WO2024018223A1

WO2024018223A1 - Computer implemented systems and methods

Info

Publication number: WO2024018223A1
Application number: PCT/GB2023/051922
Authority: WO
Inventors: Nicholas Brown; Jezz HAIGH
Original assignee: Subtvu
Priority date: 2022-07-20
Filing date: 2023-07-20
Publication date: 2024-01-25
Also published as: GB202210613D0

Abstract

The invention generally relates to determining, forecasting, and setting an impact level of a source of stimulant and/or resource upon a zone, e.g. the occupants of said zone. The impact can be a measurable level of exposure e.g. concentration level emitted from a source, or frequency/count of exposure to events. The source can be an environmental factor, a means of communication or a resource. The source of stimulant and/or resource in the zone can be a communication, and preferably an advertisement and/or a public health warning. The impact can relate to levels of exposure for safety monitoring, utilization or exposure to information. A computer- implemented method comprises: using a database comprising a record of zone data, said zone data including at least one of: a determined occupant count or level of a zone; a level of utilization and/or exposure to a source of stimulant and/or resource in the zone; and at least one parameter of the zone; and at least one of: determining, forecasting, and setting an impact level of the source upon the zone. The impact level can be measured based upon the occupants of the zone e.g. the impact level of the source upon the or each occupant in the zone.

Description

COMPUTER IMPLEMENTED SYSTEMS AND METHODS

FIELD

The invention relates to systems and methods for monitoring, predicting and adjusting the impact of a source upon occupants in a habitable environment. More specifically, the invention relates to the number of people in a defined space, e.g. a zone of a building or estate and the level of exposure to a source by those people e.g. the impact upon the people in the zone. In particular, the invention relates to determining and/or forecasting and/or setting an impact level of the source upon individual zones, a group of zones and an estate or portfolio. Examples of the invention reside in systems and devices enabling the methods and/or measurements to use modelled data and/or forecasts to determine a level of advertising, estimate or forecast said level, and set said level.

BACKGROUND

Monitoring systems are known, and include people-counters, such as light-beam detectors that correlate a broken beam with a person and/or group moving past. Environmental sensors, such as smoke alarms can provide a warning of smoke and/or fire. Known systems are unsophisticated and offer one-dimensional levels of information. The source people are exposed to can be a utility, service, hazardous substance or source of information e.g. entertainment. The Broadcasters' Audience Research Board (BARB) oversee monitoring, for example, of viewing figures of a particular television program that involves selecting a representative audience, asking whether they watched it and extrapolating the figures. Known systems are rudimentary, and at one extreme are limited to a specific individual location specific to an individual and at the other extreme involve approximations. Moreover, the scale and/or cost of running a monitoring system e.g. a BARB panel, relies on mass monitoring e.g. thousands of people documenting their viewing habits in their homes.

It is against this background that the present invention has been made. This invention results from efforts to overcome the problems of known systems and methods and provide an improved means for determining the impact a source has on people. Other aims of the invention will be apparent from the following description. SUMMARY

The invention generally relates to determining, forecasting, and setting an impact level of a source of stimulant and/or resource upon a zone, e.g. the occupants of said zone. The impact can be a measurable level of exposure e.g. concentration level emitted from a source, or frequency /count of exposure to events. The source can be an environmental factor, a means of communication or a resource. The impact can relate to levels of exposure for safety monitoring, utilization or exposure to information. The level or count of the impact can be measured for individuals and/or the total number of occupants measured or estimated to be in the zone that are subject to collective number of occupants. The impact level can vary according to the status of the source and/or the parameters of the zone.

Individuals and/or building supervisors, e.g. government or managers/owners, can benefit from knowing the impact of a source on occupants in a zone. This can be useful to monitor and/or manage populations of occupants in a zone and their exposure levels to environmental factors, resources or stimulants in a zone. For example, social distancing is advised in the event of a healthemergency, such as Covid- 19, wherein occupancy and/or resource levels are controlled to minimize the risk to health. Prior to accessing a zone, an occupant may want to know: Is it safe? Is it busy? It follows, therefore, that the occupancy levels and/or utilisation of resources in a library, bar, canteen, study-area, business venue, office-space can provide determined data and indicate accessibility and/or conditions.

The determined data and/or impact levels obtained from the records and/or actual measurements can enable safe level of access and optimum utilisation of resources. The volume of people in a room can be assessed against date/time/capacity/safe levels. Impact levels can be monitored against pre-set safety values in real-time, as determined by the zone and/or its parameters. Threshold levels of impact levels can be used to implement a ‘traffic light’ methodology of assessment i.e. OK, Caution or No-Good.

In a first aspect, the invention resides in a computer-implemented method comprising: using a database comprising a record of zone data, said zone data including at least one of: a determined occupant count or level of a zone; a level of utilization and/or exposure to a source of stimulant and/or resource in the zone; and at least one parameter of the zone; and at least one of: determining, forecasting, and setting an impact level of the source upon the zone. The impact level can be measured based upon the occupants of the zone e.g. the impact level of the source upon the or each occupant in the zone. The impact can be determined, based on measured and/or modelled data. An impact can be a level of exposure e.g. over a period of time, such as a 15 -minute interval, or cumulatively. The impact can be a frequency of exposure, such as a count of the number of exposures over a period of time, such as a 15 -minute interval, or cumulatively.

The zone can be a space having an entry and/or exit point via which occupancy levels can be measured. The space can be a room, barn, fenced area or otherwise demarcated area.

Utilization of a resource can comprise measurable consumption of a commodity e.g. data and/or power. Exposure to a resource or source of stimulant can comprise measurable levels of an environmental factor e.g. UV-levels, temperature, gas concentration or radiation levels. A source of stimulant and/or resource can be both a source of exposure and utilization i.e. a commodity that has a measurable level of output e.g. a media system have a display and/or audio from which play out or playback can be determined and/or levels of playout can be measured e.g. sound pressure levels.

Using the database can comprise generating, maintaining, providing, updating, storing, accessing or processing: the database; and/or the record that is stored within, or in association with, the database. The database and the records therein can be used to determine models representative of at least one of occupancy levels, parameters of the zones and measured data. Measured data can be used to determine occupancy levels for determining impact levels for those locations with equipment installed e.g. counters, and where no equipment is available then modelled data e.g. data from records can be used to determine an estimated impact level.

The record can include at least one of: the status of the source; a level of the source; the format of the source; a count of the sources; and the connectivity to the sources of the stimulant and/or resource. Sources can provide, for example, a WI-FI (RTM) connection, a retail pay point, ethernet connection, audio and/or visual playout equipment. Sources can also be environmental sources. Environmental sources can be measured e.g. noise levels, air quality, light levels. Sources can be intermittent, and the level of exposure can be determined over a period of time e.g. every 15 mins, per hour or per 24-hour period.

