APARELHO E MÉTODO PARA ANÁLISE DA ATIVIDADE REPETITIVA

Abstract

An archive of activity data has actions associated with individual unique identifiers. Reporting cycles are defined. Activity data in each reporting cycle is analyzed. Lookback windows covering various segments of activity data are defined. Unpopulated repeater cohort lists and an unpopulated single instance cohort list are populated by grouping unique identifiers associated with similar localized repetitive activity relative to each reference event to form populated repeater cohort lists and a populated single instance cohort list. For each reporting cycle it is validated that each cohort list is mutually exclusive. The populated repeater cohort lists and the populated single instance cohort list are utilized as organizing keys to enable discriminatory parsing and analyzing of ancillary attributes to form output data. The output data is appended to the archive.

Description

1 / 15 “APPARATUS AND METHOD FOR ANALYZING REPETITIVE ACTIVITY” REFERENCE TO RELATED APPLICATIONS

[001] This application claims priority to U.S. Provisional Patent Application 63 / 491,685, filed March 22, 2023, the content of which is incorporated herein by reference. FIELD OF THE INVENTION

[002] This request generally relates to digital analysis of large datasets. More specifically, this request is directed to the digital analysis of repetitive activities in large datasets. BACKGROUND OF THE INVENTION

[003] Advances in cloud computing, personal mobile devices, natural language processing, machine learning, artificial intelligence, and secure broadband wireless connectivity support increased scale and control to quantitatively assess and influence consumer behavioral responses. Collectively, these technological capabilities increase the connection and value of business operators' product and service offerings to individual consumers.

[004] Existing scientific software applications and methods are known in cross-platform consumer tracking, multi-source database attribute analysis, token-based loyalty programs, targeted advertising engagement, and customer-based evaluation. However, much of this work focuses on supply-side product sales data and does not directly isolate demand-side consumer purchase data. The use of supply-side data presents operational shortcomings, including, but not limited to, the inability to integrate closed-loop feedback control methods with cloud-based analytics and predictive forecasting for demand-side measurements. Petition 870250083881, dated 09 / 17 / 2025, page 9 / 31 2 / 15

[005] Therefore, there is a need to improve demand-side data analysis in large datasets, particularly with regard to assessing repetitive activity manifested in these data. SUMMARY OF THE INVENTION

[006] An activity data file has actions associated with individual unique identifiers. Reporting cycles are defined. The activity data in each reporting cycle is analyzed. Backlash windows are defined covering multiple segments of activity data. Unpopulated repeater cohort lists and an unpopulated single-instance cohort list are populated by grouping unique identifiers associated with similar localized repeating activities relative to each reference event to form populated repeater cohort lists and a populated single-instance cohort list. For each reporting cycle, it is validated that each cohort list is mutually exclusive.The populated repeater cohort lists and the populated single-instance cohort list are used as organizing keys to enable discriminant assessment and analysis of auxiliary attributes to form output data. The output data is appended to the file. BRIEF DESCRIPTION OF THE FIGURES

[007] The invention is more fully appreciated in connection with the following detailed description taken together with the accompanying drawings, in which:

[008] FIGURE 1 illustrates a system configured according to an embodiment of the invention.

[009] FIGURE 2 illustrates processing operations associated with an embodiment of the invention.

[010] Similar reference numbers refer to corresponding parts in the various views of the drawings. Petition 870250083881, dated 09 / 17 / 2025, p. 10 / 31 3 / 15 DETAILED DESCRIPTION OF THE INVENTION

[011] FIGURE 1 illustrates a system 100 configured according to an embodiment of the invention. The system 100 includes client machines 102_1 to 102_N connected to server machines 104_1 to 104_N via a network 106, which can be any combination of wired and wireless networks. The data source machines 150_1 to 150_N are also connected to the network 106.

[012] Server machines 104_1 to 104_N implement operations disclosed in the present invention. By way of overview, server machines 104_1 to 104_N collect data from data source machines 150_1 to 150_N. The data is analyzed with a repetitive activity analyzer 142 to obtain meaningful insights, as demonstrated below.

[013] Each client machine 102_1 to 102_N includes a processor 110 and input / output devices 112 connected via a bus 114. The input / output devices 112 may include a keyboard, mouse, touch screen, and the like. A network interface circuit 116 is also connected to the bus 114 to provide connectivity to the network 106. A memory 120 is also connected to the bus 114. The memory 120 stores instructions executed by the processor 110. In particular, the memory stores a client module 122 that includes instructions for using the network 106 to communicate with the servers 104_1 to 104_N. Thus, each client machine 102_1 to 102_N can access the output of the repetitive activity analyzer 142.

