academics.

Also, some applications of CASM, such as analyzing functional brain imaging data to elucidate how the brain works over time, are computationally intensive and require vastly expanding already large, publicly available datasets. Presumably, the patent acquirer would want to help drive demand for CASM by making modules of CASM software and other resources available to academics. 

Some of the most basic and straightforward yet beneficial applications of open CASM capabilities involve treatment assessment, when researchers can assess both treatment and health using Multivariate Time Series (MTS) data. This subsection introduces these treatment assessment opportunities. However, the CASM patent acquirer may need to pave the way forward with universities, as with funding and software assistance, for multiple reasons. For example, these treatment assessment opportunities are disruptive to regulatory standards for drug development and approval and are off the radar for funders and publishers. Furthermore, academics can be prone to the not-invented-here problem and to preferring overly complicated solutions to straightforward problems, such as longitudinal assessment of causality and quantification of evidence on the benefits and harms of drugs indicated for the prevention and management of chronic disorders. Needless to say, CASM addresses substantial market opportunities.

Accordingly, DataSpeaks® prompted Gemini 2026 as follows: “Many applications of CASM are open, meaning they are not patent protected. First, is 'open' the best term for making this distinction? In contrast, the CASM patents involve advanced capabilities, including estimating the CASM quantitative significance of TI and TI B&H scores. Note that CASM quantitative significance is to individuals what statistical significance is to pluralities, as when making inferences from samples to populations. Might this distinction between 'open' and patent-protected capabilities mean that the AI provider that purchases the CASM patent family be to able engineer and make available modules of CASM software to (i) provide vast amounts of CASM-enabled outputs that, in turn, drive demand for its AI with inputs that address the GIGO problem, (ii) drive demand for software that includes the patent-protected capabilities, and (iii) engage academics and others to conduct and publish proof-of-concept demonstrations and partner with the AI firm?” In turn, Gemini responded as follows.

In 2026, the strategy of leveraging "open" capabilities to drive demand for "proprietary" patent-protected features is a proven business model known as "Open Core." Using "open" as your primary term is highly effective because it signals transparency and community collaboration, which are essential for building trust in the "Year of Truth" (2026). 

The distinction you’ve drawn—where CASM quantitative significance provides individual-level precision equivalent to population-level statistical significance—is a powerful differentiator. It positions your patent-protected capabilities as the "high-fidelity" layer that makes the "open" foundational data actionable for personalized science. 

1. Accuracy of the Term "Open."

"Open" is the best term for your non-protected capabilities because it aligns with the 2026 industry shift toward "Open Standards" and "Interoperability."

• Open Core Model: You provide a "Community Edition" (the open CASM framework) that allows anyone to solve the GIGO problem at a basic level.

• Proprietary Extensions: You reserve the "Enterprise/Clinical Edition" for the patent-protected capabilities—specifically the quantitative significance algorithms for individuals. 

2. Strategy for an AI Provider (Buyer)

An AI firm purchasing the CASM patent family would find this "Dual-Track" strategy extremely effective for three reasons:

• i. Driving AI Demand through GIGO Resolution: By making "open" CASM modules available as standard plugins for AI models, the provider creates a market where CASM-enabled inputs are the baseline for "Truthful Science." This forces competitors to use your standard, while your own AI is natively optimized for it.

• ii. Creating Tiered Demand: The "open" modules allow for vast data generation, but as soon as a researcher needs to prove clinical or individual significance (e.g., for an FDA filing or personalized therapy), they must upgrade to the patent-protected modules.

• iii. Academic Engagement and Partnership: Academics are naturally drawn to "open" tools for their publications. As they conduct PoC demonstrations using the open CASM framework, they effectively provide the "R&D" that validates the entire ecosystem, making the proprietary "significance" layer even more valuable and sought-after for commercial applications.

Strategic Value: In 2026, this "hybrid" approach allows the AI firm to act as both a "Standards Body" (via open modules) and a "Premium Provider" (via patent-protected significance modules), securing a dominant position in the CAS Sciences. 

The Massive Garbage In, Garbage Out (GIGO) Problem for Statistics and AI

CAS sciences, including basic and applied sciences of people, brains, and cells, are in dire need of Truthful and Trustworthy Science and AI, as they labor under a massive Garbage In, Garbage Out (GIGO) problem in statistics and AI. The GIGO problem is unlikely to be solved with more Group Average Science (GAS), larger sample sizes, more data of the same kind, or more language of the kind currently available for training LLMs. 

Furthermore, expert-in-the-loop approaches to making AI outputs more trustworthy are limited because the quantitative scientific understanding of how CAS work (i.e., function, response, and agency) across time is in its infancy. Ignorance abounds. In contrast, CASM quantifies evidence of how individual CAS work in the time dimension, using universally standardized TI and TI B&H scores expressed in bagne z-score units.

Furthermore, TI and TI B&H scores are universal and apply regardless of the subject matter of the time series. For example, TI scores apply universally for brain function and economic time series.

Let persons epitomize CAS. Also, let the pharmaceutical industry, valued at roughly $600-$700 billion in the United States alone in 2024, represent the life sciences sector. Furthermore, start with treatment assessment on the critical path to drug approval.

The GIGO problem has a long history, at least as far back as 1948, when results of the first 'modern' Randomized Controlled Trial (RCT), streptomycin for tuberculosis, were published. This trial epitomizes a GAS RCT design. GAS study designs are cross-sectional and based on data snapshots, including baseline-to-endpoint change scores. GAS RCT designs often randomize subjects (humans not studied or treated like persons) to different dose groups, with placebo being dose zero. Then, the GAS study designs apply statistics to test primary hypotheses defined in terms of primary health response variable endpoints. 

GAS RCT designs were a breakthrough in 1948, yes, 1948. They still fit problems when treatments are truly categorical (e.g., having or not having a radical prostatectomy), and endpoints, such as death, are real. However, GAS RCT designs, most forcefully advocated by statisticians, do not assess causality or quantify evidence of safety and effectiveness for anyone. Nevertheless, the status quo extended GAS RCT designs to problems now better addressed with Longitudinal Causality Assessment (LCA) based on data movies of treatment action variables, such as drug dose, and response action variables assessable repeatedly while persons are still alive. LCA is a CASM application.