The record can include records of occupancy levels of each zone, estate or portfolio of zones/estates. Occupancy levels can be averages over set intervals or time periods e.g. 15-minute intervals. Records can include current and/or historical parameter data for each zone, estate or portfolio of zones/estates. At least one parameter can influence at least one of: the determined occupant count; and utilization and/or exposure to the source.

The determined occupant count can be determined from at least one of: an occupant counter located at the or each access point to the zone; a location sensor configured to determine at least one of the presence and position of an occupant in the zone; an estimate based on records of the occupant count of the zone and/or another zone. The records can be used to determine a model. The model can be used to determine modelled data e.g. modelled occupancy count/level. The modelled data can be used to determine a representative count of the occupants in a zone. For example, the determination can include calculating an average occupancy level which can be an average or modelled level based on one or more other zones e.g. the average of all zones for a given time and/or day. Determination of the impact can be based on regional statistical data and/or national statistical data obtained from measurement data in the zones and/or estates. Determining the presence and/or location of an occupant in a zone can include determining the position of a phone, WI-FI (RTM) connectivity, or an RFID on a wristband or lanyard of occupant. Presence and/or position can be determined through triangulation/trilateration.

At least one parameter can comprise: the nominal capacity of the zone; the accessibility to the zone e.g. based on its opening hours; the environmental conditions of the zone; and the environmental conditions external to the zone. Parameters recorded can indicate whether at a given time and/or day the zone was cool inside a building when it was hot outside or warm inside when it was raining outside. Not only does the weather influence the occupancy rate, but the occurrence of an event can influence the occupancy levels and, therefore, can be used to determine a modelled occupancy level.

The record can amortize at least one of: the determined occupant count; and a level of utilization and/or exposure to the source level of utilization, over a period of time e.g. over a 15- minute period. The database can include a plurality of records for a plurality of zones and determine and/or forecast and/or set the impact level for a zone using at least one record and/or zone data of another zone. A record can include historical and/or live data.

At least one record of another zone can be selected based on a level of similarity with the zone. The level of similarity can be determined by comparing at least one of: at least one parameter, wherein said parameter has an effect on at least one of the occupant count, utilization and exposure; historical data of the database and/or the at least one record; the period of time in which the forecast is determined; weightings of the at least one parameter; real-time information of at least one of the occupant count and the level of utilization and/or exposure to the stimulant and/or a resource, in the zone or in the another zone.

The methods herein can include determining an occupant count of a zone, the level of utilization and/or exposure to the source and applying a weighting factor derived from the at least one parameter of the zone. The weighting applied to a parameter can be dynamically adjusted e.g. if the impact of a parameter changes over time, the determination of the impact from the database can be adjusted by modifying the weight of a parameter in said determination. By way of example, the impact of the size and/or number of digital displays in a zone can be evaluated over time and modified if it is determined, post-evaluation, that the number of number and format of the screen has an increasingly positive effect upon the impact level of a source based on the zone data.

The setting of an impact can include an adjustment to an output of the source of stimulant and/or resource. The setting of the impact can further include an initial determination of an impact, a determination that the initially determined impact is different from a necessary or desired impact (which may be referred to hereon as a “predetermined” impact), and a subsequent such adjustment. For instance, where the source of stimulant is a television displaying information, e.g. the location of fire exits in a building, having determined that an initial impact (i.e., a number of viewers of the locations of fire exits) is lower than desired, the frequency of output of the fire exit locations on the television is increased to set the impact to a higher value. Alternatively, or additionally, at least one of the size or number of units of a source of stimulant and/or resource e.g. the map that illustrates the locations of fire exits can be increased to set the impact to a higher value and/or the number of televisions displaying the map can be increased. In another example, the source can be a security announcement e.g. audio and/or visual presentation on a television in a transportation hub, such as a bus station or an airport. Other properties of adjustments to the source may be changed, such as intensity or brightness of the visual stimulus, a volume of an audio announcement, an opacity of an overlay image, a colour saturation of an image, and a duration of an image display and/or audio message, among other properties of what is being output, emitted, or broadcast.

Changes to these properties of, in these examples, visual and audio outputs, may be done dynamically as follows: having determined the initial impact and determined that the initial impact is lower than a desired impact, a control signal being sent to a televisual/audio system to cause, for example, a screen to display the map over a larger proportion of the screen and/or to display the map more frequently, and/or to cause a speaker to announcement a safety message at greater volume. In other words, the impact of the source can be adjusted based on the audio and/or visual levels, such as the size.

Where the stimulus provided by the source is an environmental factor, especially one which is important to control for the purpose of health of occupants of a zone, for example ultraviolet light, temperature, gas concentration or radiation levels, a control signal may be sent much as described above to the source having determined that an initial impact of that source is undesirably high or low. For instance, where it is determined that an initial impact of a gas concentration in a hermetically controlled laboratory approaches dangerous levels (perhaps due to an over- or under-population of the laboratory, or some other measured or predicted factor as described herein), i.e., that the initial impact is greater than a pre-determined impact, then a control signal may be automatically sent to the source of the gas - such as a circulator, recycler, or diffuser - to set an impact to be lower than the initial impact, that is, to decrease the output of a particular gas from the source, perhaps indirectly, such as by reducing the power of the recycler.

The methods herein can therefore include controlling the source in a zone for setting an impact level of the source upon the zone. Using the records alone or in combination with real-time measured data, the sources and/or occupancy levels can be managed to control the determined impact level. By way of example, occupancy can be controlled if an environmental factor is detrimental to occupants, or the activation levels of a source can be increased to have a greater impact level upon the occupants.

Aspects and/or examples of the invention support the determination, forecasting, and setting of an impact level of the source upon the zone and/or the occupants therein. This can improve the management of a resource or provide an indication of the actual or anticipated level of occupancy and/or the level of utilization of resources and/or the level of exposure of occupant to environmental factors, resources or stimulants in a zone. While examples herein refer to examples of exposure to broadcasts within student bars, the invention is not limited thereto.

In another aspect, the invention resides in computer equipment comprising: memory comprising one or more memory units; and processing apparatus comprising one or more processing units, wherein the memory stores code arranged to run on the processing apparatus, the code being configured so as when on the processing apparatus to perform the method of any claim herein.

In another aspect, the invention resides in a computer program embodied on computer- readable storage and configured so as, when run on one or more processors, to perform the method of any claim herein.