[014] Each server (e.g., server 104_1) includes a processor 130, input / output devices 132, a bus 134, and a network interface circuit 136 to provide connectivity to the network 106. A memory 140 is also connected to the bus 134. The memory stores instructions executed by the processor 130 to implement operations disclosed in the present invention. In Petition 870250083881, dated 09 / 17 / 2025, page 11 / 31 4 / 15 In particular, the memory stores a repetitive activity analyzer 142, whose operations are detailed below.

[015] Each data server (e.g., 150_1) includes a processor 151, input / output devices 152, a bus 154, and a network interface circuit 156 to provide connectivity to the network 106. A memory 160 is also connected to the bus 154. The memory 160 stores a data source module 162 with instructions executed by the processor 151 to deliver data to one or more servers 104_1 to 104_N. Examples of data sources are detailed below.

[016] Figure 2 illustrates the processing operations 200 performed by the repetitive activity analyzer 142. An activity data file is formed 200. For example, server 104_1 can interact with one or more data source machines 150_1 to 150_N to form a data file in memory 140. The activity data file specifies actions associated with individual unique identifiers. Each action has a number. The number can be a sequence number and / or a timestamp. In typical instantiations, the file is large, with 1 GB or more of data in memory 140.

[017] The next operation in Figure 2 is to specify the 202 reporting cycles. Each reporting cycle has a reporting cycle number. The reporting cycle is typically a time parameter, such as a day, a month, a quarter, etc.

[018] The next operation in Figure 2 is to form the reporting period data 204. The reporting period data is calculated for each reporting cycle. That is, for each reporting cycle, the activity data is analyzed to form the reporting period data.

[019] The transition point numbers are then identified as 206. The transition point numbers designate the beginning and end of a reporting cycle. Petition 870250083881, dated 09 / 17 / 2025, page 12 / 31 5 / 15 Therefore, transition point numbers are typically temporal parameters, such as a start timestamp and an end timestamp.

[020] The maximum activities are then identified in the 208 reporting cycles. That is, the activity data in each reporting cycle is analyzed to identify and store the occurrence of the maximum number of activities associated with a unique identifier. The unique identifier designates an individual. The activity number is an activity performed repeatedly by the individual and / or otherwise associated with him / her.

[021] Next, the backsliding windows are specified 210. Backsliding windows are typically time periods of increasing size, such as day, month, quarter, etc. Alternatively, backsliding windows can be based on increasing sequence numbers of activity data.

[022] Next, repetitive measures are produced 212. Repetitive measures are produced from an analysis of each reference event in the activity data for each unique identifier. The reference event is analyzed against previous activity data associated with the unique identifier within each feedback window to produce the repetitive measure of activity data currentness and frequency for each reference event.

[023] Trend analysis 214 is then performed. For each reporting cycle, a feedback window array of unique identifiers is stored. Each element of the array associates a reference event and its corresponding previous reference event instances to determine a trend. In one mode, an element of the array is set to true if a trend exists or false if a trend does not exist.

[024] Unpopulated lists are then created 216. That is, for each reporting cycle, lists of unpopulated repeater cohorts are formed, and an additional list of unpopulated single-instance cohorts is formed. Each list of Petition 870250083881, dated 09 / 17 / 2025, page 13 / 31 The 6 / 15 unpopulated repeater cohort is a repository for aggregating unique identifiers characterized by localized trends of similar repetitive activity. The unpopulated single instance cohort list contains the unique identifiers of individuals who have performed an activity once.

[025] The lists are then populated 218. The matrix elements are used to group unique identifiers associated with similar localized repeating activities in relation to each reference event. This results in populated repeater cohort lists and a populated single instance cohort list.

[026] The cohort lists are then validated 220. That is, for each reporting cycle, there is validation that each cohort list is mutually exclusive, so that the union of the activity data associated with the cohort lists matches the aggregated source activity data for the reporting cycle.

[027] The cohort lists are then analyzed with auxiliary attributes 222. That is, the completed repeater cohort lists and the completed single instance cohort list are used as organizing keys to enable discriminatory analysis and processing of auxiliary attributes, resulting in output data. Examples of auxiliary attributes are detailed below.

