Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
2. The Amendment filed May 18, 2026 has been entered. Claims 1-20 are pending and are rejected for the reasons set forth below.
Double Patenting
3. The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
4. Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 2, 4, 6-8, 10, and 12-19 of U.S. Patent No. 12182870. Although the claims at issue are not identical, they are not patentably distinct from each other. A mapping between the limitations of these claims is provided below.
Instant Application
Issued Patent
1. A method comprising:
developing, by one or more processors, an extreme event risk score based on operational environment data, legislative environment data and a data environment;
parsing and converting, by the one or more processors, the operational environment data, the legislative environment data and the data environment to at least one of a directed graph or state-specific decision trees using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines;
determining, by the one or more processors, a remediation score based on a plurality of remediation terms including harm reduction data, cost data, sustainability data, resiliency data, the extreme event risk score and the data environment;
determining, by the one or more processors, how each remediation term of the plurality of remediation terms influences the remediation score by combining each of the plurality of remediation terms via linear combination with a weighting term, wherein the weighting term is determined by statistical analysis of at least one of previous sites, portfolio priorities or an emphasis of a regulatory environment on each of the plurality of remediation terms;
adjusting, by the one or more processors, probabilities of the extreme event risk score based on a severity of an outcome to obtain adjusted probabilities, wherein the adjusted probabilities are visually indicated by a weight of edges in a decision tree;
adjusting, by the one or more processors, the weight of the edges in the decision tree by using machine learning models;
computing, by the one or more processors, a training objective by re-calculating probabilities underlying risk calculations of an initial risk score, by using the adjusted probabilities of the extreme event risk score and by defining portfolio priorities with the operational environment data and the legislative environment data;
updating, by the one or more processors, a predictive model to create an updated predictive model, based on the training objective; and
improving, by the one or more processors using the updated predictive model, an accuracy of the initial risk score and a risk rating to improve prediction accuracies for other sites.
2. The method of claim 1, further comprising determining, by the one or more processors, a remediated risk score for the site based on the initial risk score and the remediation score.
3. The method of claim 1, further comprising: determining, by the one or more processors, the risk rating for the site pre-remediation based on the initial risk score and an information depth score; and
determining, by the one or more processors, the risk rating for the site post-remediation based on the remediated risk score and the information depth score.
4. The method of claim 1, further comprising: saving, by the one or more processors, legislative documents as text documents in a data environment;
parsing and converting, by the one or more processors, the text documents to at least one of a directed graph or state-specific decision trees using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines, wherein the directed graph is an object that describes decision nodes and edges, wherein the edges are a directional relationship between the decision nodes, and wherein the directed graph enforces directionality constraints on the nodes to encode at least one of causality or sequence.
5. The method of claim 1, further comprising: detecting, by the one or more processors, knowledge to obtain detected knowledge in the form of attributes comprising key terms, rules, topic summaries, relationships between various legal terms, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules; and
tagging, by the one or more processors, the detected knowledge from text documents as a decision node or a outcome node.
6. The method of claim 1, further comprising: ingesting, by the one or more processors, legislative documents from the legislative environment and into the data environment;
saving, by the one or more processors, the legislative documents as text documents in a data environment; parsing, by the one or more processors, the text documents; converting, by the one or more processors, the text documents to a directed graph;
re-training, by the one or more processors, the predictive model with definitions of categories of each section of the legislative documents;
implementing, by the one or more processors, content segmentation of the legislative documents to categorize each section of the legislative documents into the categories by type; and
re-training, by the one or more processors, the predictive model using the risk rating and the legislative documents.
7. The method of claim 1, further comprising: building, by the one or more processors, a knowledge graph using deep learning technologies, word embeddings, text summarization, embedded provenance and topic modeling;
extracting, by the one or more processors, cross-referenced rules from the knowledge graph; and
identifying and classifying, by the one or more processors, the cross-referenced rules into deontic expressions.
8. The method of claim 7, wherein the building the knowledge graph comprises: extracting and validating instances of the knowledge graph using extractive text summarization, extraction of topics from summarized subsections, extraction of instances from topics and extraction of descriptions from topics;
extracting semantically similar terminologies and ontology populations; and
extracting relations between key entities.
9. The method of claim 1, wherein the data environment comprises a site compartment, a surroundings compartment, a setting compartment and a data lake, wherein the data lake includes geospatial data, tabular data and documents.