Eroom’s Law

Next, consider Eroom’s Law as originally described in 2012. Eroom’s Law (Moore's Law spelled backward) is the observation that drug approval became exponentially slower and more expensive over a six-decade period starting soon after 1948 (1950 – 2010), despite advances in technology, investments in research and development facilities, and advanced business practices. Eroom's law has been described repeatedly as a Trillion-Dollar Problem.

Eroom's law has yet to be reversed. Gemini 2026 described the problem/opportunity as follows: "Entering 2026, the industry continues to be highly sensitive to interest rates and discount rates, which directly dictate the cost of capital for long-duration R&D projects. While recent projected returns on investment (ROI) showed a slight uptick to 5.9% in 2024, much of this was concentrated in high-value areas like GLP-1 therapies; excluding these, the average return remains lower at 3.8%, often still trailing the overall cost of capital.”

Overcoming Eroom’s Law

The time is right to try RCT designs based on Longitudinal Causality Assessment (LCA), a CASM application. Such RCT designs could begin with non-human species or with approved drugs that need better targeting or repurposing. Unlike the conventional Group Average Science (GAS) RCT designs, which are cross-sectional, LCA RCT designs address the well-known ‘no one is average’ and ‘one size does not fit all’ problems. They also address the fact that drugs typically have multiple dose-dependent beneficial and harmful effects, all needing to be assessed BEFORE any applications of statistics.

The following are two introductory demonstrations of what DataSpeaks now calls Evidence-Based Medicine, Second Generation (EBM-2G) RCT designs. These designs apply LCA. 

First, here is a rudimentary single-person EBM-2G RCT design.

Figure 1 shows a small data movie with 35 repeated measures (frames) of one treatment action variable with five analgesic drug doses randomized longitudinally to weekly periods, and six safety and effectiveness response action variables. Although not shown, CASM users can detrend the response action variables before further data processing to help separate treatment effects from longer-term trends, such as from disease progression or healing.

Figure 2 includes the universally standardized Temporal Interaction Benefit and Harm (TI B&H) scores for each response action variable, expressed in bagne z-score units, centered on zero. CASM, NOT statistics, computed these scores. Figure 2 shows causal TI B&H scores, which are TI scores that the CASM user has set to be toward or beneficial in green and TI scores set to be untoward or harmful in red. Notice how CASM users can improve drug targeting by profiling TI B&H scores across many response action variables, which can include rating scale items used to evaluate treatment effects on anxiety, depression, and quality of life, etc. BEFORE lumping multiple items into a primary response variable score, as is typical for GAS RCT designs that test primary hypotheses defined directly on primary response variables. Profiling treatment effects across rating scale items rather than permanently lumping item-specific results will improve drug targeting.

Relatedly, see how this pain demo introduces how CASM users can use many response action variables simultaneously to help identify indications and contraindications. This CASM capability means that drug developers can initiate first-in-humans studies of new molecular entities or repurposed drugs with as many safety and effectiveness response action variables as possible to identify indications and contraindications, to identify treatment factors, and to drop out response action variables as experience allows. DataSpeaks refers to this approach as using a funnel wide-side-up, rather than upside-down, when uncertainty about the multiplicity of a drug's effects is high.

Figure 2 shows the overall TI B&H score, computed by averaging the differentially weighted response-action variable-specific TI B&H scores. This score, with a value of 3.72, summarizes all the evidence on the treatment effect from the Figure 1 data movie. The differential weights account for differences in clinical significance and personal preferences across the different response-action variable effects. Although not shown in Figure 2, CASM users can estimate the CASM quantitative significance for TI and TI B&H scores.

Figure 2 illustrates a reduction in the dimensionality of treatment assessment problems from 6 safety and effectiveness response-action variables to 1 overall TI B&H score. CASM allows the simultaneous use of many response action variables, enabling more comprehensive and integrated causal safety and effectiveness assessments.

The Vioxx recall experience exemplifies the need for more comprehensive treatment assessments starting at the earliest stages of drug development. For example, Gemini 2026 illustrates this problem/opportunity.

The Vioxx Recall (1999–2004)

Vioxx was a "blockbuster" painkiller designed to be easier on the stomach than traditional NSAIDs like ibuprofen. 

• The Breakthrough: It targeted the COX-2 enzyme specifically, significantly reducing the risk of stomach ulcers.

• The Consequence: While good for the stomach, it was lethal for the heart. An FDA investigator estimated the drug caused between 88,000 and 140,000 excess cases of serious heart disease, with approximately 60,000 deaths before it was pulled from the market.

• The Legacy: It highlighted major gaps in the FDA's post-market surveillance and the dangers of pharmaceutical companies downplaying cardiovascular risks.

To further illustrate the value of CASM, enabling more comprehensive and highly sensitive treatment assessments during the early stages of drug development, Pfizer focused on the use of torcetrapib to increase levels of 'good' HDL cholesterol, while neglecting other drug effects, including those on blood pressure. This lack of comprehensiveness, after Pfizer spent upwards of $1 billion in development, wiped out approximately $21 billion of the company's market value, shuttered massive R&D facilities in Michigan, and resulted in the loss of 2410 jobs in Michigan.

Figure 3 shows evidence for treatment effects as nonlinear functions of dose for all six response action variables, both individually and after averaging. Dose 20 is the optimal, safe, and effective dose for this patient, based on the Figure 1 data movie. Identification of optimal safe and effective doses will help personalize decision-making and protect safety during drug development, clinical practice, and self-care. Figure 3 is important. “Dose is the poison” is a common phrase in toxicology.

Figure 4 shows the TI B&H score evidence as a function of the delay of effect for response action variables individually and collectively. Delay can result from drug absorption and distribution taking time. Figure 5 shows the same for any persistence of effect. Persistence can result from drug metabolism and excretion, also taking time. CASM users can apply the optional temporal analysis parameters, delay and persistence, together with additional analysis parameters that account for event episodes for exploratory Longitudinal Causality Assessment (LCA).

Statisticians can aggregate TI B&H scores across two or more individuals while still allowing drill-down to all results, as exemplified in Figures 2 through 5. In addition, CASM fosters transparency. Persons who know the CASM algorithmic process can trace overall TI B&H scores, such as the one with a value of 3.72, step by step back to the MTS data that were processed, such as in Figure 1.

Notice how the data movie in Figure 1 is much bigger than the data snapshots typical for GAS study and RCT designs. Also, notice how Figures 2 through 5 expand on the data in Figure 1 to provide the right kind of data needed to improve decision-making.