[028] The output data is then appended to file 224. The output data can also be transmitted over network 106 to one or more client machines 102_1 to 102_N.

[029] The above operations can be performed in a variety of contexts, including sports and entertainment, venue operations, manufacturing, biomedical, pharmaceutical, travel, recreation, social media, finance, sustainability, renewable energy, market valuation, e-commerce, and retail operations. The following examples demonstrate the above operations performed in different contexts. Petition 870250083881, dated 09 / 17 / 2025, page 14 / 31 7 / 15

[030] Selected contextual examples are organized into the following four high-level categories to demonstrate a variety of potential applications: market evaluation, biomedical, retail operations, sports and entertainment. Example of category 1 (simpler way to illustrate the main concepts): Market Valuation Loyalty Indicator

[031] An example in its simplest form is an application for loyalty trust in relation to market valuation. This example compares two similar business entities in the context of a contemplated institutional investment or merger / acquisition. General accounting practices provide financial reports in various forms: income statement, balance sheet, cash flow statement, market capitalization, sales / profit per point of distribution, etc. These reporting sources quantitatively assess the market value and performance of an entity, product, or service. In this example, two similar business entities, A and B, offer competitive services in a market.

[032] An activity data file is formed, comprising service subscription records for A and B. For each entity, the subscription records contain a unique identifier for each individual customer and associated data fields for subscription revenue and profit per customer. For example, a reporting cycle is monthly. For each reporting cycle, the file is analyzed to identify the most recent subscription activity for each unique identifier. In its simplest form, a retroactive window is specified with a fixed duration, for example, 135 days. For each subscriber, trend analysis is computed using the retroactive window to characterize them as a repeater or a one-off instance.

[033] The output data is presented as a ranked number of repeaters, unique instances, and churn, respectively for A and B. The outputs include a loyalty confidence index calculated based on more repeaters. Petition 870250083881, dated 09 / 17 / 2025, page 15 / 31 8 / 15 and less turnover. The loyalty index is then used as a robustness indicator to track market valuations using traditional methods established from information in financial reports.

[034] This example is intended to provide a simple illustration of the main concepts of the method presented. The additional feedback windows provide more granular levels of customer affinity, which can be extended to time histories that characterize volatility. Additional examples included below describe in more detail the procedural steps, intermediate results, control treatments, output data, and activity updates for different modalities and use cases of example applications. Example of category 2: Biosciences, Biomedical, Health and Pharmaceutical

[035] Another contextual example is a patient care application that uses activity-based bioscience analytics. Using the methods described and the repeater cohort structure, the output data allow for an initial assessment followed by passive and / or active monitoring of genetic predispositions and early detection of diseases and progression of neurological disorders.

[036] An activity data file is formed from patient records of genetic, physiological, clinical, imaging, biological specimen, biographical, previous injury, exposure, family history, and / or other associated data. Each patient record has a unique registration number and contains a unique identifier for an individual participant, referred to in this example as PatientID. Each data record associated with a PatientID further contains instances of the activity data described. Typical reporting cycles for healthy PatientIDs are annual. For PatientIDs participating in ongoing monitoring of a condition or illness, a typical reporting cycle might be Petition 870250083881, dated 09 / 17 / 2025, page 16 / 31 9 / 15 monthly, weekly, or more frequently, generally as determined by a medical professional.

[037] For each reporting cycle, activity data is analyzed to form reporting period data, such as physiological metrics of heart rate, respiration, blood pressure, core body temperature, among others. Maximum activities are the most recent patient records for each PatientID. The activity number is patient record data repeatedly associated with an individual PatientID that may also meet or exceed pre-established limits or norms (e.g., healthy human body temperature of 98.6 degrees F).

[038] Feedback windows are specified to produce repeatable measures of currentness and frequency of activity for each PatientID relative to the previous activity history for the same PatientID and / or to established health norms and standards of a general population. Trend analysis is calculated using a matrix that associates each reference activity level with its corresponding previous instances.

[039] Repeater cohort lists are formed and populated with unique identifiers for each PatientID to represent localized trends of similar repetitive activities from patient records, including unique instances. Repeater cohort lists are used as organizing keys to enable discriminant assessment and analysis of health / wellness measures, slow progression rates of known diseases or conditions, and other ancillary information. Output data are appended to the file for subsequent operations and analyses.