10. The method of claim 1, wherein decision nodes of the decision tree include an attribute, wherein the attribute includes a binary question for answering in response to the node being traversed.
11. The method of claim 1, further comprising at least one of: calculating a remediated risk score from a product of the initial risk score and the remediation score for any time point; or
calculating a total expectation of a risk by taking an integral of the remediated risk score over a change in time.
12. The method of claim 1, further comprising normalizing, by the one or more processors, the remediation score for a site to other sites.
13. The method of claim 1, further comprising weighting, by the one or more processors, at least one of the harm reduction data, the cost data or the sustainability data.
14. The method of claim 1, further comprising attenuating, by the one or more processors, the risk score based on at least one of an enforcement environment or regulatory resource restraints.
15. The method of claim 1, further comprising: obtaining, by the one or more processors, new data from other sites as part of a feedback loop to augment the harm reduction data, the cost data, the sustainability data, the resiliency data, the extreme event risk score and the data environment; and
re-training, by the one or more processors, the predictive model using the new data from the other sites.
16. The method of claim 1, wherein the extreme event risk score is based on a probabilistic rating that reflects how likely a site is to take an abnormally long time to close relative to similar sites.
17. The method of claim 1, wherein the harm reduction data comprises an ability of a remediation option to destroy or immobilize contamination, rendering the contamination less harmful to human health or the environment.
18. The method of claim 1, wherein the cost data comprises a financial requirement to design and enact a remediation option.
19. The method of claim 1, wherein the sustainability data comprises an environmental footprint of the remedial option.
20. A system comprising: one or more processors; and one or more tangible, non-transitory memories configured to communicate with the one or more processors, the one or more tangible, non-transitory memories having instructions stored thereon that, in response to execution by the one or more processors, cause the one or more processors to perform operations comprising:
developing, by the one or more processors, an extreme event risk score based on operational environment data, legislative environment data and a data environment;
parsing and converting, by the one or more processors, the operational environment data, the legislative environment data and the data environment to at least one of a directed graph or state-specific decision trees using artificial intelligence (Al) models that include deep learning models and natural language processing (NLP) routines;
determining, by the one or more processors, a remediation score based on a plurality of remediation terms including harm reduction data, cost data, sustainability data, resiliency data, the extreme event risk score and the data environment;
determining, by the one or more processors, how each remediation term of the plurality of remediation terms influences the remediation score by combining each of the plurality of remediation terms via linear combination with a weighting term, wherein the weighting term is determined by statistical analysis of at least one of previous sites, portfolio priorities or an emphasis of a regulatory environment on each of the plurality of remediation terms;
adjusting, by the one or more processors, probabilities of the extreme event risk score based on a severity of an outcome to obtain adjusted probabilities, wherein the adjusted probabilities are visually indicated by a weight of edges in a decision tree;
adjusting, by the one or more processors, the weight of the edges in the decision tree by using machine learning models;
computing, by the one or more processors, a training objective by re-calculating probabilities underlying risk calculations of an initial risk score, by using the adjusted probabilities of the extreme event risk score and by defining portfolio priorities with the operational environment data and the legislative environment data;
updating, by the one or more processors, a predictive model to create an updated predictive model, based on the training objective; and
improving, by the one or more processors using the updated predictive model, an accuracy of the initial risk score and a risk rating to improve prediction accuracies for other sites.