In light of the teaching of the present invention, the skilled person would appreciate that aspects of the invention were interchangeable and transferrable between the aspects described herein, and can be combined to provide improved aspects of the invention. Further aspects of the invention will be appreciated from the following description.

DESCRIPTION OF THE FIGURES

In order that the invention can be more readily understood reference is made, by way of example, to the remaining drawings, in which: Figure 1 is a schematic layout of a zone having two doorways allowing entry and exit of occupants, means for detecting the occupants, a variety of sources of stimulant and/or resource that the occupants can use or otherwise be exposed;

Figure 2 is a schematic layout of the zone of Figure 1 adjacent three other zones, which form part of an estate or building;

Figure 3 is a schematic layout of the five zones, each having a means for detecting the occupants and a variety of sources;

Figure 4 is a diagram illustrating a plurality of zones or estates connected to a hub that can exchange data with the zones;

Figure 5 is a diagram of the inputs and outputs from the hub configured to determine an impact;

Figure 6 is a diagram of data being used to adjust an impact, or set a target impact; and

Figure 7 is a table indicating the status and measurements from sources/sensors;

Figure 8 is a table indicating the effect of parameters on forecast impact;

Figure 9a is a graphical representation of forecast occupancy and impact.

Figures 9b to 9f are graphical representations of average occupancy levels for set of 6 zones and/or estates for different parameters.

Figure 10 is a flowchart representing an overview of the stages of modelling and determining occupancy levels.

Figure 11 is series of formulae used in the determination of the impact of a source upon a zone.

Figure 12 is a detailed flowchart representing an overview of the stages of determining an impact.

Figure 13 is a detailed flowchart representing an overview of the stages of determining an impact, wherein determination is complemented with external data.

Figure 14 is a schematic of a system of the zone and/or hub.

Like reference numerals refer to like features.

DETAILED DESCRIPTION

Zones

Figures 1 to 4 include a zone 10 for accommodating occupants, who can enter and exit the zone via a doorway 12. Figures 2 and 3 show a plurality of zones 10 which are adjacent and connected. Each doorway has a detector 14 for counting how many people pass through the doorway 12 e.g. the footfall of occupants. The detector can differentiate between occupants entering or exiting through a doorway. The detector can count the average number of occupants entering and/or or exiting a doorway over a period of time, which can be variable and configured remotely. For example, the detector can determine and record the average number of occupants entering and/or or exiting a doorway in intervals e.g. 15-minute intervals 24-hours a day, 7 days a week. The detector can operate upon visual and/or thermal detection. The detector can count individuals on their own or differentiate individuals as part of a crowd. The detector can count individuals anonymously and/or using facial recognition. Examples of counters are known from providers such as Irisys (RTM)

;:ct jechnok>;: ?cmr-id-pcopje--CG-;-;;o:-. Data captured from detectors 14 can at least one of: accommodate errors associated with faulty detection, wherein negative readings and/or over-counting (wherein the people count is deemed greater than the occupancy limit of a zone) are factored into the determination of the impact; and data is stored for transmission when the detector looses a data connection i.e. if the detector goes off-line, the data is stored and uploaded to the hub 32 at a later date, and retrospectively taken into account in the determination of the impact of a source. Error detection can be implemented to account for, by way of example, negative occupancy adjustment, backdated readings on individual counter feeds, individual locations and at player, such that a granular level of error correction can be implemented.

A zone 10 can be a workspace, shop, bar or a cafe, or part thereof, including at least one of: an audio-visual system 16, represented by a display screen 16, e.g. a television or image projector; a wireless transponder 18, hereinafter referred to as a ‘transponder’ or ‘WI-FI (RTM) unit’, which can communicate and/or determine the presence and/or location of a digital device e.g. mobile phones and/or RFID tags worn by occupants within the zone 10 e.g. using a plurality of transponders and triangulation and/or trilateration; and a service counter 20 e.g. a bar 20 and cashier- terminal 22 or paypoint e.g. a till 22; a desk 24 or table having a connection point 26 e.g. an ethernet terminal 26.

An occupant count determination can comprise at least one of: (i) data from an occupant counter located at, near or adjacent the or each access point to the zone e.g. at or near each entry/exit point to the zone; (ii) a location sensor configured to determine at least one of the presence and position of an occupant in the zone; and (iii) an estimate based on records of an occupant count in the zone and/or another zone. Estimations can be based on records obtained from the same and/or different zones can be weighted based on the level of matching parameters. Estimations can be based on modelled data e.g. audited and validated, and/or forecasts. Measured data can be used to determine occupancy levels for determining impact levels for those locations with equipment installed e.g. counters, and where no equipment is available then modelled data e.g. data from records can be used to determine an estimated impact level. A zone can include at least one monitor 28. The monitor can include at least one environmental sensor that monitors and records parameters, for example, lighting conditions, sounds levels, temperature, humidity, air-quality or other stimulant and/or resource. The or each monitor 28 can be a fixed asset or device configured to record parameters.

In addition to a detector 14 and/or monitor 28, the number of occupants or the level of utilization and/or exposure to a source of stimulant and/or resource in the zone can be obtained from a mobile device e.g. a phone or a tablet, which can be running an ‘app’ and application program interface that enables supplementary data to be measured via the mobile device or entered manually. The mobile device and data entered therefrom can compliment the data captured by fixed sensors. By way of example, a visual sight-check can be performed to confirm the number of occupants in a zone. Sound levels or similar sensor recordings can be performed by the mobile device. Further, by way of example, interaction between a mobile device held by occupants can provide an indication of occupancy levels.

Parameters can be controllable, e.g. the audio-visual system 16, or be subject to the environment of the zone, which can be influenced by the occupants. The monitor, therefore, can at least one of manage, monitor and record the status of one or more sources of stimulation and/or resource in the zone e.g. pay-points 22, screens 16, WI-FI (RTM) 18, audio 16.

Figure 4 includes a plurality of zones 10 and a collection of zones that is hereinafter referred to as an estate 30. The or each of the zones and/or estates are in communication with a hub 32. Using the examples of Figures 1 to 3, a monitor 28 can be provided in each zone and/or estate to at least one of manage, monitor and record the status of the sources and parameters, and communicate with the hub 32. The hub can host a database and/or processing system for at least one of a zone 10, estate 30 and a portfolio 34 of zones 10 and estates 30.

By measuring the number of occupants entering and exiting a zone 10, whether that be to move to another zone, or leave the estate 30, the number of occupants in a zone can be determined at any one time. The occupant measurements can be amortised over a period of time e.g. average number of occupants in a zone over a 15 minute period. Variation in occupancy can be monitored continuously and grouped into time-windows to support a granular analysis, determination and estimation of occupancy. A 15 -minute window is suggested by way of example, and the time window can be 5 minutes, 10, minutes, 20 minutes, 30 minutes, 1 hour etc.