[040] As an example of a more specific biomarker, the detection of disease-associated alpha-synuclein in genetic assays was investigated, with results demonstrating high sensitivity and specificity as a biomarker indicator of severity or progression of Parkinson's disease or dementia. [refs: MJFF; Petition 870250083881, dated 09 / 17 / 2025, page 17 / 31 10 / 15 [Neurology Journal Nov 2022]. The application of the repeater cohort structure and method allows for greater differentiation and analysis of PatientIDs that exhibit similarities in underlying genetic factors and activity data from associated patient records.

[041] In a further variation of this example, a treatment is introduced in which a temporary medical protocol or therapeutic regimen is activated for selected / eligible candidate individual identifier PatientIDs. Output data is monitored before, during, and after the duration of the activated treatment to measure targeted responses and post-treatment responses, as generated in subsequent activity data updates. The feedback control treatment can be repeated and / or adapted recursively to achieve increasingly desirable outcomes (or to mitigate side effects), as measured by the output data in a deterministic closed-loop manner. Example of category 3: Retail operations, brand management, e-commerce

[042] Another contextual example is an application for retail operations. Using the methods described and the resulting repeater groups, the output data allows professionals to optimize pricing, promotions, product variety, store layout, inventory, other operational functions, and performance metrics.

[043] An activity data file is formed, comprising transaction records of point-of-sale (POS) information collected at the time of purchase. Purchase actions with unique identifiers for individual consumers detail product and / or service order information on each numbered order ticket. Typical reporting cycles are daily, weekly, monthly, quarterly, yearly, or other durations, along with corresponding transitions. Petition 870250083881, dated 09 / 17 / 2025, page 18 / 31 11 / 15

[044] For each reporting cycle, purchase data is analyzed to form the reporting period data. Maximum activities are the most recent purchase activities associated with a consumer uniquely identified with activity in the reporting period data. The number of activities is an activity performed repeatedly by or associated with the individual consumer.

[045] The feedback windows are specified to produce repeatable measures. The repeatable measures indicate the recency and frequency of each purchase transaction for each unique identifier representing a consumer. Trend analysis is calculated using a matrix that associates each reference purchase event with its corresponding previous purchase event instances.

[046] Repeater cohort lists are formed and populated with unique identifiers for each consumer to represent localized trends of similar repeat purchase activities, including unique first-time experimenters. Repeater cohort lists are used as organizational keys to enable discriminatory evaluation and analysis of product and / or service purchases and other ancillary information such as demographics, weather, time of day or day of the week, or other associated information.

[047] Output data is attached to the file and presented to professionals in the form of dashboards or visual / graphical presentations. New updates of purchase activity data can be integrated into the file at any time from various sources. After the completion of each subsequent or future reporting cycle, the activity data, including any instanced updates, is processed and the outputs are computed for presentation to professionals and attached to the file. Petition 870250083881, dated 09 / 17 / 2025, page 19 / 31 12 / 15

[048] In a variation of this example, a treatment is introduced in which a temporary promotion or price incentive is offered to selected individual identifiers. Output data are monitored before, during, and after the duration of the activated treatment to measure targeted responses and post-treatment decisions and behaviors. The feedback control treatment can be repeated and / or adapted recursively to achieve the desired results, measured by the output data in a deterministic, closed-loop manner.

[049] In another variation of this example, derived attributes are calculated using output data to analyze the file. The derived attributes are then employed to form components of a prompt compatible with generative artificial intelligence (GenAI), and in particular, GenAI employing a large language model (LLM) to personalize next best actions for individual consumers.

[050] These methods allow for optimizing the allocation of promotional and advertising costs / resources to target groups, with retail operators increasing operational efficiency through greater effectiveness of marketing programs, advertising campaigns, and other forms of product promotion to their consumer population. Example of category 4: Sports and entertainment, recreation, social, other applications

[051] Another contextual example is an application for sports and entertainment venue operations. Current industry practices by single and multi-venue operators include on-site and off-site ticketing (seasonal, group sales, individual seating; empty seats, seat upgrades), dynamic pricing, food and beverage, parking, security, gaming / betting, luxury suite / hospitality sales, alternative venue uses, merchandise sales, and various related third-party service contracts. Using Petition 870250083881, dated 09 / 17 / 2025, page 20 / 31 13 / 15 the methods described and cohorts of repeaters, the output data allow site operators to optimize efficiency in various operational functions.