(claim 1) A method for creating a risk rating for a site, comprising:
(claim 1) developing, by one or more processors, an extreme event risk score based on operational environment data, legislative environment data and a data environment;
(claim 1) parsing and converting, by the one or more processors, the text documents to at least one of a directed graph or state-specific decision trees using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines. Examiner’s Note: While these limitations are not identical, they are not patentably distinct. Specifically, the instant application states that the method parses and converts “the operational environment data, the legislative environment data and the data environment” while the issued patent parses and converts, “the text documents.” However, claim 1 of the issued patent states that the text documents include the legislative environment data and the data environment (e.g., “ingesting, by the one or more processors, legislative documents from the legislative environment and into the data environment; saving, by the one or more processors, the legislative documents as text documents in a data environment”). It would have been obvious to one of ordinary skill in the art that the operational environment data could also be parsed and converted into a directed graph as described in the issued patent regarding the text documents
(claim 1) determining, by the one or more processors, a remediation score based on a plurality of remediation terms including harm reduction data, cost data, sustainability data, resiliency data, the extreme event risk score and the data environment;
(claim 1) determining, by the one or more processors, how each remediation term of the plurality of remediation terms influences the remediation score by combining each of the plurality of remediation terms via linear combination with a weighting term, wherein the weighting term is determined by statistical analysis of at least one of previous sites, portfolio priorities or an emphasis of a regulatory environment on each of the plurality of remediation terms;
(claim 1) adjusting, by the one or more processors, probabilities of the extreme event risk score based on a severity of an outcome to obtain adjusted probabilities, wherein the adjusted probabilities are visually indicated by a weight of the edges in the decision tree;
(claim 1) adjusting the weight of the edges in the decision tree by using the machine learning models;
(claim 1) computing, by the one or more processors, a training objective by re-calculating probabilities underlying risk calculations of the initial risk score, by using the adjusted probabilities of the extreme event risk score and by defining portfolio priorities with the operational environment data and the legislative environment data;
(claim 1) updating, by the one or more processors, the predictive model to create an updated predictive model, based on the training objective; and
(claim 1) improving, by the one or more processors using the updated predictive model, an accuracy of the initial risk score and the risk rating to improve prediction accuracies for the other sites.
(claim 1) determining, by the one or more processors, a remediated risk score for the site based on an initial risk score and the remediation score.
(claim 1) determining, by the one or more processors, a risk rating for the site pre-remediation based on the initial risk score and an information depth score.
(claim 1) determining, by the one or more processors, the risk rating for the site post-remediation based on the remediated risk score and the information depth score;
(claim 1) saving, by the one or more processors, the legislative documents as text documents in a data environment.
(claim 1) parsing and converting, by the one or more processors, the text documents to at least one of a directed graph or state-specific decision trees using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines, wherein the directed graph is an object that describes decision nodes and edges, wherein the edges are a directional relationship between the decision nodes, and wherein the directed graph enforces directionality constraints on the nodes to encode at least one of causality or sequence.
(claim 1) detecting, by the one or more processors, knowledge to obtain detected knowledge in the form of attributes comprising key terms, rules, topic summaries, relationships between various legal terms, semantically similar terminologies, deontic expressions and cross-referenced legal facts and rules;
(claim 1) tagging, by the one or more processors, the detected knowledge from the text documents as a decision node or a outcome node.
(claim 1) ingesting, by the one or more processors, legislative documents from the legislative environment and into the data environment;
(claim 1) saving, by the one or more processors, the legislative documents as text documents in a data environment; parsing and converting, by the one or more processors, the text documents to at least one of a directed graph or state-specific decision trees…
(claim 1) re-training, by the one or more processors, a predictive model with definitions of categories of each section of the legislative documents;
(claim 1) implementing, by the one or more processors, content segmentation of the legislative documents to categorize each section of the legislative documents into the categories by type;
(claim 1) re-training, by the one or more processors, the predictive model using the risk rating and the legislative documents.
(claim 1) building, by the one or more processors, a knowledge graph using deep learning technologies, word embeddings, text summarization, embedded provenance and topic modeling;
(claim 1) extracting, by the one or more processors, cross-referenced rules from the knowledge graph;
(claim 1) identifying and classifying, by the one or more processors, the cross-referenced rules into the deontic expressions.
(claim 2) extracting and validating instances of the knowledge graph using extractive text summarization, extraction of topics from summarized subsections, extraction of instances from topics and extraction of descriptions from topics;
(claim 2) extracting semantically similar terminologies and ontology populations; and
(claim 2) extracting relations between key entities.
(claim 4) wherein the data environment comprises a site compartment, a surroundings compartment, a setting compartment and a data lake, wherein the data lake includes geospatial data, tabular data and documents.
(claim 6) wherein the decision nodes of the decision tree includes an attribute, wherein the attribute includes a binary question for answering in response to the node being traversed.
(claim 7) calculating the remediated risk score from the product of the initial risk score and the remediation score for any time point.
(claim 8) calculating a total expectation of a risk by taking the integral of the remediated risk score over a change in time.
(claim 14) normalizing, by the one or more processors, the remediation score for a site to other sites.