For a given period of time e.g. a 15-minute window, at least: the average number of occupants can be measured and/or total number of occupants can be commutatively measured e.g. using people counters; the status and/or activity level of the sources measured and/or determined e.g. by checking whether they are switched on, operating and playing content; and parameters recorded, which provides a record of data for the zone that indicated an impact level of the source upon the zone.

The zones 10 of Figures 1 to 4 operate to record data associated with the zone. The zone data can include at least one of: parameter data; source data; and occupancy data. A record of zone data can be used to determine, forecast, and set an impact level of a source upon the zone.

Parameter data can be fixed. Fixed parameter data can include the nominal capacity of the zone and/or the estate e.g. what is the maximum number of people it can hold. Other fixed data can include the accessibility to the zone e.g. if it was a bar or cafe, how many days per week it is open, and what are its opening hours during the day. Knowing the opening hours supports the determination of the impact a source of stimulant upon occupants, or what resources are available for an occupant to use. Zone-specific parameter data can include information associated with at least one of: the ranking of the zone, which indicates its level of popularity; the location; weather conditions; and events occurring at local, national or international levels - all of said information indicating and/or influencing the probability of the zone being populated.

Overall, at least one parameter has a weighting factor of at least one of: the determined occupant count; and utilization and/or exposure to the source. At least one parameter comprises: the nominal capacity of the zone; the accessibility to the zone; the environmental conditions of the zone; and the environmental conditions external to the zone.

Parameter data can also be variable. External conditions to the zone can influence the level of occupancy and the utilisation and/or exposure to a source 16. Variable parameters can include, for example, weather conditions or events, such as sporting events, national events e.g. elections, international events e.g. Olympics or a Mars-landing.

Zone data includes source data providing an indication of the status of the or each source within a zone. While a payment terminal 22 or a screen 16 can be present, knowing that it is switched on and/or operational required to determine its level of utilization and/or exposure. In other words, are the occupants able to make use of the resource and/or be stimulated by the resource. Using the bar/cafe example, a zone can have three payment terminals 22, with one broken, one switched on and only one is operational, then the ability of occupants to use such a resource are limited. The ability to use a resource similarly applies to ethernet connection point operation and WI-FI (RTM) strength and/or bandwidth. The level of activity of the source also determines the level of exposure. Once again, using the bar/cafe example, a bar can be provided with a multi-media system including screens and/or loud speakers. The status of a screen and its operation must be known if it is to be determined that occupants in the zone were able to listen to or watch the screens i.e. the impact upon the occupants by the source. Similarly, the status of heating, ventilation and air-conditioning (HVAC) systems must be known because this source determines the comfort level of the occupants i.e. the impact on the occupant by the source. Sources can provide information, determine a sensory experience e.g. too hot or too cold and/or can be hazardous e.g. poor air quality. A source can be an air filter, which measures the particle count in the air or the zone and can operate at a range of levels.

Overall, a source can be: controllable e.g. a media system; uncontrollable e.g. a level of gas or air quality in a zone; or a combination of both, e.g. a temperature setting managed by an air- conditioning unit. In each case, exposure level can be determined, forecast and ultimately controlled by minimizing exposure by controlling access to the zone and/or controlling the source. The source may be controllable by a signal sent in response to a determination that an initial impact by the source upon the occupants is undesirably low or high, the signal being a control signal which causes the source to increase or reduce its impact as discussed earlier.

An example of a table recording source data is shown, by way of non-limiting example, in Figure 7, which indicates the status, activity/level and average measurements of example sources. Source data indications/records can include: whether specific content was transmitted over the media system 16 e.g. coded data or an advertisement for a specific product, or a public health warning; measured sound pressure or volume; the frequency of flashing lights; oxygen levels; pollen levels; noxious gas levels; radiation levels. By recording the data associated with the source, its status, level or operation, format and connectivity, the impact upon the occupants can be determined.

The influence of the parameter data of the records determines the probability of the zone being populated, and to what extent. The status of the sources derived from source data from the records enables the impact upon occupants to be determined or forecast, or the level of impact to be set. To determine the impact of source e.g. a level of utilization and/or exposure to a source of stimulant and/or resource, the occupant level is determined and/or estimated.

Occupancy levels either measured and/or forecast, provide a basis for determining, forecasting, and setting an impact level of the source upon the zone. Starting from the determined occupancy level adjustments can be made to the determination of the impact using parameter weightings. An impact level can be determined from a level of exposure and/or use from an active source over a given period of time e.g. 5, 10 or 15 minutes. A general exposure can be useful when the source is harmful e.g. air quality that impacts the zone irrespective of the number of occupants. An impact level can additionally or alternatively be determined from a level of exposure and/or use from an active source that a number of subjects (people or animals) have been exposed to over a given period of time e.g. 5, 10 or 15 minutes.

The parameters and sources described herein are provided by way of non-limiting examples in order that the use of recorded data for determining, forecasting, and/or setting an impact level of a source for a zone can be demonstrated. Bars and cafes are just one example, while other examples can include libraries, workspaces, manufacturing facilities, supermarkets i.e. zones or areas of estates that can be populated, wherein in the zone can be measured, forecast or otherwise controlled.

While the examples herein describe the occupants as people, the occupants can be animals e.g. livestock on a farm, said farm having, for example, fields, barns and milking sheds. By way of a nonlimiting example, the zones can include a field and an adjacent barn occupied by free-range chickens. The impact upon chickens depends on their zone location, parameters of the zones and measurements of the source. Therefore, the impact upon the chickens can be determined, forecast or set e.g. to maintain a threshold level of health and wellbeing.

The values and status of the sources and/or the weightings of the parameters can be determined from at least one of model data, zone analysis and monitoring and statistical analysis. The records from zone analysis, e.g. real-time monitoring and validation, therefore, can be used to update model data. In this way two zones can be matched or otherwise compared with the level of similarity determining the extent to which the record data is used to determine the impact on the occupants.

Function

Zone data can be recorded for a plurality of zones 10 and/or estates 30 and/or portfolios 34 for a plurality of zone configurations e.g. various sources and parameters, which enables substantial data records to be captured and retained in a database and/or data models to be established. Parameters and/or data from a zone can be captured by a monitor 28 and relayed to a hub 32, which can hold a database that can be used for modelling, analytics and forecasting.