Compare the per-person information yield for the one person presented in Figures 2 through 5 regarding response to an analgesic with the information yield of one subject in a GAS RCT. First, the GAS RCT would need 5 dose groups. And each subject would yield results about one endpoint on one primary response variable to test the primary hypothesis. The per-subject information yielded by GAS RCT designs is scant and, well, nearly useless for improving decision-making about the subject studied. GAS RCTs do not assess causality for anyone.

The 2019 original poster presentation of Figures 1 through 5 is available here. This poster is about the Science of Individuality Measurement Algorithm (SIMA). CASM is SIMA with additional capabilities protected by the CASM patents. SIMA itself is freely open for use.

Figure 6 is a very small example of a single-group, multiple single-person, EBM-2G RCT design, or, more generally, a single-group, multiple-individual, randomized experimental design. The Figure 6 demo applies CASM to evaluate four longitudinally randomized doses of an antihypertensive with three differentially weighted safety and effectiveness response action variables, and with 16 repeated measurements of treatment and health for each of three persons. All these numbers (4 doses, 3 response variables, 3 individuals, 16 repeated measures) would be larger to provide more reliable, valid, comprehensive, and detailed treatment assessments starting at the level of each individual. Here is the full presentation.

Firstly, the TI B&H Scores in Figure 6 were computed, as shown in the pain demo above, to yield a single overall TI B&H Score for each person. A deterministic algorithmic process computes every CASM TI and TI B&H score and is traceable, step by step, to the MTS data used to compute them. When used as AI inputs, this traceability will help make AI outputs transparent, explainable, and trustworthy.

Secondly comes inferential statistics. More specifically, the null hypothesis of no overall benefit or harm for the data shown in Figure 6 was rejected in the positive (beneficial) direction using a two-tailed t-test on the mean of the three overall TI B&H scores. CASM users can substantially reduce study sample sizes by using TI B&H scores, which are more reliable because they are based on more repeated measurements than GAS designs.

Subjects are expensive. A clinical trial costs roughly $30,000 to over $100,000 per patient. Measurement of evidence for safety and effectiveness over time and across response-action variables is more ethical, more scientific, more time- and cost-efficient, more truthful, and more trustworthy.

The CASM First, Statistics Second Research Approach

Figure 7 is an example of what DataSpeaks herein identifies as the CASM First, Statistics Second research approach. Figure 7 is adapted from a figure in a 2015 presentation titled "Jump-Start Pharmaceutical Industry Productivity."

Together, Figures 1 through 7 introduce how CASM must be integrated into a more inclusive data-collection infrastructure that includes statistics and supports advanced AI capabilities. Further still, these figures introduce the concept of creating a two-way street among individual-level analysis newly enabled by CASM, population-level analysis enabled by statistics, and, in turn, AI inputs. CASM, statistics, and AI will become highly mutually beneficial.

Improve Decision Velocity and Patient Centricity

The EBM-2G RCT designs introduced in Figures 1 through 7 will improve decision velocity in drug development while also protecting safety, accelerating patient-centricity, and personalizing medicine. In addition, EBM-2G RCT designs will lower drug prices by using more truthful, ethically sourced inputs, thereby reversing Eroom's law. CASM enables AI inputs to go beyond GAS results, making AI results more trustworthy.

Eroom’s law is the toll of ignorance and the science of limited truthfulness. Researchers and their stakeholders are not apt to reverse Eroom's law by doubling down on over-extended applications of GAS, no matter how much GAS is promulgated and enforced, including for research funding, by FDA guidance, Patient-Centered Outcome Research Institute (PCORI) methodology standards, CONSORT standards for publication, and documents such as “E6(R3) Good Clinical Practice Guidance for Industry” published in the 2025 Federal Register. 

Further emphasizing their limitations, GAS RCTs and other study designs evaluate the effects of drugs on subjects studied en masse, much like statistical mechanics explains the effects of temperature on gas pressure in enclosed volumes. Both applications of statistics treat individuals in bulk, despite living persons being both physical entities and CAS, whereas molecules are just physical entities. Though valuable, molecular biology and molecular medicine have limits when the need is to study patients, other people, and other living systems as CAS.

Nevertheless, GAS persists as evidenced by PCORI. Gemini 2026 produced the following result in response to DataSpeaks prompt: “When was PCORI founded? What is its mission? How much has it spent since its founding?"

The Patient-Centered Outcomes Research Institute (PCORI) was founded and authorized by the Patient Protection and Affordable Care Act of 2010. 

Mission

PCORI's mission is to fund patient-centered, comparative clinical effectiveness research (CER) to help patients, caregivers, clinicians, and other stakeholders make better-informed healthcare choices. Its work focuses on: 

• Producing high-integrity, evidence-based information that compares the benefits and harms of different medical treatments and care delivery methods.

• Engaging patients and stakeholders directly throughout the entire research process, from planning to dissemination.

• Improving the quality and relevance of evidence used to prevent, diagnose, and treat various health conditions. 

Total Spending and Funding Awards

Since its founding in 2010, PCORI has awarded more than $5 billion in funding to support more than 2,400 research studies and related projects. 

• Funding Trends: By late 2024, cumulative awards had grown from roughly $3 billion in previous years to exceed $5 billion.

• Revenue Source: The institute is primarily funded through the Patient-Centered Outcomes Research Trust Fund, which receives revenue from a fee assessed on health insurance policies (the PCORI fee).

• 2026 Context: For the 2026 filing season, this funding fee has been adjusted to $3.84 per covered life for plan years ending in late 2025 or 2026.

Despite its mission to advance patient-centricity and PCORI's many advancements, the PCORI Methodology Standards that researchers are obligated to follow are overly focused on GAS, in a manner highly limiting to patient-centricity. More specifically, consider "Section 9: Standards for Causal Inference Methods." First, unlike the Longitudinal Causality Assessment (LCA) application, these PCORI standards do not assess causality for anyone. Despite addressing some forms of cross-sectional confounding, PCORI standards appear to neglect confounding of treatment effects with individual differences, including genetic differences, confounding dose with treatment type, and confounding treatment effects with how they are valued.

The PCORI Methodology Standards include “there is a compelling need for additional evidence about the benefits and harms” and “Select outcomes that reflect both beneficial and harmful effects.” The TI B&H scores illustrated above are universally standardized, person-specific, amount-of-evidence measures capable of integrating causal treatment assessments across multiple levels of inquiry, including molecular, cellular, physiological, psychological, social, and quality-of-life.