(claim 15) weighting, by the one or more processors, at least one of the harm reduction data, the cost data or the sustainability data.
(claim 12) attenuating, by the one or more processors, the risk score based on at least one of an enforcement environment or regulatory resource restraints.
(claim 10) obtaining, by the one or more processors, new data from other sites as part of a feedback loop to augment the harm reduction data, the cost data, the sustainability data, the resiliency data, the extreme event risk score and the data environment; and
(claim 10) re-training, by the one or more processors, the predictive model using the new data from the other sites.
(claim 13) wherein the extreme event risk score is based on a probabilistic rating that reflects how likely a site is to take an abnormally long time to close relative to similar sites.
(claim 16) wherein the harm reduction data comprises an ability of a remediation option to destroy or immobilize contamination, rendering the contamination less harmful to human health or the environment.
(claim 17) wherein the cost data comprises a financial requirement to design and enact a remediation option.
(claim 18) wherein the sustainability data comprises an environmental footprint of the remedial option.
(claim 19) A system comprising: one or more processors; and one or more tangible, non-transitory memories configured to communicate with the one or more processors, the one or more tangible, non-transitory memories having instructions stored thereon that, in response to execution by the one or more processors, cause the one or more processors to perform operations comprising:
(claim 19) developing, by the one or more processors, an extreme event risk score based on operational environment data, legislative environment data and a data environment;
(claim 19) parsing and converting, by the one or more processors, the text documents to at least one of a directed graph or state-specific decision trees using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines. Examiner’s Note: While these limitations are not identical, they are not patentably distinct. Specifically, the instant application states that the method parses and converts “the operational environment data, the legislative environment data and the data environment” while the issued patent parses and converts, “the text documents.” However, claim 19 of the issued patent states that the text documents include the legislative environment data and the data environment (e.g., “ingesting, by the one or more processors, legislative documents from the legislative environment and into the data environment; saving, by the one or more processors, the legislative documents as text documents in a data environment”). It would have been obvious to one of ordinary skill in the art that the operational environment data could also be parsed and converted into a directed graph as described in the issued patent regarding the text documents
(claim 19) determining, by the one or more processors, a remediation score based on a plurality of remediation terms including harm reduction data, cost data, sustainability data, resiliency data, the extreme event risk score and the data environment;
(claim 19) determining, by the one or more processors, how each remediation term of the plurality of remediation terms influences the remediation score by combining each of the plurality of remediation terms via linear combination with a weighting term, wherein the weighting term is determined by statistical analysis of at least one of previous sites, portfolio priorities or an emphasis of a regulatory environment on each of the plurality of remediation terms;
(claim 19) adjusting, by the one or more processors, probabilities of the extreme event risk score based on a severity of an outcome to obtain adjusted probabilities, wherein the adjusted probabilities are visually indicated by a weight of the edges in the decision tree;
(claim 19) adjusting the weight of the edges in the decision tree by using the machine learning models;
(claim 19) computing, by the one or more processors, a training objective by re-calculating probabilities underlying risk calculations of the initial risk score, by using the adjusted probabilities of the extreme event risk score and by defining portfolio priorities with the operational environment data and the legislative environment data;
(claim 19) updating, by the one or more processors, the predictive model to create an updated predictive model, based on the training objective; and
(claim 19) improving, by the one or more processors using the updated predictive model, an accuracy of the initial risk score and the risk rating to improve prediction accuracies for the other sites.
Therefore, because claims 1, 2, 4, 6-8, 10, and 12-19 the issued patent teach each limitation of claims 1-20 of the instant application, claims 1-20 of the instant application are anticipated by claims 1, 2, 4, 6-8, 10, and 12-19 of the issued patent.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims 1-20 are rejected under 35 U.S.C. §101 because the claimed invention recites and is directed to a judicial exception to patentability (i.e., a law of nature, a natural phenomenon, or an abstract idea) and does not include an inventive concept that is “significantly more” than the judicial exception under the January 2019 and October 2019 patentable subject matter eligibility guidance (2019 PEG) analysis which follows.
Step 1
7. Under the 2019 PEG step 1 analysis, it must first be determined whether the claims are directed to one of the four statutory categories of invention (i.e., process, machine, manufacture, or composition of matter). Applying step 1 of the analysis for patentable subject matter to the claims, it is determined that the claims are directed to the statutory category of a process (claims 1-19) and a machine (claim 20); where the machine is substantially directed to the subject matter of the process (See e.g., MPEP §2106.03). Therefore, we proceed to step 2A, Prong 1.