Figure 5 shows that a hub 32 is supplied with information from at least one of: (i) sources providing source data e.g. external databases and/or sensors that provide measurement data, or samples thereof, which includes measurements, observations and adjustments originating from controllable devices e.g. media systems, sensors e.g. air quality measurement and adjustable sources e.g. air-conditioning units; (ii) the zone, estate or portfolio, which provides parameter data and/or settings associated with a zone that influences the impact of the source upon the occupants and/or the number of occupants subjected to impacts; and (iii) occupancy data derived from counters that determine how many occupants reside in a zone at any given time, or over a period of time e.g. 15 minute intervals.

The hub 32, therefore, can build a database of records for the zone data, including: parameter data; source data; and occupancy data. The zone data can be received directly from the or each different data sources and/or via a monitor 28 located in each zone 10 and/or estate 30.

The impact can be determined on a zone-by-zone basis. Additionally or alternatively the impact can be determined based on the number of sources in an estate, wherein a single source can impact upon two or more zones. By way of example, an impact can be determined based on: one zone having a plurality of digital display screens, wherein a single playback device is connected to the screens in the zones; a single playback device that is connected to a plurality of display screens that are located in a respective plurality of zones.

The data gathered can be audited or otherwise verified or correlated to provide a model representative of a plurality of configurations or zones, sources, parameters and occupancy. Additionally or alternatively, the hub 32 records and/or the model can be used to forecast an impact level directly, or by taking into account an actual count of occupants in a zone. The impact can, therefore, be derived from at least one of the records that provides modelled data or forecast conditions, with the option of taking into account the actual count.

The examples herein are illustrated using the hub 32 and its associated databases. Additionally or alternatively, third-party databases comprising data associated with an individual’s locations and/or behavior can be used to adjust the model and/or forecast that is used to determine the impact or adjust parameters or settings to achieve a set impact level. Third-party databases can include, by way of example, location data extracted from applications operating on occupants’ mobile devices e.g. Goggle (RTM) location data.

Function - Forecasting/Estimating

Operating the systems herein and implementing the methods can include using the hub 32 to at least one of generate, maintain, provide, update, store, access and/or process the database and/or a record that is stored within, or in association with, the database.

By way of example, a hub 32 can store data records of the source data, parameter data and occupancy data for 50 zones within a portfolio 34. The database holds records that have been acquired, recorded and amortised for 15 minute intervals for a period of 2 years. In the event a new zone e.g. a 51^st zone is added to the portfolio, the impact upon occupants in that zone for a given time period of 18.00 to 19.00 on a Saturday in the month of June can be determined. The impact can be determined using at least one of a plurality of calculation methods. Impact can be determined using the data records if the 51^st zone does not have an occupancy counter installed for real-time attribution e.g. by using modelled count data, which can be based on historical counts, averaged counts in corresponding zones, estates or across the portfolio.

The new zone, the 51^st zone, by way of a non-limiting example, has a known set of sources and/or parameters. The impact to be estimated or forecast is the number or people that will be exposed e.g. subject to a government campaign that discourages driving a vehicle while under the influence of alcohol or drugs, which is transmitted via a multi-media system in the zone.

The calculation methods can include:

Retrieving from the database the average occupancy rate for all zones, and the associated level of impact and scaling the impact to that of a new zone e.g. the 51^st zone based on the maximum occupancy of the zone.

Retrieving from the database the average occupancy rate (%) for all zones with counters for a given 15 minute interval, and scaling the impact to that of a new zone e.g. the 51 ^st zone by applying the average occupancy rate to the 51^st zone’s maximum occupancy.

Retrieving from the database an occupancy rate and the associated level of impact for a zone that has the same number of sources and/or comparable parameters to the 51^st zone, and scaling the impact to that of the 51^st zone based on the maximum occupancy of the zone.

Using: a modelled occupancy rate for zones, which can be determined with or without counters; the average occupancy rate; and the associated level of impact, and then obtaining the actual occupancy level of the 51^st zone between 18.00 and 19.00, which is then applied using the modelled data to estimate a scaled modelled impact for the 51^st zone based on the maximum occupancy of the zone.

Determining an estimated occupancy level for another zone that is at least one of the closest match to the zone to be estimated e.g. the 51^st zone and the closest geographically. The data for the closest zone can be measured data and/or historical data obtained from the hub 32. Multiplying said occupancy level and/or an average occupancy rate by the maximum capacity of the 51^st zone to determine an estimated occupancy level e.g. the applicable zone uses an 80% occupancy level retrieved from the database and multiplies this by the maximum capacity of 200 people, therefore the estimated occupancy level is 160 people. Thereafter, the status of the sources is determined, which can be recorded or estimated and in the present example calculations are based on a successful transmission within each 15 minute interval. It is estimated, therefore, that in the 1 hour period a transmission has been experienced i.e. impacted 960 times (6 x per hour, for 160 people = 960 impacts). However, from modelled data and/or statistical analysis of the records of zone data that incorporate the parameters into the estimated impact level, it is known that the parameters influence the number of impacts either individually and/or collectively. The estimated impact value of 960, thereafter is factored using a parameter weighting. In this example the mean parameter weight is -110% (1.1031), thus the impact level is determined to be 1056 (960 people x 110% = 1056 impacts.

Function - Setting

Figure 6 shows that the parameters and/or settings can be configured using at least one of the modelled data, forecast data and actual count of occupants. Using the example above, in which 1056 impacts were determined, the target impact can be set for the 51^st zone to be between 500 and 600 impacts per hour. Using the records it can be determined, for example, that to achieve this level of impact successful transmissions are only required once every 20 minutes, rather than once every 10 minutes. This is an example of feeding back a control signal to set an impact to be lower (in this case, by sending a control signal to reduce a frequency of transmissions), based on an initial determination that an impact is higher than required.

It follows that sources in zones can be dynamically adjusted to achieve a target level of impacts. If, for example, the actual occupant count falls, which lowers the average occupancy rating over time, the number of transmissions can be increased. Or should a transmission fail to play in the zone and/or estate, the system managing the multimedia source can increase the number of transmissions.

Overall, it can be appreciated that the function provided by the examples herein can utilise a record of zone data for said zone, or a plurality of zones to estimate, forecast or set an impact level. Average values can be used, although weightings, e.g. weighting multipliers, can be used to adjust estimated values according to zone-specific configurations.

Weightings and/or zone matching can accommodate a level of similarity between a zone and the data obtained from one or more other zones. Weighting and/or matching can include comparing at least one of: (i) at least one parameter, wherein said parameter has an effect on at least one of: realtime occupant count, utilization and/or exposure; (ii) historical data of the database and/or the at least one record; (iii) the period of time in which the forecast is determined; (iv) weightings of the at least one parameter; real-time information of at least one of the modelled occupant count; and (v) the level of utilization and/or exposure to the stimulant and/or a resource, in the zone or in another zone.