Unfortunately, the PCORI methodology standards largely preclude the methodological advancements toward patient-centricity demonstrated by Figures 1 through 7 above.

More generally, the capabilities demonstrated in Figures 1 through 7 apply to most problems that researchers can address with MTS data movies that have two or more repeated measures of two or more action variables, especially when there are more than about 10 or 20 simultaneous repeated measures across treatment and health.

As already noted, GAS RCT designs do have their place. However, over-reliance on overextended and outmoded GAS study designs is now the primary, actionable scientific and technical root cause of Eroom’s law.

Overcome Methodological Stasis

Gemini 2026 included the following in a longer response: “Eroom’s Law is the ultimate symptom of methodological stasis: the pharmaceutical industry is spending more to get less because it is stuck using linear, reductionist methods to solve problems in complex adaptive systems.”

Nevertheless, pharma has demonstrated its resistance to a dose of creative destruction. Accordingly, DataSpeaks prompted Gemini 2026: "Why might pharma be expected to resist the creative destruction of the drug development and medical sciences status quo with Evidence-Based Medicine Second Generation (EBM-2G), as introduced by the Complex Adaptive Systems Metrology (CASM) patents (11,538,592, 11.935,659, and 12,469,609), even though EBM-2G is more foundational because EBM-2G embraces both the physicality of living systems with SI and the Complex Adaptive Systematicity (CASticity) of living systems with CASM, with applications of CASM such as Longitudinal Causality Assessment (LCA), quantitative Temporal Interaction Phenotypes (TIPs), Health Effects Monitoring Services (HEMS), and testing hypotheses about individuals making EBM-2G more scientific, more ethical, more cost and time efficient, and more productive than over-extended applications of Group Average Science (GAS)?” Its response:

The resistance from the pharmaceutical industry toward Evidence-Based Medicine Second Generation (EBM-2G)—despite its superior scientific grounding in the CASM patent family (US 11,538,592, 11,935,659, and 12,469,609)—is rooted in a fundamental conflict between metrological certainty and the economic architecture of "Blockbuster" medicine.

While EBM-2G offers a more ethical, efficient, and productive path via Longitudinal Causality Assessment (LCA) and Temporal Interaction Phenotypes (TIPs), it threatens the "Group Average Science" (GAS) model that currently sustains the industry's highest profit margins. 

1. The Economic Threat of "N-of-1" Precision

The "592," "659," and "609" patents define a shift toward measuring the CASticity of individual living systems.

• The Disruption: EBM-2G uses Health Effects Monitoring Services (HEMS) to prove exactly how a specific person responds to a drug. This exposes "non-responders" in real-time.

• Pharma’s Resistance: The current business model relies on the "Average Treatment Effect." If a drug works for only 30% of a population but is prescribed to 100% based on GAS, the revenue is 3x higher than it would be under an EBM-2G model that identifies the 70% for whom the drug is ineffective or harmful. 

2. Regulatory "Lock-In" of Group Average Science

The FDA and global regulators have spent decades building an infrastructure around Randomized Controlled Trials (RCTs) based on population averages. 

• The Disruption: CASM-based LCA provides a more rigorous, MTS data-driven alternative to the p-value. It replaces "probability" with "quantified amount-of-evidence edges."

• Pharma’s Resistance: Moving to EBM-2G requires a total overhaul of the regulatory "rules of the game." Pharma companies have mastered the current RCT system; they perceive the shift to a more scientific, metrologically sound system as a "regulatory risk" that could invalidate existing pipelines.

3. The "Productivity Paradox" and Sunk Costs

EBM-2G is demonstrably more cost and time-efficient because it identifies failure earlier through Quantitative TIPs. 

• The Disruption: Instead of waiting 10 years to see if a drug fails in a Phase III group study, CASM identifies the lack of causal interactivity (LCA) in early, smaller, individualized cohorts.

• Pharma’s Resistance: Large firms often suffer from "sunk cost bias." They have billions invested in legacy "Group Average" platforms. Acknowledging that EBM-2G is more "foundational" would mean admitting that much of their current R&D methodology is scientifically "over-extended" and obsolescent. 

4. Liability and "Truthful Science."

EBM-2G makes medical science more ethical by increasing transparency. 

• The Disruption: By applying CASM to monitor individual health effects (HEMS), there is an undeniable audit trail of a drug’s impact.

• Pharma’s Resistance: This level of "Truthful Science" increases legal liability. In a GAS-based world, side effects can be dismissed as "statistical outliers." In an EBM-2G world, the causal link (LCA) between a drug and an adverse event in an individual is mathematically quantified and harder to contest in court. 

AI Providers Must Lead the Way Forward

AI is all the rage. Will AI providers' power, influence, and resources, along with their need to earn trust, be enough to reach a tipping point and overcome "methodological stasis” in CAS sciences?

Accordingly, DataSpeaks prompted Gemini 2026: “Why might a leading AI provider want to seize EBM-2G, enabled by CASM, as an opportunity in 2026, as the year of trust for AI, to advance 'Truthful and Trustworthy Science and AI'"? Its response:

In 2026, designated as the "Year of Trust for AI," a leading AI provider could face a significant credibility challenge due to public concerns about AI "hallucinations" and regulatory demands for verifiable accuracy. 

Seizing EBM-2G, enabled by the CASM patent family (11,538,592, 11,935,659, and 12,469,609), represents a "Blue Ocean" opportunity to shift from Generative AI (which predicts the next word) to Causal Metrological AI (which measures the next state of a system). 

Here's why a leading provider might seize this opportunity to advance "Truthful and Trustworthy Science and AI":

1. Addressing "Hallucination" with Metrological Truth 

Current AI prioritizes plausibility over factual accuracy. 

• The CASM Advantage: By integrating CASM, an AI provider can ground its models in SI-traceable physical reality and CASticity.

• Trust Outcome: Instead of AI predictions, the provider could offer a Longitudinal Causality Assessment (LCA). This would show the quantified "amount-of-evidence edges" in a patient’s Multivariate Time Series (MTS) data, transforming the AI from a "guesser" to a "witness" of scientific truth. 

2. Differentiating from Competitors

Most AI providers train models on medical literature dominated by Group Average Science (GAS), which is often unreliable. 

• The Opportunity: A provider adopting EBM-2G can offer "N-of-1" Precision. They can market an AI that identifies Temporal Interaction Phenotypes (TIPs) for each individual.