[052] In this example, an activity data file is formed from ticket purchases and tokenized transactional data from in-game point-of-sale (POS) systems and collected per transaction. Unique identifiers associated with individual spectators correspond to each participant in games or other entertainment offered by the venue. Reporting cycles are per game, seasonal, annual, or other durations.

[053] For each reporting cycle, transactional data is analyzed to form the reporting period data. Maximum activities are the most recent transactions associated with a uniquely identified viewer. The number of activities is an activity performed repeatedly by or associated with an individual consumer, including previous history at the venue during other events operated by the venue.

[054] The feedback windows are specified to produce repeatable measures, including in-game and, optionally, before and after the game, if there is sufficient activity data history in the file for an individual spectator identifier. The repeatable measures indicate the recency and frequency of each transaction for each spectator. Trend analysis is computed using a matrix that associates each transaction with its corresponding previous instances.

[055] Repeater cohort lists are formed and populated with viewer identifiers to represent localized trends of similar repeat activities, including unique instances. Repeater cohort lists are used as organizational keys to enable discriminatory evaluation and analysis of previous viewer transactions and other ancillary attributes, such as demographics, current weather and / or weather forecast, day of the Petition 870250083881, dated 09 / 17 / 2025, pages 21 / 31 14 / 15 week or other situational occurrences in the game (e.g., halftime or intermission, 7th inning pause, rivalry game with a tied score, bases loaded with a full count and two outs, playoff spot dispute, overtime, imminent world record, etc.).

[056] Output data is attached to the file and presented to the site operator team in the form of dashboards or visual / graphical presentations. New activity data updates are integrated from various sources. After the completion of each subsequent or future reporting cycle, the activity data, including any instantiated updates, is processed and the outputs are computed for presentation and next actions.

[057] One embodiment of the present invention relates to a computer storage product with a computer-readable storage medium containing computer code for performing various computer-implemented operations. The media and computer code may be those specially designed and constructed for the purposes of the present invention, or they may be of a type well known and available to those skilled in the art of computer software. Examples of computer-readable media include, but are not limited to: magnetic media, optical media, magneto-optical media, and hardware devices specially configured to store and execute program code, such as application-specific integrated circuits (ASICs), programmable logic devices (PLDs), and ROM and RAM devices.Examples of computer code include machine code, as produced by a compiler, and files containing higher-level code that are executed by a computer using an interpreter. For example, one embodiment of the invention may be implemented using an object-oriented programming language and development tools. Another embodiment of the invention may be... Petition 870250083881, dated 09 / 17 / 2025, pp. 22 / 31 15 / 15 implemented in wired circuits in place of, or in combination with, machine-executable software instructions.

[058] The preceding description, for explanatory purposes, has used specific nomenclature to provide a complete understanding of the invention. However, it will be evident to one skilled in the art that specific details are not necessary to practice the invention. Thus, the preceding descriptions of specific embodiments of the invention are presented for illustrative and descriptive purposes. They are not intended to be exhaustive or to limit the invention to the precise forms disclosed; obviously, many modifications and variations are possible in view of the above teachings. The embodiments have been chosen and described to better explain the principles of the invention and its practical applications, thus enabling others skilled in the art to better utilize the invention and various embodiments with various modifications suitable for the specific use contemplated. The following claims and their equivalents are intended to define the scope of the invention. Petition 870250083881, dated 09 / 17 / 2025, pages 23 / 31

Claims

1 / 5 CLAIMS 1. A non-transient, computer-readable storage medium with instructions executed by a processor, CHARACTERIZED in that it is for: forming an activity data file comprising actions associated with individual unique identifiers, each action having a number, the file consuming at least 1 GB within the non-transient, computer-readable storage medium; specifying reporting cycles, each reporting cycle having a reporting cycle number; calculating activity data from each reporting cycle to form reporting period data; identifying and storing transition point numbers for all reporting cycles; analyzing activity data in each reporting cycle to identify and store the occurrence of the maximum number of activities associated with a unique identifier; specifying backlash windows as numerical progressions;Analyze, for each reference event in the activity data for each unique identifier, the same previous activity data associated with the unique identifier within each feedback window to produce a repeatable measure of the timeliness and frequency of activity data for each reference event; store, for each reporting cycle, an array of feedback windows of unique identifiers, in which each element of the array associates a reference event and its corresponding previous reference event instances to determine a trend to assign the array element true if valid and false if invalid;Petition 870250083881, dated 09 / 17 / 2025, page 24 / 31 2 / 5 assign for each reporting cycle lists of unpopulated repeater cohorts and an additional unpopulated single-instance cohort list, each unpopulated repeater cohort list being a repository for aggregating unique identifiers represented by localized trends of similar repetitive activity; populate the lists of unpopulated repeater cohorts and the unpopulated single-instance cohort list, using matrix elements to group unique identifiers associated with similar localized repetitive activity in relation to each reference event to form populated repeater cohort lists and a populated single-instance cohort list;Validate for each reporting cycle that each cohort list is mutually exclusive, so that the union of activity data associated with the cohort lists matches the aggregated source activity data for the reporting cycle; use the populated repeater cohort lists and the populated single-instance cohort list as organizing keys to enable discriminant evaluation and analysis of ancillary attributes to form output data; append the output data to the file; and transmit the output data over a network to a user.

