Patent Application
20250148478
The invention relates to the process and products as per the first portion of the independent claims.
Coaching is a form of development in which an experienced person, called a coach, supports a learner or client—hereinafter referred to as the coachee—in achieving a specific personal or professional goal by providing training and guidance. More specifically, business coaching—sometimes labelled executive coaching, corporate coaching, or leadership coaching—is a type of human resource development for executives, members of management, teams, and leadership. Here, the coach provides positive support, feedback, and advice to improve the coachee's personal effectiveness in the business setting and help him or her advance towards specific professional goals pertaining to, for instance, career transition, interpersonal and professional communication, performance management, organizational effectiveness, developing executive presence, enhancing strategic thinking, dealing effectively with conflict, and building an effective team within an organization.
By way of example, PTL1 discloses an interactive performance training and coaching system that focuses on both knowledge acquisition and behavioral embedding of skills and techniques such as through Web-based seminars.
The invention is set out in the appended set of claims.
According to NPL1, 75% of all employees consider the training offered to them as ineffective. Often this is due to the “seminar effect”—employees may leave a seminar feeling well-informed and motivated, but the positive effects decrease as employees find themselves back carrying out their day-to-day routines.
The problem is solved as per the second portion of claim 1.
Recognizing the importance of the personal relationship between coach and coachee as a critical factor for coaching effectiveness, the invention facilitates a scalable, flexible, and measurable matching logic based on expertise, immediate business needs, and the requirements of the coachee's position, department, or industry. This relationship in turn establishes a sound basis for sustainable learning and behavioral change on the part of the coachee.
As any back-end developer will appreciate, corresponding embodiments, without departing from the scope of the invention, may employ Microsoft Azure, Google Cloud, or other suitable infrastructure as a service (IaaS) that meets the requirements of the platform with respect to frequency and volume of data.
In the example at hand, the matching logic is expressed in the well-established Python scripting language. Following said logic, the coachee is prompted to select six matching preferences with respect to the coach, such as a preferred mother tongue, age range, or gender. This coachee selection, along with further quantitative (e.g., experience) and filtering (e.g., status) conditions, defines the scope of the subsequent matching.
For this purpose, the platform, for each coach from its pool, establishes the properties depicted in
As an example, the matching system will only propose a coach if mandatory requirements are fulfilled. Static requirements of this type (qualified as “Filtering” in the “Logic” column) would include the creation of the coach's profile in the platform, completion of prerequisite on-boarding courses in a learning library (for example on data protection and confidentiality aspects), or recordal of a video introduction managed in a headless content management system. Other mandatory requirements (qualified as “Matching”) are imposed by the coachee's selection at runtime, the pertinent properties of the coach being sourced from a customer relationship management (CRM) suite or plainly specified by the coach herself in a survey.
Still further properties (qualified as “Priority” or “Ranking”) are optional yet used by the system to perform a ranking in cases where more than one coach fits the coachee. For instance, the frequency with which a coach, as per internal calculations by the BI source, has been proposed to other coachees—while not blocking or forcing a coach selection—could affect his or her rank among the fitting coaches.
As will become apparent from the figure, certain properties may influence the system in several respects. By way of example, the coach's prior agreement on certain terms such as willingness to reduce greenhouse emissions, as could be obtained in a custom web form, might be considered mandatory (“Filtering”), with other factors impacting the ranking as quantitative criteria.
This system will now be elucidated in detail, referencing the flowchart of
It is noted that while in the present example of a regular coachee, matching is effected against the general pool of coaches, it may well be constrained to a predetermined sub-pool of coaches preselected in accordance with customer requirements. To improve workload balancing and mitigate any risk associated with independent contractors, the platform preferably restricts utilization of each coach to a maximum number of coachees serviced in parallel. Moreover, to avoid disguised employment and ensure regulatory compliance, assignments to any one coach may be capped depending on percentage of total income he or she receives from CoachHub. Finally, for ongoing quality assurance, individual coach performance is continuously evaluated and monitored in practice through session ratings and supervision by experienced senior coaches.
Where, according to his or her properties (cf.
In the present embodiment, the ranking is based upon a score computed from the properties specified in
Even throughout this matching process, the platform may need to process various personal and sometimes confidential data such as names, titles and positions, contact information, employers, profile pictures, Internet Protocol (IP) addresses, credentials, or usage statistics. For coachees, additional data may include selected preferences, goals, focus areas, activities, billing data, et cetera. To reflect the importance of trust as a key driver for coaching success and simplify the regulatory environment for its corporate clients, the platform needs to support compliance with applicable law and in particular maintain the strictest levels of confidentiality, data protection, and information security for example by means of anonymization.
To this end, coachees' control and rights over personal data are enhanced through extensive and effective measures that meet the principles of data protection by design and by default. For instance, any personal data is encrypted during transit, may be anonymized or pseudonymized and administrators authenticate via a two factor process and important administrators generally by means of a cryptographic hardware security key as per the FIDO Client to Authenticator Protocol 2 (CTAP2). Moreover, the platform enforces role-based access control and data deletion, anonymization, and retention policies wherein digital coaching sessions are not stored except during their transmission, data processing protocols are deleted within a set number of days following the end of the processing, for example potentially necessary evidence for legal disputes may be retained for 4 years, and contracts stored for 7 years after the end of the contractual relationship. Where required, the transfer of personal data to third countries is avoided or performed in compliance with applicable data protection and other law.
The invention is applicable, inter alia, throughout the adult education and expert matching industry.
The following documents are cited hereinbefore.
PTL1: U.S. Pat. No. 9,679,495 B (BREAKTHROUGH PERFORMANCE TECH LLC [US]) Dec. 3, 2015
NPL1: GRYGER, Liz, et al. Building Organizational Capabilities: Mckinsey Global Survey Results. Mckinsey Quarterly. 2010, no. 1, p. 288-295.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2022/053536 | 2/14/2022 | WO |