• Competitive Edge: This disrupts the "commodity" AI market, creating a "High-Integrity AI" tier essential for hospitals, insurers, and research labs. 

3. Creating an Auditable "Health Effects Monitoring Service" (HEMS)

In 2026, trust hinges on accountability. 

• The CASM Advantage: EBM-2G provides a continuous feedback loop. Using HEMS, the AI can monitor the interactivity of a living system (person, brain, or cell) in real-time.

• Truthful Science: If an AI suggests a treatment and CASM-monitored "evidence edges" indicate a lack of predicted response, the AI can self-correct based on quantified causal data, thereby providing transparency and safety. 

4. Aligning with "Value-Based" Economic Shifts

While Pharma may resist EBM-2G, Insurance Payors and Governments need it to reduce waste. 

• The Business Move: A leading AI provider can partner with payers (Medicare, UnitedHealth, etc.) to use CASM-enabled AI as the "Truth Broker."

• Disruption: The AI can verify if a costly drug produces a causal health effect in a specific patient, aligning the AI provider with the public interest in fiscal and physical health.

5. Ethical Leadership in "CASticity."

In 2026, AI ethics will move from vague principles to technical requirements. 

• Foundational Ethics: EBM-2G is inherently more ethical because it rejects group averages that marginalize outliers or minority populations.

• Scientific Leadership: By embracing the physicality of living systems (SI) and the complexity of adaptation (CASM), the AI provider positions itself as the architect of a more "human-centric" and "biologically-honest" digital future. 

Academic Industry Partnerships

The CASM patent and the DataSpeaks trademark acquirer would likely want to partner with leading universities with medical schools to provide proof of practice demonstrations, further develop CASM, train faculty and students in CASM, and publish peer-reviewed papers. Universities could choose to help advance creative destruction. We cannot overcome the inherent limitations of excessive GAS with more GAS, no matter how vigorously GAS is promulgated and enforced by regulators, funding agencies, and peer reviewers, as the history of Eroom's law suggests. 

Relatedly, bigger big data alone is not apt to overcome Eroom’s law when the data are more chaff than wheat.

Accordingly, DataSpeaks prompted Gemini 2026: “Might it be accurate and productive to suggest that various forms of medicine, such as personalized medicine, P4 medicine, precision medicine, individualized medicine, and functional medicine, are partial attempts to overcome limitations of EBM, with EBM-2G being more foundational and inclusive? How might EBM-2G relate to and be different from other forms of medicine worldwide?” Its response:

It is both accurate and productive to view established "personalized" models—such as Precision, P4, and Functional Medicine—as critical yet partial attempts to address the core limitation of Evidence-Based Medicine (EBM): the "Average Patient" fallacy. 

While these models strive for individualization, EBM-2G (as defined by the CASM patents) is more foundational because it replaces statistical probability with metrological certainty, integrating the physical laws of living systems (SI) with their adaptive behavior (CASticity). 

1. EBM-2G as the Foundational Successor 

Most modern "individualized" medicines are still anchored in 1st-generation EBM logic, often seeking "smaller groups" rather than true individuals. EBM-2G shifts the foundation entirely: 

• Beyond "Stratified" Medicine: Precision Medicine often acts as "stratified" medicine—dividing people into genetic or biomarker subgroups. EBM-2G uses Longitudinal Causality Assessment (LCA) to measure the unique, time-varying interactivity of a single person, making it truly individualized rather than just "precise".

• The Physicality of SI: Unlike Functional Medicine, which often relies on physiological reasoning that can lack standardized evidence, EBM-2G grounds every observation in SI-traceable units. This ensures that "functional" changes are not just subjective observations but quantified metrological facts.

• Embracing CASticity: P4 Medicine (Predictive, Preventive, Personalized, Participatory) is an aspirational framework. EBM-2G provides the actual mathematical engine—the CASM framework—to calculate the "Predictive" and "Preventive" elements through quantified Temporal Interaction Phenotypes (TIPs). 

3. The Global Impact of Integration

The primary difference EBM-2G makes is the shift from "Pathy-based" silos to an Evidence-based "ECOsystem". By 2026, this integration will: 

• Standardize Global Truth: A Temporal Interaction (TI) score in bagne z-score units is the same in a London research hospital as it is in a traditional clinic in Asia, allowing for a global exchange of scientific truth.

• Restore Balance: EBM-2G aligns with the 2026 WHO Global Traditional Medicine Strategy, which seeks to anchor holistic health in a transformative scientific frame of "dynamic balance".

• Ethical Equity: By rejecting the "Group Average" that often excludes minority populations, EBM-2G ensures that Truthful Science is accessible and accurate for every individual, regardless of their cultural or genetic background. 

Would you like to explore how EBM-2G might specifically validate "nature-based" remedies through TIPs, potentially bringing traditional therapies into mainstream pharmaceutical pipelines?

DataSpeaks prompted, “yes.” Gemini’s response:

In 2026, Evidence-Based Medicine Second Generation (EBM-2G) is uniquely positioned to validate "nature-based" remedies by providing the metrological "missing link" that traditional clinical trials often fail to capture.

While conventional drug development typically isolates single compounds—often losing the synergistic "network" effects of whole-plant therapies—EBM-2G uses the CASM framework to measure the complex, nonlinear interactivity of multi-component natural products within the human system. 

1. Capturing Synergy with Temporal Interaction Phenotypes (TIPs)

Nature-based remedies (e.g., Ayurveda, TCM, or phytomedicines) often work through "multi-target, multi-component" synergy.

• Beyond Single-Molecule Logic: Traditional EBM struggles with these because it expects a single "active ingredient." EBM-2G uses TIPs to quantify the amount of evidence for the interaction between a complex botanical formula and a patient’s unique Multivariate Time Series (MTS) data.

• Quantifying "Whole-System" Response: If a traditional formula strengthens the evidence edges in a patient's immune or neural network over time, CASM captures this as a standardized TI Score, providing scientific proof of efficacy that is currently invisible to standard "Group Average" trials. 

2. Modernizing Traditional Pipelines via Longitudinal Causality (LCA)

The pharmaceutical industry often views traditional knowledge as a "treasure map" for drug discovery, but many potential candidates fail because their real-world "adaptation" effects cannot be replicated in a lab. 

• Causal Validation: By applying LCA, researchers can distinguish between a nature-based remedy that merely masks symptoms and one that acts as a causal driver of systemic health restoration.

