GreatNorthCollective/greatnorth-us-federal-ai-inventory-2024 huggingface dataset:
2_use_case_name: CoralNet: Ongoing operational use, improvement, and development, of machine vision point classification
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
8_question_type: Coral reef conservation
10_commercial_ai: None of the above.
11_purpose_benefits: Computer vision for coral reef monitoring and conservation efforts.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/7/1905
22_dev_method: Developed with contracting resources.
29_contains_pii: No
30_saop_review: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: Coral Reef Watch
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_question_type: In production: more than 1 year
12_outputs: Remote sensing data interpretation and prediction
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: Steller sea lion brand sighting 
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_question_type: In production: more than 1 year
12_outputs: U-net model, using R and a package called TLC specifically designed for this project
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: CoralNet: Ongoing operational use, improvement, and development, of machine vision point classification.
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_question_type: In production: more than 1 year
12_outputs: FCNN
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: BANTER, a machine learning acoustic event classifier
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_question_type: In production: less than 1 year
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: CyberSecurity Hardening & Automation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Other
8_question_type: CyberSecurity Information & Configuration
10_commercial_ai: None of the above.
11_purpose_benefits: Promptly analyze cybersecurity architecture, documentation, inventories, and vulnerabilities for remediation, automation opportunities, continuous monitoring, responding to data calls, and architectural risk assessments (e.g. Security Impact Analyses [SIAs]).
12_outputs: Risk Assessment, documentation generation, responses to data calls
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 1/1/2024
19_date_acq_dev_began: 6/1/2024
22_dev_method: Developed in-house.
24_hisp_support: No
27_public_info: No
29_contains_pii: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: AI based Community Radiative Transfer Model
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Other
8_question_type: Satellite radiance assimilation for operational eather forecasts, physical retrievals of environmental data records
10_commercial_ai: None of the above.
11_purpose_benefits: Fast radiative transfer calculations for increasing satellite data needed by weathers forecasts and the generation of satellite products.
12_outputs: simulated satellite radiances and sensitivities (Jacobian) of satellite data
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 7/1/2024
19_date_acq_dev_began: 7/1/2024
20_date_implemented: 1/1/2027
22_dev_method: Developed in-house.
24_hisp_support: Yes
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: No
33_agency_data: Meteorological data such as profiles of temperature and water vapor
34_data_docs: Documentation is widely available: Documentation is not only complete, but is widely accessible within the agency, and has an owner and a regular update cadence.
37_custom_code: No
38_code_access: No – agency does not have access to source code.
40_has_ato: No
42_dev_tools_wait: More than 12 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Planned or in-progress.
53_real_world_testing: No testing: No testing of the model to simulate performance in an operational environment has been conducted thus far.
54_key_risks: Lack of funding
55_independent_eval: Yes – by another appropriate agency office that was not directly involved in the system’s development
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: Test, assess and feedback collection
63_stakeholder_consult: Direct user testing
65_appeal_process: No – it is not operationally practical to offer this.
66_no_appeal_reason: Very low risk to use this AI based application algorithm.
67_opt_out: Yes
2_use_case_name: AI Snowfall Detection and Snowfall Rate estimation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
10_commercial_ai: None of the above.
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: AI Rain Rate estimation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
10_commercial_ai: None of the above.
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: AI Total Precipitable Water estimation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
10_commercial_ai: None of the above.
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: AI Cloud Liquid Water estimation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: NOAA - National Oceanic and Atmospheric Administration
8_topic_area: Science & Space
10_commercial_ai: None of the above.
16_dev_stage: Initiated
17_impact_type: Neither
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: AI retrieval for patent search
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: Augmentation for next generation patent search tool to assist examiners identify relevant documents and additional areas to search.  System takes input from published or unpublished applications and provides recommendations on further prior art areas to search, giving the user the ability to sort by similarity to concepts of their choosing.
12_outputs: System takes input from published or unpublished applications and provides recommendations on further prior art areas to search, giving the user the ability to sort by similarity to concepts of their choosing.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 11/7/2018
19_date_acq_dev_began: 10/31/2019
20_date_implemented: 10/15/2021
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 1333BJ24C00280005
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
33_agency_data: The models are trained on Patent Publication information, including US Patent Publications, that are publicly available.
