an abstract idea:
Training a model using a set of training data comprising (i) product information for a plurality of products, wherein the product information comprises a set of components for each of the plurality of products, and (ii) a plurality of regulations/standards applicable to the plurality of products, wherein the model comprises a semantic knowledge graph representing interconnections between the plurality of regulations/standards and the plurality of products;
storing, in a storage, the model;
accessing information associated with a product with which compliance to a regulation/standard is not known, wherein the information indicates that the product (i) is a new model of an existing product, and (ii) comprises at least one new component that is not included in the existing product;
analyzing, using the model, the information associated with the product to predict whether the product complies with the regulation/standard, wherein the analyzing comprises:
Querying the semantic knowledge graph to match the product to the regulation/standard based on the information associated with the product, and
Evaluating potential certification performance of the product based on past certification results associated with the existing process; and
displaying, in a display, a result of the analyzing to enable a user to manage compliance for the product.
The above limitations recite the concept of regulatory/legal analysis. These limitations, under their broadest reasonable interpretation, fall within the “Certain Methods of Organizing Human Activity” grouping of abstract ideas, enumerated in MPEP 2106, in that they recite commercial or legal interactions, and managing personal behavior or relationships or interactions between people, e.g., following rules or instructions. Accordingly, under Prong One of Step 2A, claims 4-6, 8-13, 15-20, and 22-23 recite an abstract idea (Step 2A, Prong One: YES).
Prong Two of Step 2A is the next step in the eligibility analyses and looks at whether the abstract idea is integrated into a practical application. This requires an additional element or combination of additional elements in the claims to apply, rely on, or user the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that the claim is more than a drafting effort designed to monopolize the exception.
In this instance, the claims recite the additional elements of:
The method being computer-implemented
Using machine learning/ a machine learning model
Steps being performed by at least one processor
A memory
A user interface
A system comprising a memory a user interface and at least one processor interfaced with the memory and the user interface
A non-transitory computer-readable storage medium storing processor-executable instructions
However, these elements do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
In addition, the recitations are recited at a high level of generality and also do not amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception.
The dependent claims also fail to recite elements which amount to an improvement in the functioning of a computer or any other technology or technical field; apply the judicial exception with, or by use of, a particular machine; or apply or use 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 to monopolize the exception. For example, claims 5-6, 8-10, 12-13, 15-17, 19-20, and 22-23 are directed to the abstract idea itself and do not amount to an integration according to any one of the considerations above. Therefore the dependent claims do not create an integration for the same reasons.
Step 2B is the next step in the eligibility analyses and evaluates whether the claims recite additional elements that amount to an inventive concept (i.e., “significantly more”) than the recited judicial exception. According to Office procedure, revised Step 2A overlaps with Step 2B, and thus, many of the considerations need not be re-evaluated in Step 2B because the answer will be the same.
In Step 2A, several additional elements were identified as additional limitations:
The method being computer-implemented
Using machine learning/ a machine learning model
Steps being performed by at least one processor
A memory
A user interface
A system comprising a memory a user interface and at least one processor interfaced with the memory and the user interface
A non-transitory computer-readable storage medium storing processor-executable instructions
These additional limitations, including the limitations in the dependent claims, do not amount to an inventive concept because they were already analyzed under Step 2A and did not amount to a practical application of the abstract idea. Therefore, the claims lack one or more limitations which amount to an inventive concept in the claims.
For these reasons, the claims are rejected under 35 U.S.C. 101.
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Claim Rejection – 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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-
obviousness.
Claims 4, 6, 8-11, 13, 15-18, 20, 22-23 are rejected under 35 U.S.C. 103 as being unpatentable over Morones et al (US 20200372977 A1), hereinafter Morones, in view of Marthouse et al (US 20220107977 A1), hereinafter Marthouse.