Step 2A, Prong 1
8. Under the 2019 PEG step 2A, Prong 1 analysis, it must be determined whether the claims recite an abstract idea that falls within one or more designated categories of patent ineligible subject matter (i.e., organizing human activity, mathematical concepts, and mental processes) that amount to a judicial exception to patentability.
Claim 1 recites the abstract idea of:
A method comprising: developing, [[by one or more processors]], an extreme event risk score based on operational environment data, legislative environment data and a data environment;
parsing and converting, [[by the one or more processors]], the operational environment data, the legislative environment data and the data environment to at least one of a directed graph or state-specific decision trees [[using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines]];
determining, [[by the one or more processors]], a remediation score based on a plurality of remediation terms including harm reduction data, cost data, sustainability data, resiliency data, the extreme event risk score and the data environment;
determining, [[by the one or more processors]], how each remediation term of the plurality of remediation terms influences the remediation score by combining each of the plurality of remediation terms via linear combination with a weighting term,
wherein the weighting term is determined by statistical analysis of at least one of previous sites, portfolio priorities or an emphasis of a regulatory environment on each of the plurality of remediation terms;
adjusting, [[by the one or more processors]], probabilities of the extreme event risk score based on a severity of an outcome to obtain adjusted probabilities,
improving, [[by the one or more processors using the updated predictive model]], an accuracy of the initial risk score and a risk rating to improve prediction accuracies for other sites.
Here, the recited abstract idea falls within one or more of the three enumerated 2019 PEG categories of patent ineligible subject matter, to wit: certain methods of organizing human activity, which includes fundamental economic practices or principles and/or commercial interactions (e.g., mitigating risk).
Step 2A, Prong 2
9. Under the 2019 PEG step 2A, Prong 2 analysis, the identified abstract idea to which claim 1 is directed does not include limitations or additional elements that integrate the abstract idea into a practical application.
Besides reciting the abstract idea, the limitations of claim 1 also recite generic computer components (e.g., one or more processors, artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines, machine learning models, a predictive model, and an updated predictive model). In particular, the recited features of the abstract idea are merely being applied on a computer or computing device or via software programming that is simply being used as a tool (“apply it”) to implement the abstract idea. (See e.g., MPEP §2106.05(f)). Therefore, these additional elements are recited at a high level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components. In other words, the additional elements are simply used as tools to perform the abstract idea.
Additionally, claim 1 recites the following limitations:
adjusting, by the one or more processors, the weight of the edges in the decision tree by using machine learning models;
computing, by the one or more processors, a training objective by re-calculating probabilities underlying risk calculations of an initial risk score, by using the adjusted probabilities of the extreme event risk score and by defining portfolio priorities with the operational environment data and the legislative environment data; and
updating, by the one or more processors, the predictive model to create an updated predictive model, based on the training objective.
These limitations recite process steps for using machine learning to adjust the weights of edges of a decision tree, determining a training objective, and updating the predictive model based on the training objective. However, the claims do not provide significant technical detail regarding how these processes are implemented. For example, simply stating that the predictive model is updated based on the training objective does not provide any indication of an improvement to predictive modeling technology. These limitations provide no technical detail regarding how the training objective is determined and/or how it is implemented to update the predictive model. Therefore, these limitations amount to no more than merely applying generic machine learning/predictive modeling technology to implement the abstract idea on a computer.
Claim 1 also recites the following limitation:
wherein the adjusted probabilities are visually indicated by a weight of edges in a decision tree.
This limitation merely states that the method includes visually displaying the weight of the edges in the decision tree. However, the claim does not provide significant technical detail regarding how the weights are displayed. Therefore, this limitation amounts to no more than merely outputting/displaying data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, claim 1 does not include any limitations or additional elements that integrate the abstract idea into a practical application. As a result, claim 1 is directed to an abstract idea.