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Other
35_question_type: Does not use any demographic variables.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: PPL-Patent Search Artificial Intelligence (PTOC-00-060-00)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: AI retrieval for TM design coding and image search
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Clarivate COTS solution to assist examiner identification of similar trademark images, to suggest the correct assignment of mark image design codes, and to determine the potential acceptability of the identifications of goods and services.
12_outputs: System is anticipated to use both incoming trademark images and registered trademark images and output design codes and/or other related images.
16_dev_stage: Initiated
17_impact_type: Neither
18_date_initiated: 9/30/2021
24_hisp_support: No
27_public_info: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
2_use_case_name: Enriched Citation
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Summarizing the key points of a lengthy report using AI.
11_purpose_benefits: Data dissemination system that identifies which references, or prior art, were cited in specific patent application office actions, including: bibliographic information of the reference, the claims that the prior art was cited against, and the relevant sections that the examiner relied upon. Use of AI to extract summarized information from unstructured text is providing operational cost savings. 
12_outputs: System extracts information from unstructured office actions and provides the information through a structured public facing API.
16_dev_stage: Operation and Maintenance
17_impact_type: Neither
18_date_initiated: 9/18/2017
19_date_acq_dev_began: 11/7/2017
20_date_implemented: 3/21/2019
24_hisp_support: No
27_public_info: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: N/A - Not Safety or Rights impacting
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: Yes - All individual decisions or actions are automated and may be rendered without direct human interaction.
59_ai_notice: N/A - individuals are not interacting with the AI for this use case
61_adverse_impact: No
62_disparity_mitigation: N/A - Not Safety or Rights impacting
63_stakeholder_consult: Direct user testing
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Inventor Search Assistant (iSAT)
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Government Services (includes Benefits and Service Delivery)
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: Service to help inventors "get started" identifying relevant documents, figures, and classification codes used to conduct a novelty search.  Expected benefit was to provide continuity in service access to public search capabilities during COVID-19 related closure of in-person search assistance services. 
12_outputs: System takes a user entered short description of invention and provides a user selectable set of recommended documents, figures, and classification areas.
16_dev_stage: Retired
17_impact_type: Neither
18_date_initiated: 10/3/2019
19_date_acq_dev_began: 2/3/2020
20_date_implemented: 6/15/2021
21_date_retired: 10/18/2024
24_hisp_support: No
27_public_info: Yes
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: No
53_real_world_testing: Performance evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution, or has been tested in simulated or controlled environment using operational or synthetic data.
54_key_risks: N/A - Not Safety or Rights impacting
56_monitor_postdeploy: Intermittent and Manually Updated: A plan for monitoring the AI use case is in place, and requires data science teams to work with DevOps engineers to manually update models at scheduled intervals, and create metrics to detect data distribution shifts between the operational environment and the training data for the model. 
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: None of the above
61_adverse_impact: No
62_disparity_mitigation: N/A - Not Safety or Rights impacting
63_stakeholder_consult: Post-transaction customer feedback collections
65_appeal_process: Yes
67_opt_out: Yes
2_use_case_name: Patent Design Image Search (DesignVision)
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Mission-Enabling
10_commercial_ai: Searching for information using AI.
11_purpose_benefits: A SaaS AI driven image search tool to support the examination of design applications. 
12_outputs: The tool compares uploaded images from design applications and yields image results based on similarity thereby providing access, search, and retrieval of information from Industrial Design collections.
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 2022-11-16 (first formal action related to this project)
19_date_acq_dev_began: 2024-02-15 (contract kick-off); 2024-08-23 (signed Acquisition Plan)
20_date_implemented: 2024-10-16 (pilot licenses assigned)
22_dev_method: Developed with contracting resources.
23_contract_piids: 1333BJ24C00150001
24_hisp_support: No
27_public_info: No
29_contains_pii: Yes
30_saop_review: No
31_data_catalog: No
34_data_docs: Documentation is missing or not available: No documentation exists regarding maintenance, composition, quality, or intended use of the training and evaluation data.
37_custom_code: Yes
38_code_access: No – agency does not have access to source code.
40_has_ato: Yes
41_system_name: PTO-PPL-DV-DesignVision (PPL-PEC-02-00)
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: No
45_compute_request: No
47_timely_resources: No
49_existing_reuse: None: This use case does not re-use any internally developed tooling or managed infrastructure from any other AI development efforts within the agency.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.