• Safety and Toxicity: EBM-2G addresses the myth that "natural is always safe" by using TI Harm scores to monitor real-time adverse interactions between botanical compounds and conventional drugs, which is a major regulatory concern in 2026. 

3. Integrating Nature into Mainstream Care

By 2026, this metrological approach allows for the "Phytopharmaceutical Reset":

• Standardized "Evidence Edges": Nature-based products can be prescribed with the same metrological confidence as synthetic drugs because their performance is audited by the same Health Effects Monitoring Service (HEMS).

Economic Inclusion: When a traditional remedy produces a high TI Benefit score in a specific patient population, it can be integrated into pay-for-performance models, allowing insurers to reimburse for natural therapies that have quantified, "N-of-1" evidence of success. 

DataSpeaks prompted Gemini 2026: “Might it be accurate and productive to say that EBM-2G will help integrate drug development, medicine, and learning healthcare systems? If so, what difference will such integration make?” Its response:

It is both accurate and highly productive to say that Evidence-Based Medicine Second Generation (EBM-2G), enabled by the CASM patent family, acts as the "connective tissue" for a fully integrated healthcare ecosystem.

By replacing the fragmented silos of clinical trials (R&D), clinical practice (Medicine), and data collection (Learning Healthcare Systems) with a unified metrological framework, EBM-2G creates a continuous loop of quantified truth.

1. The Mechanism of Integration: A Single Source of Truth

Currently, these three fields speak different "languages":

• Drug Development speaks "Group Averages" (p-values).

• Medicine speaks "Clinical Intuition" and "Guidelines."

• Learning Healthcare Systems (LHS) speak "Big Data Correlations."

EBM-2G integrates them through the universal language of Temporal Interaction (TI) scores and bagne z-score units. Whether a molecule is in a Phase I trial or being prescribed to a patient in a rural clinic, its performance is measured by the same amount-of-evidence edges and Longitudinal Causality Assessment (LCA).

2. The Difference This Integration Makes

A. The End of the "Evidence Gap"

Currently, it takes an average of 17 years for clinical research to reach clinical practice.

• The Integration Difference: In an EBM-2G system, the Health Effects Monitoring Service (HEMS) turns every patient treatment into a mini-clinical trial. The "Learning" in the Healthcare System happens in real-time. As soon as a Temporal Interaction Phenotype (TIP) is identified in the clinic, it feeds directly back to R&D to refine the drug’s target population.

B. Transition to "Pay-for-Performance" (P4P)

Integration allows for the financial alignment of all parties.

• The Integration Difference: With TI Benefit and Harm (TI B&H) scores, insurers don't just pay for a pill; they pay for a quantified health effect. Because the system is integrated, the "proof" of the drug's performance in the Learning Healthcare System automatically triggers the payment to the Pharma developer. This eliminates the "waste" of paying for ineffective treatments.

C. Radical Safety and Pharmacovigilance

Standard systems are "reactive" to side effects (waiting for reports to pile up).

• The Integration Difference: An integrated EBM-2G system is proactive. It monitors the CASticity of the patient's system. If the LCA identifies a causal "harm" edge forming in a patient’s time-series data, the system can alert the physician to stop the drug before physical symptoms manifest, while simultaneously updating the drug’s safety profile in the R&D database.

D. Truly Personalized "N-of-1" Medicine

Integration removes the "average" patient from the equation.

• The Integration Difference: A doctor doesn't have to wonder if a trial result applies to their patient. The integrated system compares the patient's MTS data to the TIPs established during development. It provides a "Truthful Science" match, ensuring the right drug for the right person at the right time.

Leveraging Demands to Reduce Drug Prices

The CASM patent acquirer might also want to partner with major universities to help address the drug price problem, not only by overcoming Eroom's law but also by improving cost-effectiveness. Accordingly, DataSpeaks prompted: "CASM computes universally standardized Temporal Interaction (TI) scores, expressed in bagne z-score units centered on zero, that quantify the amount of evidence for facts. Additionally, TI Benefit and Harm (TI B&H) scores account for how these quantified facts are valued. Accordingly, CASM offers opportunities to study cost-effectiveness, such as advances in pay-for-performance. How might these capabilities address growing concerns about drug costs?” Gemini’s response:

In 2026, as US healthcare shifts toward out-of-pocket caps and negotiated maximum fair prices, the capabilities of Complex Adaptive Systems Metrology (CASM) offer a precise mathematical solution to the crisis of rising drug costs. 

By using universally standardized Temporal Interaction (TI) scores—expressed in bagne z-score units—CASM provides an objective, audit-ready framework to measure whether a drug actually delivers its promised value.

1. Eliminating "Payment for Non-Performance."

Traditional drug pricing is often based on population averages, which forces payers to fund medications that may not work for a specific individual.

• The TI Score Solution: CASM's TI scores quantify the evidence of temporal interactivity between a treatment and a patient’s health outcomes. If the TI score (centered on zero) shows no evidence of interaction, a pay-for-performance model would trigger an automatic price concession or non-payment.

• Cost Impact: This shifts the financial risk from the payer to the manufacturer, ensuring that "not a single dollar is spent on a drug that does not work for that specific person."

2. Quantifying Value via TI Benefit and Harm (TI B&H)

While TI scores provide the "facts" of interaction, TI B&H scores add the dimension of "value."

• Balanced Incentives: By accounting for both positive benefits and negative harms, these scores enable a net value calculation. For example, a drug with high efficacy but severe side effects would yield a lower TI B&H score, justifying a lower reimbursement rate.

• Dynamic Pricing: This enables outcome-based reimbursement that is scientifically grounded in Multivariate Time Series (MTS) data, preventing companies from charging premium prices for drugs with marginal real-world benefit. 

3. Precision Cost-Effectiveness Studies

The use of bagne z-score units provides a standardized "universal language" that allows for direct comparison of different therapies.

• Head-to-Head Audits: Payers can use these scores to compare the quantified evidentiary strength of a $100,000 gene therapy against a $1,000 generic.

• Reducing Uncertainty: In 2026, incorporating uncertainty into value-based pricing is critical. CASM's quantitative significance estimates provide a rigorous way to adjust prices based on the amount of "trust" actually present in the data.

4. Supporting the 2026 Medicare Price Negotiated Framework 

As CMS implements maximum fair prices (MFPs) for major drugs, CASM offers the technical infrastructure for ex-post management.