Regarding Claim 4, Morones teaches a computer-implemented method to predict product certification compliance, the method comprising:
generating, by at least one processor, a model using a set of data comprising (i) product information for a plurality of products [chemicals], wherein the product information comprises a set of components [components/characteristics] for each of the plurality of products, and (ii) a plurality of regulations applicable to the plurality of products (Morones: “Application 110 receives or retrieves Chemical Literature 105 in order to generate one or more Chemical Effect Graph(s) 115. …the Chemical Literature 105 includes regulatory information, such as restrictions or bans related to chemicals or chemical compositions” [0014] – “ identify chemicals and, for each chemical, determine the structure and characteristics, identify any known effects or regulations related to the chemical, determine any known reactions between the chemical and other chemical(s), and the like. This information is then represented in the Chemical Effect Graph 115. ” [0015] – “parses the Chemical Literature 105 to identify the structure of each chemical/composition …identify the components included, determine the types and location(s) of bond(s) that make up the structure ” [0025] – “the Graph Generator 235 includes within each node the relevant details for the chemical(s), such as the known effects, safe exposure rates, the structure of the substance, reactions involving the substance, regulations or restrictions applicable” [0027]),
wherein the model comprises a semantic knowledge graph representing interconnections between the plurality of regulations and the plurality of products (Morones: “generates a knowledge graph (e.g., a Chemical Effects Graph 115) including identified chemicals, the similarities between each substance” [0031] – “the Graph Generator 235 further generates and inserts links or connections between the nodes based on similarity between them.” [0027] – “generates, for each respective chemical, compound, element, or composition, a respective node in the knowledge graph. In an embodiment, each generated node includes the identified effects, regulations… for a given composition, the corresponding node in the graph can include the toxicology, known reactions, existing regulations or restrictions” [0034] – See also [0039]);
storing, in memory, the model (Morones: “the Storage 220 of the Analysis Device 205 includes a Chemical Effect Graph … the Memory 215 includes a Chemical Analysis Application” [0023] – “the Chemical Analysis Application 110 stores and/or returns the knowledge graph for future use” [0041] – “store the knowledge graph at a storage location ” [0066]);
accessing, by the at least one processor, information associated with a product with which compliance to a regulation is not known, wherein the information indicates that the product (i) is similar to an existing product, and (ii) comprises at least one new component [structure] that is not included in the existing product (Morones: “receives a newly proposed chemical composition (e.g., from a user). In embodiments, this proposed substance can indicate the chemical name of the substance, the structure of the substance, and the like. ” [0031] – “identify the substances that share at least fifty percent of the structure of the indicated new compound. … traverse the graph to identify a set of compositions that are within a predefined structural similarity to the proposed compound.” [0043] – “identify the components included, determine the types and location(s) of bond(s) that make up the structure and link the components, the positioning of each component in the structure (e.g., ortho, para, or meta)” [0025]);
analyzing, by the at least one processor using the model, the information associated with the product to predict whether the product complies with the regulation (Morones: “predict the likelihood that the proposed substance will be banned or otherwise regulated, based on the data contained in the graph.” [0030]), wherein the analyzing comprises:
querying the semantic knowledge graph to match the product to the regulation based on the information associated with the product (Morones: “determine similarities between the Proposed Substance 120 and other substances which are currently subject to regulations by the relevant entities (e.g., national or local governments). Based on these similarities, the Chemical Analysis Application 110 can determine how likely it is that the Proposed Substance 120 will be similarly regulated.” [0017] – “the Probabilistic Evaluator 240 can similarly predict the likelihood that the proposed substance will be banned or otherwise regulated, based on the data contained in the graph.” [0030]), and
evaluating potential certification performance of the product based on past certification results associated with the existing product (Morones: “the Predicted Effects 125 can also include a likelihood that the Proposed Substance 120 will be banned or otherwise regulated. For example, the Chemical Analysis Application 110 can determine similarities between the Proposed Substance 120 and other substances which are currently subject to regulations by the relevant entities (e.g., national or local governments). Based on these similarities, the Chemical Analysis Application 110 can determine how likely it is that the Proposed Substance 120 will be similarly regulated.” [0017] – “determine whether the selected substance is regulated or banned…so that the Chemical Analysis Application 110 can generate overall predictions for the proposed composition,” [0044]);
but does not specifically teach a method of using machine learning; that generating the model using a set of data comprises training a machine learning model using a set of training data; that the product being similar to an existing product comprises being a new model of an existing product; or displaying, in a user interface, a result of the analyzing to enable a user to manage compliance for the product.