As described above, occupancy levels are a factor in determining the impact level. Occupancy levels can be determined from (i) real-time / actual counts e.g. from zones with counters, and (ii) modelled counts e.g. where a counter is not present or operational, and the data 32 is used to derive an estimated occupancy level.

Examples

The teaching herein can be applied to any zone in which an impact is be determined. As described above, an impact is a measurable level of exposure to a source e.g. the length of time someone has remained in a room with a particular environment e.g. temperature or gas level, or the frequency to which a person has been exposed to a noise-level, image or information, such as an advert. Overall, it can be appreciated that the function provided by the examples herein can utilise a record of zone data, determine an occupant count of a zone, determine a level of utilization and/or exposure to a source of stimulant and/or resource in the zone, take into account at least one parameter of the zone and determine, forecast, and/or set an impact level of the source upon the zone and/or occupants in that zone.

Figure 7 is an example of a table for recording source data for a zone, and, by way of nonlimiting example, indicates the status, activity/level and average measurements of example sources - said sources including: a payment terminal, e.g. till having a certain number of transactions per hour; media player, e.g. screen 16; audio playback, measurable in dB; WIFI (RTM) status, which can be measurable in the number of connections, bandwidth and/or download speed; air quality, measurable in parts-per-million (ppm); air-conditioning, temperature and humidity levels.

Figure 8 is an example of a table of parameters for a bar or cafe. Different levels of the parameter values have corresponding multipliers, which indicate that affect upon the impacts upon the occupants. The parameter levels are implemented because of modelling and/or statistical analysis that adjusts the calculations to improve the determination of the impacts for zones that do not have people counter technology installed. By way of example, the number and/or type of display screens 16 will influence the impacts audio-visual content or data has on the occupants in a zone. Similarly, the total power output of the audio-visual system e.g. in kW has an impact on the audible impact upon occupants within a zone. If a screen or interface for displaying sports is provided in the zone, then its presence and/or type influences whether occupants are present to primarily socialize or watch sporting activity on a screen and maintain a focus on the source of the impact because they are predominately looking at a screen.

Other parameters include, by way of non-limiting examples: the maximum capacity of the zone, which has a greater tolerance range the larger it becomes; the average number of days a zone is open; the average number of hours a zone is open; ranking of the zone, which indicates its level of popularity e.g. a TripAdvisor (RTM) rating; the location; weather conditions; and events occurring at local, national or international levels - all of said information indicating and/or influencing the probability of the zone being populated.

Using again the example of determining the impact of a government campaign video that discourages driving a vehicle while under the influence of alcohol or drugs, which is transmitted via a multi-media system in the zone: An impact in this example is 1 person seeing the video once, thus a zone having 10 people and 10 successful play outs of the video would have 100 impacts. It is assumed that play outs of the government video occurs once every 15 minutes. Playouts can be monitored, and their status or success recorded. The methods herein determine that each occupant present in a zone when a source operates or ‘emits’ is exposed to that source. For example, if there were 20 people in a zone over a 15-minute period, and an audio- visual playout successfully occurs in that 15-minute period in that zone then the impact is ‘20’, because all of those 20 occupants will have been exposed and seen/heard the playback. And if the playout occurred twice in that 15-minute period then the impact would be ‘40’ exposures.

In practice, the determination can begin by testing occupancy detection in a plurality of zones 10, estates 30 and property portfolios 34. Different detection techniques can be deployed to determine the level of accuracy required for representative counting. Different zones can have different entry and exit requirements - for example, a cafe can have a relative wide doorway through which a crowd can wander, a sports stadium has controlled barriers, each permitting access for one person at a time. The assessment of the technology is audited and/or verified with footfall visualisations. When an accurate occupant counting method has been identified for a zone then an appropriate system can be deployed and further auditing and/or verification is optional.

Further, determination requires accurate monitoring of the sources. If a screen displaying a video is not working, the video does not upload or otherwise freezes, then the impact upon occupants in the zone must be discounted. The records can include reports on the performance of the source.

Zone data includes records, over time, of the status of the, or each source, in a zone, and the footfall i.e. occupancy levels of a zone. Zone data can be accumulated for a single zone, and used to forecast and/or set impact data for said single zone. Records can be improved by including zone data for a plurality of zones, and with modelling and/or statistical analysis the influence of parameters can be consolidated into weightings. An impact report and/or forecast can log the weightings applied to the determined impact and, therefore, can be verified, audited or otherwise improved upon. This can be applied in the determination of the impacts upon occupants.

The parameters of the example of Figure 8 can, for example, be applied to a student bar located inside a college or university. The student bar can have one or more zones 10, as shown in Figures 1 to 3, sources as shown in Figure 7 and a set of parameters as shown in Figure 8. A selection of 12 student bars, which can be treated as 12 estates 30, are used to evaluate and select accurate means for determining footfall, while the means for monitoring the sources in the zones are established. Using the differences between the student bars, the influence of the different parameters on the impacts upon the occupants can be determined. Modelling of the parameters and/or statistical analysis can be audited with observations. Data records are collated in a database within the hub 32.

The student bars in each of the 12 estates are part of a property portfolio 34 of 85 student bars, each with differing parameters. The records in the hub can be used to at least one of: determine an actual impact on occupancy that has occurred; estimate an impact on occupants that has been modelled; forecast a level of impact that will occur; and/or set a level of impacts upon occupants over a set period of time.

Using the records from the database, zone data can be derived for individual zones 10, estates 30 or for the portfolio 34. Figure 9a is an example based on data from 85 estates, namely 85 student bars having differing parameters. Figure 9a is a graph illustrative of a total capacity potential 36 of the 85 bars, which is the range of potential impacts, counted on the Y-axis, upon occupants in a property portfolio 34 over a 12-hour period in a sampled day, and represented by a shaded-band across a time period of approximately 09.00 to 21.00, at 15 minute intervals, as shown on the X-axis. Figure 9a is based on actual data obtained from the example portfolio 34 on a given day in March 2022. In Figure 9a the ‘impact’ upon the occupants was from an audio-visual source i.e. a broadcast e.g. an advertisement.

The total capacity potential 36 can take into account zone operating times. As described above, an impact upon an occupants occurs while the source is operating and/or present and having an effect upon an occupant e.g. delivering content and/or services. The parameters of the zone can further influence the impact e.g. is a sports screen or projector provided in a zone.