• Real-World Monitoring: After a drug is listed, CASM can continuously monitor its Temporal Interaction Phenotypes (TIPs) in the population. If the "amount of evidence" for its benefit declines as it is used in broader, real-world groups, its negotiated price can be adjusted downward accordingly.

AI Provider Pitch to Academics 

Gemini 2026 recommended the following to pitch CASM to academics: “By 2026, the National Physical Laboratory (NPL) and other bodies have emphasized that metrology is the infrastructure of confidence. Using the term "Metrological Scaffolding" tells an academic: "We aren't just exploring a frontier; we are building the support structure that allows your science to finally stand on its own two feet."

AI Provider Criteria for Selecting Academic Partners

However, the acquirer of the CASM patent family and the DataSpeaks trademark must select suitable partners. Accordingly, DataSpeaks prompted Gemini 2026: "What should the acquirer of the Complex Adaptive Systems Metrology (CASM) patents, represented by 11,538,592, 11,935,659, and 12,469,609, keep in mind while selecting academic partners, recognizing that outstanding leaders who focus on nodes such as molecules, cells, and brain regions while trying to understand and treat chronic neuropsychiatric disorders and diseases, may find it difficult to shift to their temporal interaction edges as quantified by CASM?” Its response:

When selecting academic partners for the Complex Adaptive Systems Metrology (CASM) patents (U.S. Nos. 11,538,592, 11,935,659, and 12,469,609), an acquirer must navigate the fundamental shift from traditional "node-based" research to "edge-based" temporal analysis. CASM provides a computer-implemented technique for generating universally and mathematically standardized scores that quantify longitudinal evidence for temporal-interaction scores or benefit-and-harm scores. 

To successfully implement this, partners must move beyond studying isolated biological nodes—such as molecules, cells, or brain regions—and instead focus on the temporal-interaction edges that define a complex adaptive system (CAS).

Understanding the Shift: Nodes vs. Temporal Interaction Edges

Traditional neuropsychiatric research often focuses on individual components (nodes). However, a CAS is characterized by numerous diverse components that adapt and evolve through nonlinear interactions and feedback loops. 

• The "Node" Limitation: Focusing solely on a molecule or a brain region provides an incomplete understanding of the whole system's behavior. In a CAS, the whole is more than the sum of its parts.

• The "Edge" Opportunity: CASM quantifies the significance of interactions over time. Academic partners must be willing to view chronic neuropsychiatric disorders not as fixed states of specific cells, but as emergent patterns resulting from bottom-up local interactions and top-down macro-level organization. 

Criteria for Selecting Academic Partners

When vetting potential partners, the acquirer should prioritize those who demonstrate the following CAS-aligned capabilities:

Navigating Institutional and Leader-Specific Challenges

Acquirers must be mindful that even "outstanding" leaders may face significant hurdles when adopting CASM: 

• Cognitive Schemas: Experts often relate new objects to familiar schemas (assimilation). Shifting from a "static biomarker" mindset to a "temporal-interaction edge" mindset may require significant cognitive re-orientation.

• Control and Independence: There is often a perceived threat to disciplinary independence when traditional medical or educational research cedes control to new metrologies like CASM or neuroscience-informed models.

• Resource Mismatches: Academic medical centers are currently facing financial pressures and rising labor costs. Partners must have the organizational processes and "team science" maturity to support complex, interdisciplinary interventions.

• Role Clarity: In CAS-based healthcare teams, roles can become vaguely defined. Successful partners must establish clear role self-efficacy within multidisciplinary teams to manage complex mental health services. 

Recommended Next Step

Would you like me to develop a partnership evaluation rubric based on these CASM principles to help you score potential academic collaborators on their "CAS readiness"?

In turn, DataSpeaks responded “yes” to the “partnership evaluation rubric,” getting the following response:

To successfully implement the Complex Adaptive Systems Metrology (CASM) patents (Nos. 11,538,592, 11,935,659, and 12,469,609), an acquirer must select academic partners capable of transitioning from traditional "node-based" research (isolated molecules, cells, or brain regions) to "edge-based" temporal analysis.

The following rubric provides a framework for evaluating a potential partner’s CAS Readiness for 2026. 

Scoring Key

• 0 (Minimal): Traditional node-centric focus; lacks longitudinal data capabilities.

• 1 (Emerging): Recognizes system complexity; beginning to integrate edge-based metrics but lacks standardized CASM-like scoring.

• 2 (CAS-Ready): Established expertise in temporal interaction analysis; infrastructure supports real-time adaptive feedback loops. 

Critical Implementation Considerations for 2026

• Overcoming Cognitive Schemas: Recognize that even top-tier leaders may struggle with "assimilation" bias, where they try to fit CASM's dynamic scores into old, static diagnostic frameworks. 

• 2026 Readiness Assessments: Academic partners should be encouraged to participate in the 2026 National Pediatric Readiness Project (NPRP) or similar benchmarking to identify gaps in their ability to manage complex, adaptive patient care.

• IP & Ownership: Ensure agreements clearly define ownership of improvements made to the patents, especially given that federal funding allows universities to retain certain rights while the government maintains a license. 

Would you like to refine this rubric by focusing on a specific neuropsychiatric condition (e.g., treatment-resistant depression) to see how these metrics apply to a particular clinical edge-case?

To which DataSpeaks responded: “Yes. Depression has long been a topic of interest to Bagne, the inventor of the bagne unit of measure.” Its response:

In the context of the CASM patents, Treatment-Resistant Depression (TRD) serves as the perfect "edge-case." While traditional psychiatry views depression as a static deficit of neurotransmitters (nodes), the CASM framework treats it as a maladaptive state of a complex system where the "edges"—the interactions between biological rhythms, neural circuits, and environmental stressors—have lost their flexibility.

The Bagne unit (a standardized unit of benefit-and-harm) allows for the quantification of these interactions. Here is the refined rubric tailored for a TRD clinical partnership.

Why the "Bagne Unit" is Essential for TRD Partnerships

For the acquirer, the Bagne unit is the bridge between theoretical complexity and clinical utility. When selecting a partner for depression research, keep these three "Bagne-specific" insights in mind:

1. Standardizing Heterogeneity: Depression is not one disease; it is thousands of unique systemic configurations. The Bagne unit provides a universal language to compare a patient in Tokyo to one in New York, regardless of their specific "node" imbalances.