However, Marthouse teaches systems for determining compliance status (Marthouse: Abstract), including:
a method of using machine learning (Marthouse: “machine learning … ML engine identifies particular features of objects …ML engine that identifies features that are damage features or features indicating a lack of compliance.” [0026] – “ML engine 140 uses classification and/or object detection techniques to classify object features as anomalous or non-anomalous features. … in the context of compliance, classified as being in compliance or out of compliance, depending on the standard or regulation being classified against.” [0045]);
that generating the model using a set of data comprises training a machine learning model using a set of training data (Marthouse: “ the training data provided for features/objects ML engine 135 comprises template images of known and labeled object types, with labeled features of labeled objects. ” [0044] – “structures and structure elements that may be labeled as to their compliance status—in compliance or out of compliance, depending on the training data images/objects of the desired training.” [0049]);
that the product being similar to an existing product comprises being a new model [anomalous/future]of an existing product[non-anomalous/original form] (Marthouse: “one or more features are identified and/or classified as anomalous features of the structure element. In this context, an anomaly is a feature of a structure or structure element identified … as non-compliant. … structures and/or objects may be compared to non-anomalous images/templates in order to determine anomalous features. …where an assessment of physical structures and/or goods as being ascertained …, features and objects may be recorded at this operation as being in an undamaged state without anomalies.” [0066] – “A feature classified as anomalous in embodiments could be detected features of a structure element that were not part of the structure element in its original, or new, form, such as … features indicating a lack of compliance with requirements. ” [0045] – “ record a current state of an object, vehicle, …images/point clouds of the objects … may stored as templates of non-anomalous condition of these items, against which futures scans may be compared ” [0047] – “generate a point cloud model data or 3D image model data of the imaged/scanned structures, structural elements, and/or physical objects.” [0040] – See also [0049]); and
displaying, in a user interface, a result of the analyzing to enable a user to manage compliance for the product (Marthouse: “A financial estimation ML engine takes an updated data model from the anomaly assessment ML engine and, using regression analysis, develops a data model for a structure element estimating a financial value of the identified structure element based on the value of the element itself, its material composition, and damage/non-compliance features. The output of the financial estimation ML engine is provided to a reporting module that displays the structure element, anomalous features (e.g., damage and/or non-compliant features), and financial value to a user.” [0027]).
Examiner Note: It is noted that a recitation of the intended use of the claimed invention does not impose any limit on the interpretation of the claim unless such a recitation results in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. [MPEP 2111.04] In claim 4, the language “to enable a user to manage compliance for the product” represents an intended use of the result by the user, and as such is afforded little weight. See also MPEP 2103: “statements of intended use ... including statements of purpose” in the claims “raise a question as to its limiting effect.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Morones would continue to teach generating, by at least one processor, a model using a set of data comprising (i) product information for a plurality of products, wherein the product information comprises a set of components for each of the plurality of products, and (ii) a plurality of regulations applicable to the plurality of products; & accessing, by the at least one processor, information associated with a product with which compliance to a regulation is not known, wherein the information indicates that the product (i) is similar to an existing product, and (ii) comprises at least one new component that is not included in the existing product, except that now it would also teach a method of using machine learning; that generating the model using a set of data comprises training a machine learning model using a set of training data; that the product being similar to an existing product comprises being a new model of an existing product; and displaying, in a user interface, a result of the analyzing to enable a user to manage compliance for the product, according to the teachings of Marthouse. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved efficiency in assessing compliance (Marthouse: [0028]).
Regarding claim 6, Morones/Marthouse teach the computer-implemented method of claim 4, wherein accessing the information associated with the product comprises: accessing the information associated with the product with which compliance to the regulation is not known, wherein the information indicates that a previous model of the product complies with the regulation (Marthouse: “uses classification and/or object detection techniques to classify object features as anomalous or non-anomalous features …in the context of compliance, classified as being in compliance or out of compliance, depending on the standard or regulation being classified against.” [0045] – “structures and/or objects may be compared to non-anomalous images/templates in order to determine anomalous features. …where an assessment of physical structures and/or goods as being ascertained …, features and objects may be recorded at this operation as being in an undamaged state without anomalies.” [0066]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Marthouse with Morones for the reasons identified above with respect to claim 4.
Regarding Claim 8, Morones/Marthouse teach the computer-implemented method of claim 4, wherein accessing the information associated with the product comprises: accessing the information associated with the product with which compliance to the regulation in a specified market is not known (Morones: “determine similarities between the Proposed Substance 120 and other substances which are currently subject to regulations by the relevant entities (e.g., national or local governments).” [0017] – “the Regulatory Bodies 250 can include governmental entities at a national level, state level, local level, and the like. In one embodiment, the user can specify the relevant Regulatory Bodies 250 when analyzing new substances. … the user can specify a relevant location, and the Analysis Device 205 can identify the corresponding Regulatory Bodies” [0022]).