The maximum occupancy 38 and minimum occupancy 40 are indicated for the portfolio 34. In the example of Figure 9a the maximum 38 and minimum 42 levels are the uppermost and lowermost occupancy levels, respectively, of all of the zones 10 i.e. figures were taken from the busiest and quietest individual zones 10 in the portfolio 34. Maximum occupancy 38 and minimum occupancy 40 levels can, however, be measured using different statistical techniques. An average occupancy level 42 can be used for modelled occupancy levels e.g. taking an average across the portfolio as an indicator for a zone that does not have a means for counting and uses a modelled count. The average occupancy level 42 can discount closed venues.

Table 1

Table 1 lists parameters of six different zones 10 and/or estates 30, which are part of the example portfolio 34 discussed above and described in relation to Figure 9a (i.e. 85 student bars). Figures 9b to 9f illustrate an average occupancy graph 44 that indicates a percentage occupancy level (note: percentages are represented decimally i.e. 0.3 = 30%, 0.6 = 60% etc.) for different parameters for the zones 10 listed in Table 1.

As described above, data gathered in the hub can include parameter information and actual footfall counted. By taking into account an actual count of occupants in a zone, and parameters of those zones, models and/or forecasts and/or impact setting can be improved. Moreover, using the records and zone data held in the hub 32 can support retrospective data analysis for adjusting parameters and/or settings to achieve an impact level.

Figure 9b illustrates a percentage occupancy level over a one-day period from 09.00 to 21.00 for the average of two bars 46 (1012, 1029) compared to the average of four bars having caferestaurant facilities. It can be appreciated for this given day that occupancy levels of bars having caferestaurant facilities tend to have higher occupancy levels.

Data from the hub 32 can be used to provide contextual comparison. For example, Figure 9c illustrates a percentage occupancy level over a one-week (on X-axis, Sunday is on the left and Saturday on the right), averaged per day for two bars 46 and four bars having cafe-restaurant facilities that are represented in Figure 9b. It can be appreciated for this given week that occupancy levels of bars having cafe-restaurant facilities tend to have higher occupancy levels mid-week compared to weekends.

Figures 9d to 9f illustrate the percentage occupancy level over a one-day period from 09.00 to 21.00 for the bars of Figures 9a and 9b for different parameters. In particular: Figure 9d shows the average occupancy levels for city 50 compared to campus 52 locations; Figure 9e shows the average occupancy levels for estates 30 showing sports 56 compared to an estate that does not show sport 50; and Figure 9f shows the average occupancy levels for the estates of Table 1 that don’t show sport 50, have a screen 58 for sport or a projector 60 for sport.

While the sample size is small, the graphs of Figures 9b to 9f illustrate that data can be collated and extracted to improve the modelling and forecasting taught herein. In the example provided, it can be appreciated from Figure 9c that bars have higher footfall on a Monday, while zones having multiple functions e.g. a bar plus a cafe have a higher overall footfall throughout the day. Analysis, including statistical analysis, supports correlation between footfall and parameters e.g. the zones of Table 1 are shown in Figure 9d to have a higher footfall (compared to a city centre location) if based on a campus because it does not have to compete with comparable zones. It can be shown that zones 10 or estates showing sports see higher footfall overall, especially if sports are shown on screens. Using the records and zone data for the 12 estates 30, the estimated impact level can be determined for one of the other 12 estates, or even a new 13^th estate. Estimation can be for a specific time and/or day, and data records selected accordingly. Determination begins by selecting a zone and obtaining an occupancy level percentage for that zone at a specific time of day or a time window e.g. 15 minutes - which can be based on measured footfall, the footfall of the nearest student bar, an average value of footfall in the nearest zone and/or the average value of footfall for the portfolio 34 at the corresponding time and/or day.

With an occupancy percentage, and a nominal maximum capacity of a zone and/or estate the estimated number of occupants that can be subject to an impact from a source can be determined. For example, occupancy level is determined at 50% and the capacity of the zone is 200 people, thus the estimated potential is 100 people.

The parameters of the zone 10 or estate 30 are then used to weigh the estimated potential and improve the accuracy of the determined estimate. In this example, the parameters of the zone are shown in Figure 8, in which the specification of the zone is indicated by the values in bold, with a different cell-border. In particular, the zone has (with weightings indicate in parentheses): a maximum capacity greater than 151 (1.1); is open on average 5 or 6 days per week (1.05); is open no more than 9 hours per day (1.05); has no more than 3 display screens (1.0); has an audio amplification power of 3kW or less (0.75); includes a sports screen (1.05); is ranked in the “top 10” of student-bars (1.1); the location is ’’premium” (1.0); the weather is ‘rain’ (1.0); and there are no events taking place (1.0). The product of the selecting weighting is “1.1031” i.e. a factor of 110%. Weightings can be dynamic and/or interdependent. Determination of the impact can be limited to a subset of weightings, which by way of example can include: the number of opening hours; the location e.g. city location, or campus location; and the format of the zone e.g. does the zone have a bar, show sports or serve food.

Assuming the government broadcast is successfully played every 15 minutes via the display screens and audio system, and the occupancy level for that 15-minute time window is 50%, then with a maximum capacity of 200 and a weighting of 1.1031 then it is estimated that there are 110 impacts in this 15-minute period.

Example Overview

Figure 10 is an overview of a process SI 00 that is part of the determination of an impact in a zone 10 from a source e.g. a screen 16, which can be associated with a playback device. As described above, numerous parameters can influence the impact of a source upon occupants in a zone. Not only can an impact be determined, but the parameters can be adjusted to control the impact.

Continuing with an example wherein the zone 10 is a bar/cafe, three parameters can be: the opening hours of the bar, which determines the accessibility of the zone 10 to occupants; the location, which can influence the level of occupation of the zone; and the number of screens 16 in the zone 10, which influences the impact of the source upon the occupants. Data is extracted from detectors 14 that monitor the occupancy levels, which can be determined for specific time periods e.g. in 15-minute windows. Data associating the source and the zones is recorded such that the impact of the source behind the emissions can be associated with a zone or zones. Median levels and graphical representations can be analysed to determine relationships between occupancy levels, parameters and the impact of the source upon said occupants - and with continuous analysis the influence i.e. the weighting of the parameters in the model can be dynamically updated. Median levels of occupancy are used to mitigate irregular readings. Weightings of parameters in the modelling and/or forecasting, or setting on an impact level, reflect the ability of certain features e.g. parameters of a zone e.g. a bar to increase occupancy by a certain amount.

