{ "task_id": "consumerfinance_007", "domain": "CFPB_COMPLAINT_DATABASE", "autonomy_type": "ordered table", "oracle_output_cardinality": 4, "instruction": "I am drafting a layered analysis report on the core factors behind a batch of cryptocurrency complaints. I need your help to verify the true causes of this complaint surge step by step using the CFPB complaint database.\n\nFirst, define the base scope of the analysis. Using the current CFPB product and issue taxonomy documentation, confirm the product bucket that \"Virtual currency\" falls under. Once the bucket is determined, strictly restrict the time range to between 2020-01-01 and 2022-08-26, and only for the \"Virtual currency\" subcategory under that product bucket, extract the original peak month (BaselinePeak) and its total complaint count for this initial complaint cohort.\n\nSecond, determine the shift in peaks by progressively removing core influencing factors. Round 1: In the original peak month identified above, identify the most frequent \"Company public response\" value, then completely remove all complaints with that value from the entire initial cohort to obtain the recalculated second peak month (AfterDominantPublicResponse). Round 2: In the second peak month, identify the most frequent \"Issue\" type, remove it from the residual data of the previous round to lock in the third emerging peak month (AfterDominantPublicResponseAndIssue). Round 3: In the third peak month, identify the single most complained-about company (Company), remove that company, and perform the final round of statistics to obtain the final residual peak month (AfterDominantPublicResponseIssueAndCompany).\n\nThird, for each of the identified characteristic months, extract detailed data. For these four complaint clusters, besides obtaining the total complaint count for that month, also determine the newly added removal dimension and its specific excluded value, and the count of that excluded value in the month that served as its reference source. Additionally, identify the state with the highest complaint count in that month and its complaint count, the most complained-about company within that state and its complaint volume, and the complete information of the earliest complaint received by the highest-complaint company in that state during that characteristic month, including complaint ID, date, Issue, and Sub-issue.\n\nFinally, after completing the investigation and information collation for all the above dimensions, concatenate all extracted parameters for these four complaint clusters strictly in the order of BaselinePeak, AfterDominantPublicResponse, AfterDominantPublicResponseAndIssue, and AfterDominantPublicResponseIssueAndCompany into four strings with no spaces. Each line's field order is strictly fixed as: |||||||||||||. In case of ties, always select by lexicographic ascending order; for the earliest complaint with the same date, select by ascending Complaint ID numeric value. Normalize all empty values to NONE before sorting.", "start_url": "https://www.consumerfinance.gov/data-research/consumer-complaints/", "output_format": "Concatenate into four strings with no spaces, strictly in the order of BaselinePeak, AfterDominantPublicResponse, AfterDominantPublicResponseAndIssue, AfterDominantPublicResponseIssueAndCompany; each line's field order is strictly fixed as: |||||||||||||. In case of ties, always select by lexicographic ascending order; for the earliest complaint with the same date, select by ascending Complaint ID numeric value. Normalize all empty values to NONE before sorting.", "oracle_answer": "BaselinePeak|2021-05|336|NONE|NONE|0|CA|58|Coinbase, Inc.|21|4341451|2021-05-01|Money was not available when promised|NONE,AfterDominantPublicResponse|2022-07|31|Company public response|NONE|321|CA|6|BANK OF AMERICA, NATIONAL ASSOCIATION|4|5735227|2022-07-05|Other transaction problem|NONE,AfterDominantPublicResponseAndIssue|2021-10|13|Issue|Fraud or scam|26|TX|2|BANK OF AMERICA, NATIONAL ASSOCIATION|1|4821611|2021-10-18|Other transaction problem|NONE,AfterDominantPublicResponseIssueAndCompany|2021-06|8|Company|Foris DAX, Inc.|5|NC|3|TRUIST FINANCIAL CORPORATION|2|4462160|2021-06-15|Money was not available when promised|NONE", "metadata": { "State-Gated Retrieval": [ "Only consider Complaint Database records under the current product bucket's Virtual currency subcategory with a received date between 2020-01-01 and 2022-08-26.", "Determine the peak months for the four stages BaselinePeak, AfterDominantPublicResponse, AfterDominantIssue, and AfterDominantCompany sequentially as required.", "Each round's identified dominant Company public response, dominant Issue, and dominant Company must be written back and globally deleted from the corresponding upstream residual cohort before running the next round of statistics." ], "dependency_type": "Data + Control", "intra_chain": true, "inter_chain": true, "data_dependency": [ "current complaint-form taxonomy yields the product bucket for Virtual currency", "complaint records yield the baseline peak plus the dominant Company public response, dominant Issue, and dominant Company at successive residual-peak states", "final rows are produced only after each dominant value is applied to the full upstream residual cohort and then used to determine the next peak" ], "control_dependency": [ "dominant values found in a peak month cannot be deleted only inside that month; each must be propagated to the full upstream cohort", "each new residual peak depends on the prior round's global rewrite, so local month-by-month deletion gives the wrong chain", "downstream counts and exemplar complaints are valid only after the full three-stage waterfall is executed" ], "freeze": { "historical_window": "Complaint Database records received from 2020-01-01 through 2022-08-26 under the current product-bucket mapping for Virtual currency" }, "answer_type": "multi-row ordered table" }, "rubric": { "inclusion_conditions": [ "Only consider Complaint Database records under the current product bucket's Virtual currency subcategory with a received date between 2020-01-01 and 2022-08-26.", "Determine the peak months for the four stages BaselinePeak, AfterDominantPublicResponse, AfterDominantIssue, and AfterDominantCompany sequentially as required.", "Each round's identified dominant Company public response, dominant Issue, and dominant Company must be written back and globally deleted from the corresponding upstream residual cohort before running the next round of statistics." ], "exclusion_conditions": [ "Exclude results that only delete records locally within the current peak month without writing back to the entire upstream cohort.", "Exclude results that do not re-find the next peak month on each round's residual cohort.", "Exclude intermediate or final fields based on downstream statistics that were not fully re-run." ], "normalization": { "field_separator": "|", "record_separator": ",", "schema": [ "Cluster", "Month", "MonthComplaints", "NewExclusionType", "NewExclusionValue", "NewExclusionCountInSourcePeak", "TopState", "StateComplaints", "TopCompany", "CompanyComplaints", "EarliestComplaintID", "EarliestDate", "Issue", "SubIssue" ], "cluster_order": [ "BaselinePeak", "AfterDominantPublicResponse", "AfterDominantPublicResponseAndIssue", "AfterDominantPublicResponseIssueAndCompany" ], "date_format": { "Month": "YYYY-MM", "EarliestDate": "YYYY-MM-DD" }, "NewExclusionType": "emit the canonical exclusion-type label from the source workflow; when no new exclusion is applied, emit NONE", "NewExclusionValue": "emit the canonical excluded value; when no value exists, emit NONE", "TopState": "use the uppercase two-letter state abbreviation", "TopCompany": "use the company display name as shown in the source data", "tie_breaks": "normalize empty values to NONE before sorting; for other ties use lexicographic order ascending, and for the earliest complaint use EarliestDate ascending then Complaint ID ascending", "SubIssue": "emit NONE when the complaint row has no sub-issue value" } } }