Regarding Claim 9, Morones/Marthouse teach the computer-implemented method of claim 4, further comprising: displaying, in the user interface, an entry dialog for inputting a comment associated with the regulation, wherein the entry dialog tags the user to cause the user to be notified of the comment (Marthouse: “the result is provided to an anomaly assessment ML engine that identifies features that are damage features or features indicating a lack of compliance. ” [0026] – “A financial estimation ML engine takes an updated data model from the anomaly assessment ML engine and, using regression analysis, develops a data model for a structure element estimating a financial value of the identified structure element based on the value of the element itself, its material composition, and damage/non-compliance features. The output of the financial estimation ML engine is provided to a reporting module that displays the structure element, anomalous features (e.g., damage and/or non-compliant features), and financial value to a user.” [0027] – “structure elements that may be labeled as to their compliance status—in compliance or out of compliance” [0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Marthouse with Morones for the reasons identified above with respect to claim 4.
Regarding Claim 10, Morones/Marthouse teach the computer-implemented method of claim 4, wherein the regulation is a standard (Marthouse: “a legal requirement (e.g., local, city, state, national, international building codes/requirements from private and/or public entities, materials requirements, material component requirements, and the like), contractual requirement, or industry standard. ” [0026] – “the standard or regulation being classified against” [0045]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Marthouse with Morones for the reasons identified above with respect to claim 4.
Regarding Claim 11, Morones discloses a system to predict product certification compliance, the system comprising: a memory storing instructions and data associated with a model; and at least one processor interfaced with the memory, and configured to execute the instructions (Morones: [0005]) to cause the at least one processor to:
generate the model using a set of data comprising (i) product information for a plurality of products [chemicals], wherein the product information comprises a set of components components/characteristics] for each of the plurality of products, and (ii) a plurality of rules applicable to the plurality of products (Morones: “Application 110 receives or retrieves Chemical Literature 105 in order to generate one or more Chemical Effect Graph(s) 115. …the Chemical Literature 105 includes regulatory information, such as restrictions or bans related to chemicals or chemical compositions” [0014] – “ identify chemicals and, for each chemical, determine the structure and characteristics, identify any known effects or regulations related to the chemical, determine any known reactions between the chemical and other chemical(s), and the like. This information is then represented in the Chemical Effect Graph 115. ” [0015] – “parses the Chemical Literature 105 to identify the structure of each chemical/composition …identify the components included, determine the types and location(s) of bond(s) that make up the structure ” [0025] – “the Graph Generator 235 includes within each node the relevant details for the chemical(s), such as the known effects, safe exposure rates, the structure of the substance, reactions involving the substance, regulations or restrictions applicable” [0027]),
wherein the model comprises a semantic knowledge graph representing interconnections between the plurality of rules and the plurality of products (Morones: “generates a knowledge graph (e.g., a Chemical Effects Graph 115) including identified chemicals, the similarities between each substance” [0031] – “the Graph Generator 235 further generates and inserts links or connections between the nodes based on similarity between them.” [0027] – “generates, for each respective chemical, compound, element, or composition, a respective node in the knowledge graph. In an embodiment, each generated node includes the identified effects, regulations… for a given composition, the corresponding node in the graph can include the toxicology, known reactions, existing regulations or restrictions” [0034] – See also [0039]);
cause the memory to store the model (Morones: “the Storage 220 of the Analysis Device 205 includes a Chemical Effect Graph … the Memory 215 includes a Chemical Analysis Application” [0023] – “the Chemical Analysis Application 110 stores and/or returns the knowledge graph for future use” [0041] – “store the knowledge graph at a storage location ” [0066]);
access information associated with a product with which compliance to a rule is not known, wherein the information indicates that the product (i) is similar to an existing product, and (ii) comprises at least one new component [structure] that is not included in the existing product (Morones: “receives a newly proposed chemical composition (e.g., from a user). In embodiments, this proposed substance can indicate the chemical name of the substance, the structure of the substance, and the like. ” [0031] – “identify the substances that share at least fifty percent of the structure of the indicated new compound. … traverse the graph to identify a set of compositions that are within a predefined structural similarity to the proposed compound.” [0043] – “identify the components included, determine the types and location(s) of bond(s) that make up the structure and link the components, the positioning of each component in the structure (e.g., ortho, para, or meta)” [0025]);
analyze, using the model, the information associated with the product to predict whether the product complies with the rule, (Morones: “predict the likelihood that the proposed substance will be banned or otherwise regulated, based on the data contained in the graph.” [0030]), including:
querying the semantic knowledge graph to match the product to the rule based on the information associated with the product (Morones: “determine similarities between the Proposed Substance 120 and other substances which are currently subject to regulations by the relevant entities (e.g., national or local governments). Based on these similarities, the Chemical Analysis Application 110 can determine how likely it is that the Proposed Substance 120 will be similarly regulated.” [0017] – “the Probabilistic Evaluator 240 can similarly predict the likelihood that the proposed substance will be banned or otherwise regulated, based on the data contained in the graph.” [0030]), and
evaluating potential certification performance of the product based on past certification results associated with the existing product (Morones: “the Predicted Effects 125 can also include a likelihood that the Proposed Substance 120 will be banned or otherwise regulated. For example, the Chemical Analysis Application 110 can determine similarities between the Proposed Substance 120 and other substances which are currently subject to regulations by the relevant entities (e.g., national or local governments). Based on these similarities, the Chemical Analysis Application 110 can determine how likely it is that the Proposed Substance 120 will be similarly regulated.” [0017] – “determine whether the selected substance is regulated or banned…so that the Chemical Analysis Application 110 can generate overall predictions for the proposed composition,” [0044]),
but does not specifically teach a system for using machine learning; a user interface that is interfaced with the at least one processor; that generating the model using a set of data comprises training a machine learning model using a set of training data; that the rules are standards; that the product being similar to an existing product comprises being a new model of an existing product; and causing the user interface to display a result of the analyzing to enable a user to manage compliance for the product.
However, Marthouse teaches systems for determining compliance status (Marthouse: Abstract), including:
a system for using machine learning (Marthouse: “machine learning … ML engine identifies particular features of objects …ML engine that identifies features that are damage features or features indicating a lack of compliance.” [0026] – “ML engine 140 uses classification and/or object detection techniques to classify object features as anomalous or non-anomalous features. … in the context of compliance, classified as being in compliance or out of compliance, depending on the standard or regulation being classified against.” [0045]);
a user interface that is interfaced with the at least one processor (Marthouse: “a system … comprising a processor, and a memory storing instructions, which, when executed by the processor perform a method …The system further includes a computer system that includes…a display configured to display at least one of the structure, ” [0012--0013]);
that generating the model using a set of data comprises training a machine learning model using a set of training data (Marthouse: “ the training data provided for features/objects ML engine 135 comprises template images of known and labeled object types, with labeled features of labeled objects. ” [0044] – “structures and structure elements that may be labeled as to their compliance status—in compliance or out of compliance, depending on the training data images/objects of the desired training.” [0049]);
that the rules are standards (Marthouse: “a legal requirement (e.g., local, city, state, national, international building codes/requirements from private and/or public entities, materials requirements, material component requirements, and the like), contractual requirement, or industry standard. ” [0026] – “the standard or regulation being classified against” [0045]);
that the product being similar to an existing product comprises being a new model [anomalous/future]of an existing product[non-anomalous/original form] (Marthouse: “one or more features are identified and/or classified as anomalous features of the structure element. In this context, an anomaly is a feature of a structure or structure element identified … as non-compliant. … structures and/or objects may be compared to non-anomalous images/templates in order to determine anomalous features. …where an assessment of physical structures and/or goods as being ascertained …, features and objects may be recorded at this operation as being in an undamaged state without anomalies.” [0066] – “A feature classified as anomalous in embodiments could be detected features of a structure element that were not part of the structure element in its original, or new, form, such as … features indicating a lack of compliance with requirements. ” [0045] – “ record a current state of an object, vehicle, …images/point clouds of the objects … may stored as templates of non-anomalous condition of these items, against which futures scans may be compared ” [0047] – “generate a point cloud model data or 3D image model data of the imaged/scanned structures, structural elements, and/or physical objects.” [0040] – See also [0049]); and
causing the user interface to display a result of the analyzing to enable a user to manage compliance for the product (Marthouse: “A financial estimation ML engine takes an updated data model from the anomaly assessment ML engine and, using regression analysis, develops a data model for a structure element estimating a financial value of the identified structure element based on the value of the element itself, its material composition, and damage/non-compliance features. The output of the financial estimation ML engine is provided to a reporting module that displays the structure element, anomalous features (e.g., damage and/or non-compliant features), and financial value to a user.” [0027]).