Step 2B
10. Under the 2019 PEG step 2B analysis, the additional elements of claim 1 are evaluated to determine whether they amount to something “significantly more” than the recited abstract idea. (i.e., an innovative concept). Here, the recited additional elements (e.g., one or more processors, artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines, machine learning models, a predictive model, and an updated predictive model), do not amount to an innovative concept since, as stated above in the Step 2A, Prong 2 analysis, the claims are simply using the additional elements as a tool to carry out the abstract idea (i.e., “apply it”) on a computer or computing device and/or via software programming (See e.g., MPEP §2106.05(f)). The additional elements are specified at a high level of generality such that they are being used in the claims to simply implement the abstract idea and are not themselves being technologically improved (See e.g., MPEP 2106.05(I)(A)); (See also applicant’s Specification at least Paragraphs 109-122).
Additionally, the following limitation identified above as insignificant extra-solution activity (merely outputting/displaying data) has been reevaluated under Step 2B:
wherein the adjusted probabilities are visually indicated by a weight of edges in a decision tree
As stated in MPEP 2106.05(d), a factual determination is required to support a conclusion that an additional element (or combination of additional elements) is well-understood, routine, conventional activity (Berkheimer v. HP, Inc., 881 F.3d 1360, 1368 (Fed. Cir. 2018)). In view of this requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Thus, claim 1 does not recite any additional elements that amount to “significantly more” than the abstract idea.
Additional Independent Claims
11. Independent claim 20 similarly rejected under 35 U.S.C. 101 for the reasons described below:
Claim 20 recites limitations that are substantially similar to those recited in claim 1. However, the primary difference between claims 20 and 1 is that claim 20 is drafted as a system rather than as a method. Similarly, as described above regarding claim 1, claim 20 recites generic computer components (e.g., one or more processors; one or more tangible, non-transitory memories configured to communicate with the one or more processors; machine learning models; a predictive model; and an updated predictive model) that are simply being used as a tool (“apply it”) to implement the abstract idea. Therefore, since the same analysis should be used for claims 1 and 20, claim 20 is not patent eligible (See Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 134 S. Ct. 2347, 2354 (2014)).
Dependent Claims
12. Dependent claims 2-19 are also rejected under 35 U.S.C. 101 for the reasons described below:
Claim 2 simply further refines the abstract idea because it recites a process step (e.g., determining a remediated risk score) that falls under the category of organizing human activity, as described above regarding claim 1.
Claim 3 simply further refines the abstract idea because it recites a process step (e.g., determining a risk rating for the site pre-remediation and post-remediation) that falls under the category of organizing human activity, as described above regarding claim 1.
Claim 4 recites process steps for converting text documents into a directed graph which represents a state regulatory framework. However, the claim does not provide significant technical detail regarding how the directed graph is generated. The claim states that the directed graph is generated using “artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines.” However, the claim does not provide any detail regarding how the artificial intelligence functions. Therefore, such limitations amount to no more than merely applying generic artificial intelligence technology to implement the abstract idea on a computer.
Claim 5 recites process steps for obtaining “directed knowledge” from text documents, and “tagging” the directed knowledge as a decision node or an outcome node. However, the claim does not provide significant technical detail regarding how this information is obtained and/or tagged. Therefore, such limitations amount to no more than mere data gathering, which is a form of insignificant extra-solution activity (See MPEP 2106.05(g): OIP Techs., Inc. v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015) and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2024)). In view of the requirement set forth by Berkheimer, these limitations do not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of mere data gathering to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015); and buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, (Fed. Cir. 2014)).
Claim 6 recites the limitations, “ingesting, by the one or more processors, legislative documents from the legislative environment and into the data environment; saving, by the one or more processors, the legislative documents as text documents in a data environment.” These limitations simply state that legislative documents are saved/stored in a data environment. However, the claim does not provide significant technical detail regarding how the data is stored. Therefore, such limitations amount to no more than merely storing data, which is a form of insignificant extra-solution activity (See MPEP 2016.05(d): Versata Dev. Group, Inc. v. SAP Am., Inc., 793F.3d 1306, 1334 (Fed. Cir. 2015); and OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d at 1363). In view of the requirement set forth by Berkheimer, this limitation does not integrate the abstract idea into a practical application, or amount to significantly more than the abstract idea, because the courts have found the concept of merely outputting/displaying data to be well-understood, routine, and conventional activity (See MPEP 2106.05(d): OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363 (Fed. Cir. 2015)).
