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 .
Double Patenting
2. 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.
3. 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;
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 the edges in the decision tree;
adjusting the weight of the edges in the decision tree by using the 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, the 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, 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.
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 the 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;
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 the edges in the decision tree;
adjusting the weight of the edges in the decision tree by using the 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, the 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) 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) 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 Objections
4. The claims are objected to because of the following informalities, and the following is suggested to overcome the informalities and to improve claim clarity:
Claim 6 recites the limitation, “re-training, by the one or more processors, a predictive model with definitions of categories of each section of the legislative documents.” It appears that the predictive model recited in claim 6 refers to the same predictive model recited in claim 1. Therefore, claim 6 should be amended to clarify that these terms refer to the same predictive model. For example, claim 6 could be amended to state, “re-training, by the one or more processors, the predictive model with definitions of categories of each section of the legislative documents.”
Appropriate correction or clarification is requested.
Claim Rejections - 35 USC §112
5. The following is a quotation of 35 U.S.C. §112(b):
(b) CONCLUSION —The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. §112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
6. Claims 1-20 are rejected under 35 U.S.C. §112(b) or 35 U.S.C. §112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor, or for pre-AIA the applicant regards as the invention.
Claim 1 recites a plurality of terms for which there is insufficient antecedent basis in the claims. For example, claim 1 recites the limitation, “adjusting the weight of the edges in the decision tree by using the machine learning models.” There is insufficient antecedent basis for this limitation in the claim. Specifically, the claim does not introduce the terms “machine learning models” and “decision tree” before they are referred to in this limitation. For the purpose of examination, this limitation has been interpreted as if it stated, “adjusting the weight of the edges in a decision tree by using a plurality of machine learning models.” Similarly, claim 1 recites the limitation, “updating, by the one or more processors, the predictive model to create an updated predictive model.” the claim does not introduce the term “predictive model” before it is referred to in this limitation. For the purpose of examination, this limitation has been interpreted as if it stated, “updating, by the one or more processors, a predictive model to create an updated predictive model.
Since claim 20 has the substantially same issue as claim 1, claim 20 is rejected for the grounds and rationale used to reject claim 1. Since claims 2-19 include the respective limitations of claim 1, these claims are rejected for the grounds and rationale used to reject claim 1. Appropriate correction or clarification of these claims is required. No new matter may be added.
Claim 7 recites the limitation, “identifying and classifying, by the one or more processors, the cross-referenced rules into the deontic expressions.” There is insufficient antecedent basis for this limitation in the claim. Specifically, claim 1, from which claim 7 depends, does not introduce the term “deontic expressions” before it is referred to in this limitation. For the purpose of examination, this limitation has been interpreted as if it stated “identifying and classifying, by the one or more processors, the cross-referenced rules into deontic expressions.”
Since claim 8 includes the respective limitations of claim 7, this claim is rejected for the grounds and rationale used to reject claim 7. Appropriate correction or clarification of these claims is required. No new matter may be added.
Claim Rejections - 35 USC § 101
7. 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.
8. 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
9. 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
10. 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;
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
11. 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, 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 the weight of the edges in the decision tree by using the 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 the edges in the 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
12. 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, 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 the edges in the 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
13. 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
14. 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).
Citation of Pertinent Prior Art
15. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Sarkar (U.S. Pre-Grant Publication No. 20230077527): Describes a system, method, and article of a local agent system for obtaining hardware monitoring and risk information utilizing machine learning models.
Crabtree (U.S. Pre-Grant Publication No. 20210073915): Describes a system and method for event-based modeling and model refinement for natural catastrophe related risks, including but not limited to fires, floods, earthquakes, and hurricanes/tornados.
Netzer (U.S. Pre-Grant Publication No. 20160129787): Describes a system for generating directed graphs which provide driver decision making support.
Ocher (U.S. Pre-Grant Publication No. 20210097395): Describes a system for using a directed graph, comprising connected edges and associated weights, to generate a prediction using the neural network model.
Conclusion
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/WILLIAM D NEWLON/Examiner, Art Unit 3696
/MATTHEW S GART/Supervisory Patent Examiner, Art Unit 3696
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