Examiner Note: It is noted that a recitation of the intended use of the claimed invention does not impose any limit on the interpretation of the claim unless such a recitation results in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. [MPEP 2111.04] In claim 4, the language “to enable a user to manage compliance for the product” represents an intended use of the result by the user, and as such is afforded little weight. See also MPEP 2103: “statements of intended use ... including statements of purpose” in the claims “raise a question as to its limiting effect.”
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Morones would continue to teach generating a model using a set of data comprising (i) product information for a plurality of products, wherein the product information comprises a set of components for each of the plurality of products, and (ii) a plurality of rules applicable to the plurality of products; & accessing information associated with a product with which compliance to a rule is not known, wherein the information indicates that the product (i) is similar to an existing product, and (ii) comprises at least one new component that is not included in the existing product, except that now it would also teach a system for using machine learning; a user interface that is interfaced with the at least one processor; that generating the model using a set of data comprises training a machine learning model using a set of training data; that the rules are standards; that the product being similar to an existing product comprises being a new model of an existing product; and causing the user interface to display a result of the analyzing to enable a user to manage compliance for the product, according to the teachings of Marthouse. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved efficiency in assessing compliance (Marthouse: [0028]).
Regarding claim 13, Morones/Marthouse teach the system of claim 11, wherein the information indicates that a previous model of the product complies with the standard (Marthouse: “uses classification and/or object detection techniques to classify object features as anomalous or non-anomalous features …in the context of compliance, classified as being in compliance or out of compliance, depending on the standard or regulation being classified against.” [0045] – “structures and/or objects may be compared to non-anomalous images/templates in order to determine anomalous features. …where an assessment of physical structures and/or goods as being ascertained …, features and objects may be recorded at this operation as being in an undamaged state without anomalies.” [0066]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Marthouse with Morones for the reasons identified above with respect to claim 11.
Regarding Claim 15, Morones /Marthouse teach the computer system of claim 11, wherein the information associated with the product indicates that compliance to the standard in a specified market is not known (Morones: “determine similarities between the Proposed Substance 120 and other substances which are currently subject to regulations by the relevant entities (e.g., national or local governments).” [0017] – “the Regulatory Bodies 250 can include governmental entities at a national level, state level, local level, and the like. In one embodiment, the user can specify the relevant Regulatory Bodies 250 when analyzing new substances. … the user can specify a relevant location, and the Analysis Device 205 can identify the corresponding Regulatory Bodies” [0022]).
Regarding Claim 16, Morones/Marthouse teach the system of claim 11, wherein the at least one processor is configured to execute the instructions to further cause the processor to: cause the user interface to display an entry dialog for inputting a comment associated with the standard, wherein the entry dialog tags the user to cause the user to be notified of the comment (Marthouse: “the result is provided to an anomaly assessment ML engine that identifies features that are damage features or features indicating a lack of compliance. ” [0026] – “A financial estimation ML engine takes an updated data model from the anomaly assessment ML engine and, using regression analysis, develops a data model for a structure element estimating a financial value of the identified structure element based on the value of the element itself, its material composition, and damage/non-compliance features. The output of the financial estimation ML engine is provided to a reporting module that displays the structure element, anomalous features (e.g., damage and/or non-compliant features), and financial value to a user.” [0027] – “structure elements that may be labeled as to their compliance status—in compliance or out of compliance” [0049]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine Marthouse with Morones for the reasons identified above with respect to claim 11.
Regarding Claim 17, Morones /Marthouse teach the system of claim 11, wherein the standard is a regulation (Morones: “Application 110 receives or retrieves Chemical Literature 105 in order to generate one or more Chemical Effect Graph(s) 115. …the Chemical Literature 105 includes regulatory information, such as restrictions or bans related to chemicals or chemical compositions” [0014] – “ identify chemicals and, for each chemical, determine the structure and characteristics, identify any known effects or regulations related to the chemical, determine any known reactions between the chemical and other chemical(s), and the like. This information is then represented in the Chemical Effect Graph 115. ” [0015] – “parses the Chemical Literature 105 to identify the structure of each chemical/composition …identify the components included, determine the types and location(s) of bond(s) that make up the structure ” [0025] – “the Graph Generator 235 includes within each node the relevant details for the chemical(s), such as the known effects, safe exposure rates, the structure of the substance, reactions involving the substance, regulations or restrictions applicable” [0027] ).