Additionally, claim 6 recites the limitations, “parsing, by the one or more processors, the text documents; converting, by the one or more processors, the text documents to a directed graph; re-training, by the one or more processors, a predictive model with definitions of categories of each section of the legislative documents; implementing, by the one or more processors, content segmentation of the legislative documents to categorize each section of the legislative documents into the categories by type; and re-training, by the one or more processors, the predictive model using the risk rating and the legislative documents.” These limitations recite a process for training and implementing a predictive model to convert text documents into a directed graph and categorize sections of the legislative documents. However, the claim does not provide significant technical detail regarding how the predictive model is trained and/or implemented. Therefore, such limitations amount to no more than merely applying a generic predictive model to implement the abstract idea on a computer.
Claim 7 recites the limitation, “building, by the one or more processors, a knowledge graph using deep learning technologies, word embeddings, text summarization, embedded provenance and topic modeling.” However, the claim does not provide significant technical detail regarding how the knowledge graph is built. Simply stating that the knowledge graph is built using “deep learning technologies, word embeddings, text summarization, embedded provenance and topic modeling” does not integrate the abstract idea into a practical application. Rather, this amount to no more than merely applying generic computer-related technologies to implement the abstract idea on a computer.
Additionally, claim 7 recites the limitations, “extracting, by the one or more processors, cross-referenced rules from the knowledge graph; and identifying and classifying, by the one or more processors, the cross-referenced rules into the deontic expressions.” These limitations simply further refine the abstract idea because they recite a process step (e.g., extracting rules from a knowledge graph, and identifying/classifying the rules into deontic expressions) that falls under the category of organizing human activity, as described above regarding claim 1.
Claim 8 simply further refines the abstract idea because it recites process steps (e.g., extracting/validating information form the knowledge graph) that fall under the category of organizing human activity, as described above regarding claim 1. The claims do not provide significant technical detail regarding how the various information is extracted and/or validated.
Claim 9 simply provides further definition to the “data environment” recited in claim 1. Simply stating that the data environment comprises various “compartments” does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of data that is stored within the data environment.
Claim 10 simply provides further definition to the “decision tree” recited in claim 1. Simply stating that the decision tree comprises decision nodes which include a binary question does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of data that is stored within the decision tree.
Claim 11 simply further refines the abstract idea because it recites a process step (e.g., calculating a remediated risk score and a total expectation of risk) that falls under the category of organizing human activity, as described above regarding claim 1.
Claim 12 simply further refines the abstract idea because it recites a process step (e.g., normalizing the remediation score for a site to other sites) that falls under the category of organizing human activity, as described above regarding claim 1. The claim does not provide any technical detail regarding how the remediation score is normalized.
Claim 13 simply further refines the abstract idea because it recites a process step (e.g., weighting at least one of the harm reduction data, the cost data or the sustainability data) that falls under the category of organizing human activity, as described above regarding claim 1. The claim does not provide any technical detail regarding how the data is weighted.
Claim 14 simply further refines the abstract idea because it recites a process step (e.g., attenuating the risk score based on at least one of an enforcement environment or regulatory resource restraints) that falls under the category of organizing human activity, as described above regarding claim 1.
Claim 15 recites a process for re-training the predictive model based on new-data received from other sites. However, the claim does not provide significant technical data regarding how the re-training process is performed. Therefore, such limitations amount to no more than applying generic machine learning training technology to implement the abstract idea on a computer.
Claim 16 simply provides further definition to the “extreme event risk score” recited in claim 1. Simply stating that the extreme event risk score is based on a probabilistic rating that reflects how likely a site is to take an abnormally long time to close relative to similar sites does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines how the extreme event risk score is determined.
Claim 17 simply provides further definition to the “harm reduction data” recited in claim 1. Simply stating that the harm reduction data comprises an ability of a remediation option to destroy or immobilize contamination, rendering the contamination less harmful to human health or the environment does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of data included in the harm reduction data.
Claim 18 simply provides further definition to the “cost data” recited in claim 1. Simply stating that the cost data comprises a financial requirement to design and enact a remediation option does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of data included in the cost data.
Claim 19 simply provides further definition to the “sustainability data” recited in claim 1. Simply stating that the sustainability data comprises an environmental footprint of the remedial option does not provide an indication of an improvement to any technology or technological field. Rather, this merely defines the type of data included in the sustainability data.