2_use_case_name: Patent Automatic Document Code Determination
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Mission-Enabling
10_commercial_ai: Inputting large amounts of data from paper forms into a digital system using AI.
11_purpose_benefits: Quality control of customer applied document labels
12_outputs: Document Labels
16_dev_stage: Implementation and Assessment
17_impact_type: Neither
18_date_initiated: 2/1/2023
19_date_acq_dev_began: 2/1/2023
20_date_implemented: To Be Determined (calendar 2025)
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 280-EIPL-0762 
24_hisp_support: No
27_public_info: No
29_contains_pii: No
30_saop_review: No
31_data_catalog: Yes
33_agency_data: Publicly available open data of previous documents and their labels
34_data_docs: Documentation is complete: Documentation exists regarding the maintenance, composition, quality, and intended use of the training and evaluation data, as well as any statistical bias across model features and protected groups. 
35_demo_features: Other
35_question_type: Does not use any demographic variables.
37_custom_code: Yes
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: Yes
41_system_name: PE2E-DAV
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Documentation has been developed: Complete documentation detailing model performance across a range of benchmarks, architecture, relevant features and information regarding the appropriate use of the model for predictive tasks has been created.
51_extension_request: No – Agency did not request an extension for this use case.
52_impact_assessment: Yes
53_real_world_testing: Impact evaluation in operational environment: The AI use case has been tested in an operational environment before being fully implemented as a solution and has utilized randomized experiments with a control group or other counterfactual, social systems analysis, or other rigorous research methodologies to evaluate impact and identify potential harm to users as well as broader groups of people.
56_monitor_postdeploy: No monitoring protocols have been established: Necessary infrastructure needed to perform monitoring of AI systems in production is not available and/or a plan to monitor models has not yet been established.
57_autonomous_impact: No - Some individual decisions or actions require direct human oversight.
59_ai_notice: Other 
59_question_type: Only internal employees use the result of this use case. They are trained on the use of the results.
61_adverse_impact: No
63_stakeholder_consult: Other
63_question_type: Employee testing and review of predicted labels
65_appeal_process: Yes
67_opt_out: Other
67_question_type: A staff member can overrule the predicted label
2_use_case_name: Patents - Skill Group Matching
3_agency: Department of Commerce
3_abr: DOC
4_bureau: USPTO - United States Patent and Trade Office
8_topic_area: Mission-Enabling
10_commercial_ai: None of the above.
11_purpose_benefits: AI will permit adoption of Skill Groups system for delineating subject matter of patent applications by automating the allocation of Skill Group designations to applications.  Benefits:  Cost reduction from eliminating dependence on outdated US Patent Classification (USPC) system; cost savings from reducing Skill Group designation workload; improvement in availability of metrics data; improvements in ability to manage unexamined patent inventory and workforce allocation/hiring
12_outputs: Prediction of the correct Skill Group describing the technology/subject matter claimed in a patent application, along with a confidence score indicating the likelihood of each possible Skill Group.
16_dev_stage: Acquisition and/or Development
17_impact_type: Neither
18_date_initiated: 2022-08 (estimated)
19_date_acq_dev_began: 2022-09-13 Technical kickoff (no new acquisition - supported by existing contract)
22_dev_method: Developed with both contracting and in-house resources.
23_contract_piids: 280-EIPL-0762 (OY2)
24_hisp_support: Yes
27_public_info: No
29_contains_pii: No
31_data_catalog: Yes
33_agency_data: Full text of published patent documents; USPC classifications thereof; USPC-to-Skill Group correspondence table
34_data_docs: Documentation has been partially completed: Some documentation exists (detailing the composition and any statistical bias or measurement skew for training and evaluation purposes), but documentation took place within this use case’s development.
35_demo_features: Other
35_question_type: Does not use any demographic variables.
38_code_access: Yes – agency has access to source code, but it is not public.
40_has_ato: No
42_dev_tools_wait: Less than 6 months
43_infra_provisioned: Yes
45_compute_request: Yes
47_timely_resources: Yes
49_existing_reuse: Use of existing data platforms: This use case is being developed on existing enterprise data and analytics platforms within the agency rather than procuring additional platforms or SaaS to operate.
50_internal_review: Limited documentation for review: Some information regarding the model’s performance on specific benchmarks are available or have been shared with specific stakeholders within the agency.