Regarding Claims 18, 20, 22-23, the limitations of claims 18, 20, and 22-23 are closely parallel to the limitations of claims 4,6, and 8-9, with the additional limitation of a non-transitory computer-readable storage medium having stored thereon a set of instructions executable by at least one processor (Amar: Col. 32), and are rejected on the same basis.
Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Morones in view of Marthouse, and further in view of Loomis et al (US 20230254222 A1), hereinafter Loomis.
Regarding Claim 5, Morones/Marthouse teach the computer-implemented method of claim 4, but do not specifically teach that accessing the information associated with the product comprises: accessing the information associated with the product with which compliance to the regulation is not known, wherein the information indicates that the product complies with an additional regulation of the plurality of regulations other than the regulation.
However, Loomis teaches systems for management of regulatory compliance information (Loomis: [0001]), including accessing the information associated with the product with which compliance to the regulation is not known, wherein the information indicates that the product complies with an additional regulation of the plurality of regulations other than the regulation (Loomis: “new regulations are generated …compliance information associated with the numbers is reevaluated to determine whether it is still compliant with their associated regulations in view of the one or more regulation updates. … statuses of any compliance information that will no longer be compliant with their associated regulations in view of the one or more regulation updates … are each changed ” [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Morones/Marthouse would continue to teach accessing the information associated with the product, except that now it would also teach accessing the information associated with the product with which compliance to the regulation is not known, wherein the information indicates that the product complies with an additional regulation of the plurality of regulations other than the regulation, according to the teachings of Loomis. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to keep compliance information up to date (Loomis: [0004]).
Regarding Claim 12, Morones/Marthouse teach the system of claim 11, but do not specifically teach that the information indicates that the product complies with an additional standard of the plurality of standards other than the standard.
However, Loomis teaches systems for management of regulatory compliance information (Loomis: [0001]), including that the information indicates that the product complies with an additional standard of the plurality of standards other than the standard (Loomis: “new regulations are generated …compliance information associated with the numbers is reevaluated to determine whether it is still compliant with their associated regulations in view of the one or more regulation updates. … statuses of any compliance information that will no longer be compliant with their associated regulations in view of the one or more regulation updates … are each changed ” [0029]).
It would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because the results would be predictable. Specifically, Morones/Marthouse would continue to teach accessing the information associated with the product, except that now it would also teach that the information indicates that the product complies with an additional standard of the plurality of standards other than the standard, according to the teachings of Loomis. This is a predictable result of the combination.
In addition, it would have been obvious to one of ordinary skill in the art before the effective filing date of invention to combine these references because it would result in an improved ability to keep compliance information up to date (Loomis: [0004]).
Regarding Claim 19, the limitations of claim 19 are closely parallel to the limitations of claim 5, and are rejected on the same basis.
Response to Arguments
Applicant's arguments filed 3/12/2026 have been fully considered but they are not persuasive.
Claim Rejection – 35 USC §101
Applicant argues that the claims integrate the abstract idea into a practical application “because they improve a non-abstract technology, namely computer-based compliance management systems used in product certification workflows.” Applicant makes reference to the Specification, which describes “a difficulty in accurately and effectively assessing which product regulation updates may be applicable to products before or during the introduction of the products to the market,” and that having to “manually review product regulation updates” is “often time consuming, ineffective, and/or expensive due to the inherent complexity of tracking individual regulation updates.” Applicant argues that the claims “offer improved capabilities to solve these problems by dynamically and accurately assessing regulation update applicability to products based on up-to-date information and machine learning techniques,” and that, “because the systems and methods employ communication between and among multiple devices, the systems and methods are necessarily rooted in computer technology in order to overcome the noted shortcomings that specifically arise in the realm of supply chain management.” Applicant argues that newly amended limitations reciting a “semantic knowledge graph representing interconnections between the plurality of regulations and the plurality of products,” querying the graph “to match the product to the regulation based on the information associated with the product,” and “evaluating potential certification performance of the product based on past certification results,” “recite a particular technical solution (namely a semantic knowledge graph data structure that is queried to match products to regulations, combined with evaluation of past certification results) that improves how computer-based compliance management systems operate.”