Thus, the dependent claims do not add any additional element or subject matter that provides a technological improvement (i.e., an integration into a practical application) that results in the claims being directed to patent eligible subject matter or include an element or feature that is significantly more than the recited abstract idea (i.e., a technological inventive concept under Step 2B).
Response to Arguments
13. Applicant’s arguments filed May 18, 2026 have been fully considered.
Arguments Regarding Double Patenting
14. On page 8 of their remarks, the applicant argues, “As such, Applicant respectfully requests that the Examiner continue to hold this rejection in abeyance until the Examiner agrees to allowable claims, and then re-submit the request for a Terminal Disclaimer based on the pending claims at that time, if the Examiner still believes a Terminal Disclaimer is necessary.” The examiner agrees that a terminal disclaimer would be sufficient to overcome the double patenting rejection. However, the rejection will be maintained and updated until the claims are sufficiently differentiated from the issued patent, or a terminal disclaimer is filed.
Arguments Regarding 35 U.S.C. 112
15. All prior rejections under 35 U.S.C. 112 have been withdrawn in response to the applicant’s claims amendments.
Arguments Regarding 35 U.S.C. 101
16. Applicant’s arguments (Amendment, Pgs. 9-11) concerning the prior rejection of the claims under 35 USC §101, including supposed deficiencies in the rejection, are not persuasive for the following reasons. Under the prior and current 101 analysis under 2019 PEG, the amended claims recite and are directed to a patent ineligible abstract idea, without something significantly more, for the reasons given above after consideration of the claimed features and elements. The abstract idea has been restated herein in line with the 2019 PEG guidance and the amended claims. Applicant is directed to the above full Alice/Mayo analysis in the 101 rejection.
Additionally, on page 9 of their remarks, the applicant argues, “Applicant asserts that the pending claims recite additional elements which provide significantly more than the abstract idea. Specifically, the totality of the claims amounts to significantly more than simply generating a risk score for site. The claims recite limitations for utilizing artificial intelligence models and natural language routines for parsing and extracting information from legislative documents, and generating a directed graph which helps the user identify potential outcomes of the remediation process for the site. Applicant asserts that such limitations amount to significantly more than simply gathering data from documents. Therefore, such limitations, when considered in combination, apply the judicial exception in a way that is beyond a general linking to the technological environment and/or merely applying generic computer components to implement the abstract idea on a computer.” Similar arguments are presented on pages 10 and 11 of the applicant’s remarks. The examiner respectfully disagrees. Specifically, the examiner notes that the claims provide little technical detail regarding how the processes of parsing/extracting information from legislative documents is performed. For example, claim 1 was amended to recite the limitation, “parsing and converting, by the one or more processors, the operational environment data, the legislative environment data and the data environment to at least one of a directed graph or state-specific decision trees using artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines.” This limitation simply states that the operational environment data, the legislative environment data, and the data environment are “parsed” to generate the directed graph. The claims provide little technical detail regarding how the parsing operation is performed. Similarly, simply stating that the parsing and converting operations are performed using “artificial intelligence (AI) models that include deep learning models and natural language processing (NLP) routines” does not integrate the abstract idea into a practical application. The claims provide little technical detail regarding the AI models and/or the NLP routines and how they function. Therefore, such limitations, when considered individually and/or in the context of the claim as a whole, amount to no more than merely applying generic computer-related components to implement the abstract idea on a computer.
Therefore, for these reasons and the reasons given above, the rejection of these claims under 35 U.S.C. §101 is maintained.
Citation of Pertinent Prior Art
17. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Beverina (U.S. Patent No. 9292874): Describes an integrated risk management tool that includes a persistent object database to store information about actors (individuals and/or groups), physical surroundings, historical events and other information. The risk management tool also includes a decision support system that uses data objects from the database and advanced decision theory techniques, such as Bayesian Networks, to infer the relative risk of an undesirable event.
D’Amelia (U.S. Patent No. 10726957): Describes an automated and comprehensive communications and monitoring platform that enables conditions to be monitored at one or more sites, and which includes a set of tools for preventing, mitigating and/or remediating occurrences of unfavorable conditions, such as hazardous biological/chemical conditions or device failures at the sites.
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILLIAM D NEWLON whose telephone number is (571)272-4407. The examiner can normally be reached Mon - Fri 8:30 - 4:30.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Matthew Gart can be reached at (571) 272-3955. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
Prosecution Insights