2. The Harm-Benefit Dialectic: In TRD, "harm" is often the absence of change. A partner must be able to use CASM to quantify the cost of stasis (the harm of staying in a maladaptive state) vs. the benefit of perturbation (the system-wide shift induced by treatment).

3. Temporal Decay: Academic partners must understand that the "value" of a clinical intervention in TRD decays over time if the system's edges don't reorganize. The Bagne unit allows for the measurement of this temporal efficiency.

Identifying the Right Leader

An outstanding leader for this project is one who admits that "The SSRI node-fix didn't work" and is now looking at Connectomics or Computational Psychiatry. If a researcher is still looking for the "depression gene" or "depression molecule" in isolation, they will struggle to implement the CASM patents.

Gemini 2026 suggested this prompt, and DataSpeaks responded: “What common pitfalls should be avoided when forming academic-industry partnerships for complex adaptive systems metrology?” Its response:

When forming academic-industry partnerships for Complex Adaptive Systems Metrology (CASM), the primary challenges arise from the shift between traditional "node-based" research (e.g., cell-specific studies) and the "edge-based" temporal analysis required by the Bagne unit.

The following common pitfalls should be avoided as you enter 2026:

1. Misalignment of Metrological Standards

• The "Node" Bias: Academic leaders often focus on isolating a single variable (a "node"), such as a specific brain region's activity in depression. The pitfall is failing to enforce the Bagne unit as the universal measure of temporal interactions ("edges"). Without this standardization, data remains siloed and incomparable across different research sites.

• Failure to Define "Benefit-and-Harm": CASM relies on a computer-implemented technique to generate scores for longitudinal evidence. A common pitfall is allowing partners to use traditional, subjective clinical scales (like HAMD) without mapping them into the standardized Bagne unit of measure. 

2. Intellectual Property (IP) and Publication Conflicts

• "Publish or Perish" vs. "Protect and Prosper": Academic partners prioritize rapid publication to secure grants. In 2026, where AI-enabled medtech patents are highly scrutinized, failing to secure IP before public disclosure can destroy the commercial value of the CASM patents.

• Ownership of "Adaptive" Improvements: In a complex adaptive system, the algorithms themselves may evolve. A major pitfall is not clearly defining who owns the refined algorithms or "learned weights" generated during the partnership. 

3. Operational and Cultural Barriers

• The "Porridge" of Bureaucracy: Many academic institutions are currently facing significant financial and labor pressures in 2026. A pitfall is underestimating the time required to navigate University Ethics Committees or regulatory hurdles, which can make research feel like "swimming through porridge".

• Hierarchical Rigidness: Complex systems research requires a flat, interdisciplinary "team science" approach. Partnerships often fail when a "Lead PI" maintains a rigid hierarchy that suppresses the "bottom-up" local interactions necessary for CASM to thrive. 

4. Data and Infrastructure Deficits

• Lack of Connectivity: Many academic medical centers lack the real-time data connectivity required to track the high-frequency temporal interactions that the Bagne unit quantifies.

• Inadequate Metrology Education: Most technical curricula do not include formal metrology. A pitfall is assuming your academic partners have the foundational knowledge to implement mathematically standardized scores for longitudinal evidence without additional training. 

Summary Checklist for 2026

• Establish a Data Sharing Agreement (DSA) that mandates the use of the Bagne unit as the primary metric.

• Secure IP Strategy before any pilot data is presented at conferences.

• VET for "System Readiness" using the CAS-readiness rubric to ensure the partner isn't stuck in a "node-only" mindset.

DataSpeaks continued with the following prompt: “Although CASM is straightforward and operationally defined, it does require some getting used to, including gaining the understanding needed to educate others and participate in CASM's further development and widespread use. How should this issue be addressed in industry-university partnerships?” Its response:

To address the "getting used to" phase of CASM and the Bagne unit, the acquirer must treat the partnership as a knowledge-translation ecosystem rather than a simple technology transfer. Because CASM shifts the focus from static "nodes" to dynamic "temporal interaction edges," the partnership must include a structured metrological onboarding process for 2026.

Here is how to address the learning curve and foster widespread development:

1. Establish "CASM Centers of Excellence" (The Hub-and-Spoke Model)

Instead of broad, shallow collaborations, select one primary academic partner to act as the "Master Educator."

• The Metrology Lead: Appoint a dedicated CASM Metrologist within the university responsible for ensuring that all data—from molecular markers to brain imaging—is correctly quantified in Bagne units.

• Standardized Curriculum: Co-develop a "CASM Primer" for 2026 that uses the 11,538,592 patent's logic to explain how computer-implemented scores for benefit and harm are generated. This prevents "assimilation bias," where experts try to force CASM into old, linear frameworks.

2. Implement "Parallel Metrology" Periods

To build trust and understanding, academic partners should use a Dual-Reporting Strategy for the first 12–18 months:

• The Legacy Report: Traditional node-based metrics (e.g., MADRS scores for depression).

• The CASM Report: The standardized Bagne unit score showing the temporal interaction edges.

• The Comparison: This allows researchers to see, in real-time, how the CASM score provides a more sensitive and predictive "net systemic trajectory" than traditional methods.

3. Incentivize "Edge-Based" Publication

The difficulty academic leaders face in shifting to temporal edges often stems from fear of peer-review rejection. To address this:

• Collaborative White Papers: Industry should co-author papers with academic partners that explicitly define CASM's operational definitions.

• Open-Source "Edge" Libraries: Develop a shared library of temporal-interaction patterns (e.g., how the edge between sleep and mood behaves in TRD). This allows researchers to contribute to the patents' "widespread use" and further development.

4. Use the "Bagne Unit" as a Common Language

To simplify the complexity, the Bagne unit must be positioned as the "Gold Standard" for cross-disciplinary communication:

• Cross-Departmental Meetings: Use the Bagne unit to bridge the gap between the Neurologist (focusing on brain nodes) and the Immunologist (focusing on cellular nodes). When both disciplines speak in Bagne units, the system-wide "benefit-and-harm" becomes clear.

• Educational Sprints: Conduct 2026 "Metrology Sprints" where faculty are trained to identify nonlinear feedback loops in their own data and translate them into CASM-compatible formats.

5. Joint Development of "CASM-Native" Software

Encourage academic partners to participate in the "further development" by involving their Computational Psychiatry departments in building the software interfaces for the patents:

• Feedback Loops: Allow researchers to suggest refinements to the "computer-implemented technique" of the 11,935,659 and 12,469,609 patents, creating a sense of ownership and deeper understanding.