Examiner disagrees. The semantic knowledge graph, and the ability to query it based on product information and evaluate certification performance based on past results, are part of the abstract idea itself – these are manual steps for analyzing compliance/certification of a product, with the additional elements invoked as mere instructions to apply these abstract steps to a technological environment [MPEP 2106.05(f)], creating only a general linking between the abstract idea and computer technology, e.g. a generic machine learning model, a generic UI, etc. Similarly, the alleged problem is not a technological problem, but a business problem in the abstract field of compliance management. The alleged solution to this problem stems solely from the abstract idea, and as such is at most a business solution rooted in the abstract idea alone. The additional elements at most offer only the improved speed or efficiency inherent with applying an abstract idea on a generic computer, which does not integrate the abstract idea into a practical application [MPEP 2106.05(a)].
Applicant further argues that the claims recite “specific technical features that go beyond well- understood, routine, and conventional computer functions,” including “a semantic knowledge graph representing interconnections between the plurality of regulations and the plurality of products, and further recites that the analyzing step comprises querying the semantic knowledge graph to match the product to the regulation based on the information associated with the product, and evaluating potential certification performance of the product based on past certification results associated with the existing product.” Applicant argues that the claims do not “merely recite a generic machine learning model. Rather, the claim specifies that the machine learning model comprises a semantic knowledge graph representing interconnections between the plurality of regulations and the plurality of products. This is a specific data structure, not a generic recitation of machine learning. The specification explains that this semantic knowledge graph uses a graph-structured data model or topology to integrate data, where the knowledge graph may store interlinked descriptions of entities while encoding the semantics underlying the used terminology.” Applicant concludes that “the claim recites a specific sequence of operations: (1) training a machine learning model that includes a semantic knowledge graph representing interconnections between regulations and products; (2) storing the model; (3) accessing information about a new product model with a new component; (4) analyzing the information by querying the semantic knowledge graph to match the product to the regulation and evaluating potential certification performance based on past certification results; and (5) displaying the result. This ordered combination reflects the technical improvement described in the specification; namely, a system that can dynamically and accurately assess regulation applicability and predict compliance for new product models based on past certification data, thereby overcoming the shortcomings of conventional manual review methods.”
Examiner disagrees. Similar to the discussion above with respect to Step 2A Prong 2, the additional elements create only a general linking to computer technology, with the argued ability to generate, store, and use a semantic knowledge graph to gather data for an evaluation of potential certification performance of a new product, being part of the abstract idea itself. The “specific data structure” of the semantic knowledge graph is part of the abstract idea, as identified in the rejection above, with the claim merely including instructions to “apply it” to a generic machine learning model. The alleged ability to more “accurately assess regulation applicability and predict compliance for new product models based on past certification data,” stems solely from the abstract idea, which cannot form the sole basis for a technological improvement to itself.
Claim Rejection – 35 USC §103
In response to applicant's argument that Marthouse is nonanalogous art, i.e. that it is “directed to fundamentally different technical fields than the present application,” it has been held that a prior art reference must either be in the field of the inventor’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the inventor was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992). In this case, Marthouse is directed to systems for determining compliance status of elements of a structure [Abstract], including classifying elements as being in or out of compliance with regulations or standards using a trained ML model [0045]. Thus, the reference, analogous to the claims, teaches a trained machine-learning model for predicting regulatory compliance of elements of a system.
Applicant’s arguments with respect to the technical field of the Amar reference have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument; specifically, Amar is not relied upon in the 103 rejections above.
Applicant’s arguments with respect to the amended steps of training, analyzing, and evaluating have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument; specifically, Amar is not relied upon in the 103 rejections above, with newly-relied upon reference Morones teaching the generating of the semantic knowledge graph as claimed, as well as the querying of the graph to determine applicable regulations and evaluating to predict potential certification performance; and Marthouse newly teaching that the generation of the model can be the training of an ML model.
Applicant’s arguments with respect to motivation to combine Amar with Marthouse have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument; specifically, Amar is not relied upon in the 103 rejections above.
Applicant’s arguments with respect to claims 5, 12, and 19, specifically the combination of Amar with Marthouse and Loomis have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument; specifically, Amar is not relied upon in the 103 rejections above.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Peters et al (US 20200372557 A1) discusses systems for determining regulatory classifications for products using a trained machine learning model.
Chin et al (US 11755761 B2) teaches methods for assessing regulatory compliance in different markets.
Reference U (NPL – see attached) discusses AI/ML applications for regulatory compliance.
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/T.J.S./
Examiner, Art Unit 3689
/Jeffrey A. Smith/Supervisory Patent Examiner, Art Unit 3688
Prosecution Insights