2. Non-transient, computer-readable storage medium according to claim 1, CHARACTERIZED in that the number is a sequence number.

3. Non-transient, computer-readable storage medium according to claim 1, CHARACTERIZED in that the number is a date and time stamp.

4. Non-transient computer-readable storage medium, according to Petition 870250083881, dated 09 / 17 / 2025, page 25 / 31 3 / 5, with claim 1, CHARACTERIZED in that it further comprises instructions executed by the processor to receive new activity data to form an augmented file.

5. Non-transient computer-readable storage medium according to claim 4, CHARACTERIZED in that it further comprises instructions executed by the processor to: designate a current reporting cycle using initial and final conditions; analyze within the current reporting cycle using the augmented file to generate activity levels of the current reporting cycle and repeater cohorts, including single instance cohort; calculate new augmented file output data results for the current reporting cycle; store the new augmented file output data results for the current reporting cycle; and increment the current reporting cycle to a subsequent reporting cycle.

6. Non-transient computer-readable storage medium according to claim 4, CHARACTERIZED in that the augmented file is analyzed to form a next best predictive action.

7. Non-transient, computer-readable storage medium according to claim 4, CHARACTERIZED in that it further comprises instructions executed by the processor to augment new activity data with metadata, including a unique record locator, source timestamp, and most recent timestamp.

8. Non-transient, computer-readable storage medium according to claim 7, CHARACTERIZED in that it further comprises instructions executed by the processor to use metadata to prevent the use of a duplicate copy or a non-duplicate variation with an older date and time stamp of previously logged activity data.

9. Non-transient computer-readable storage medium according to claim 1, CHARACTERIZED in that it further comprises instructions executed by the processor to organize the output data according to auxiliary attributes.

10. Non-transient, computer-readable storage medium according to claim 9, CHARACTERIZED in that the auxiliary attributes are selected from a multi-level categorical hierarchy, brand information, demographic information, temporal information, spatial attributes, and meteorological information.

11. Non-transient, computer-readable storage medium according to claim 1, CHARACTERIZED in that it further comprises instructions executed by the processor to: specify a processing action activated by a pre-specified initial condition and deactivated by a pre-specified final condition; and generate processing activity data comprising processing actions associated with individual unique identifiers.

12. Non-transient, computer-readable storage medium according to claim 11, CHARACTERIZED in that the treatment is a promotional activity in a retail environment.

13. Non-transient, computer-readable storage medium according to claim 11, CHARACTERIZED in that the treatment is a medical procedure or therapeutic process.

14. A non-transient, computer-readable storage medium according to claim 11, CHARACTERIZED in that the processing is a content recommendation mechanism.

15. Non-transient, computer-readable storage medium, in accordance with Petition 870250083881, dated 09 / 17 / 2025, p. 27 / 31 5 / 5, claim 11, CHARACTERIZED by the fact that the processing is a regulatory action.

16. Non-transient computer-readable storage medium according to claim 1, CHARACTERIZED in that it further comprises instructions executed by the processor to generate lists of unique identifiers for the reporting cycles, wherein each list has unique identifiers for individuals with similar repetitive activity.

17. Non-transient computer-readable storage medium according to claim 16, CHARACTERIZED in that the repetitive activity is similar to an altered repetition rate.

18. Non-transient computer-readable storage medium according to claim 16, CHARACTERIZED in that similar repetitive activity is a first instance of a repetition rate. Petition 870250083881, dated 09 / 17 / 2025, pp. 28 / 31

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