At SI 02, occupancy levels determined from counters 14 are normalized based on weightings of parameters, such that outliers e.g. irregular spikes or dips in readings are removed form the determination. Normalisation can select readings within a statistical tolerance band e.g. within +/- 1 sigma. At SI 04, readings from detectors 14 are modified e.g. normalized. In one example, readings are summed for each window of readings e.g. for each 15-minute slot and dividing by the count by 1 the sum of the capacities of each zone 10. At S106, the processed counter 14 data e.g. aggregate counter occupancy is mapped to at least one zone 10, and preferably to at least one of a zone 10, estate 30 and a portfolio 34 of zones 10 and estates 30. In the mapping, weightings of each parameter are applied to the zones 10 and/or the individual sources of emissions within those zones e.g. the screens, and the playback devices providing information to said screens 16. Weightings take into account the parameters of each zone 10. In SI 06 an occupancy percentage can be determined for a zone, and in SI 08 said percentage can be used to determine a calculation and/or estimation the occupancy and/or impact in each zone or associated with the source of emissions in each zone.

In one non-limiting example, a detector 14 measures that in one specific window the national occupancy level is 30%. A zone 10 without a detector has a capacity of 120 people, and three parameters and their associated weightings for the zone are used to determine an estimated occupancy level such that an impact level can be determined. The three parameters used are: the number of opening hours, which is lower than a national average and has a weighting of -3.1% i.e. a weighting multiplier of -0.031; the location having a weighting of 2.2% i.e. a weighting multiplier of 0.022; and the number of screens in the zone giving a weighting of 1.7% i.e. a weighting multiplier of 0.017. The individual weighting multipliers are summed with a value of ‘ 1’, then multiplied together to determine a weighting influence of 1.00715. An adjusted occupancy percentage can be determined as 30% x 1.00715, giving 30.21%, which applied to the capacity of the zone i.e. 120 x 30.21% = 36.26, therefore 36 occupants.

Example Formulae

Parameters that influence the occupancy of a zone 10 have a consequential effect upon the impact of a source upon occupants in a zone. Physical parameters can be adjusted to influence the occupancy, and in turn the impact. Similarly, the source of emissions can be adjusted to modify the impact. Through measurements and analysis, weightings for each of the parameters can be determined, said weightings derived from the relationship between occupancy and the parameters of a zone. In Figure 11, a series of formulas are used to determine a correlation between the parameters of a venue and an estimated occupancy level. The formula is described as follows:

Initially, the ‘total weight’ of a plurality of parameters associated with an output device i.e. the source of emissions, such as a screen and its playback device, in a zone 10, can be determined by multiplying each ‘weight’ associated with the parameters in said zone. As described elsewhere herein, a weighting can include the opening hours, the number of screens in a zone, etc. Overall, the influence of parameters upon occupancy levels is analysed to determine their weightings. The total weight can be determined for individual zones and/or the associated source of emissions e.g. a screen connected to a playback device.

Thereafter, for an individual zone, a ‘base occupancy’ level can be determined from occupancy measurements associated with said zone. Base occupancy is calculated by dividing the measured ‘occupancy’ level for said zone 10 by the ‘total weight’. In this way, the impact of a specific parameter weighting in a zone is amortised for the purposes of determining a statistical average occupancy level. An individual ‘base occupancy’ level is determined for each measurable zones. Base occupancy can be determined, from occupancy measurements, in windows, or snapshots e.g. in 15-minute periods. In other words, the variation in occupancy can be monitored continuously and grouped in to time-windows to support granular analysis, determination and estimation of occupancy.

An ‘average base occupancy percentage’ is then determined from the sum of all ‘base occupancy’ levels determined from all measurable zones, which is then divided by sum of the ‘occupancy capacity’ for all of said measured zones. More specifically, said percentage can be determined for a time-window e.g. for each 15 minute period through the opening hours of the zone 10.

For any given time window, which could be between one hour and 24 hours, and is preferably Ihour or less, an ‘estimated occupancy percentage’ can be determined for a zone that does not have functioning occupancy measurement equipment. Such an estimation is, preferably, based on a time- windows of 1-hour or less, and preferably for 15-minute windows, to accommodate fluctuations in occupancy levels throughout the opening hours. The ‘estimated occupancy percentage’ is based upon a multiple of (i) the ‘average base occupancy percentage’, which was obtained from occupancy levels in measured zones, and allows for occupancy percentages to be determined for a given time window, and (ii) the ‘total weight’ of the actual zone whose occupancy is to be estimated. If, for example, the average base occupancy percentage is 25%, the total weighting is 1.05 and the maximum capacity of a zone is 80, then it is estimated that there are 21 occupants in the zone (25% * 80 = 20 * 1.05 = 21).

The processes herein not only accommodate for estimating an impact, or setting an impact, particularly for zones that do not have occupancy counters i.e. only their maximum occupancy and parameters are known, which can be used for a weighting, but impacts can be determined e.g. estimated, when a counter fails or loses connectivity.

Detailed Example

Figure 12 is a detailed flowchart showing, by way of example, a selection of individual steps in the process SI 10 for determining an impact. At SI 12, data is extracted directly from a monitor 28, which in examples herein is a detector 14 that can count the incoming and outgoing occupants from a zone to determine occupancy of the zone 10. At S 114, data is consolidated for at least one of a detector 14, zone 10, screen 16 and playback device, said playback device being the source of emissions via the screen. At SI 16, assessment and correction of the consolidated data can accommodate, at least: incorporation of backdated data that was not obtained because of connectivity issues; adjustment in response to errors flagged on individual counter feeds; consolidating the readings e.g. count of people entering and leaving a zone at in individual zone, screen or player (source of emissions). Monitoring the occupancy levels and emissions from a source e.g. playback from a screen can, therefore, be broken down to a granular level. At SI 18, corrections are applied to the retrieved data e.g. instances where negative capacity is detected can be reset to ‘zero’ and/or where the maximum capacity of a zone 10 and/or estate 30 is exceeded, the occupant count can be capped.

Clean data that has had adjustments for corrections and/or errors can be stored at SI 20. Error records can be retained. Errors can include detector 14 outage, anomalies from a determined the national average, a malfunction and site-error. Errors can be recorded at a granular level i.e. for each window or snapshot of data captured e.g. for each 15 -minute interval. Data can be tabulated for each zone 10 and/or estate 30. Attributes can be assigned to portions of data to determine its suitability for subsequent determination of the impact or setting an impact. In one example, each zone 10 and/or estate 30 can be labelled with one of the following statuses: green, wherein information is error free and reliable; blue, wherein at least 1 detector is faulty; and orange, wherein one or more components in a zone 10, such as a detector, screen or playback device has malfunctioned.

At SI 22, one or more intervals of data e.g. packets of data representing 15-minute intervals can be removed from any further determination of the impact. At SI 24, data associated with zones 10 within an estate 30 or portfolio 34 can be adjusted in light of opening hour data, such that data captured when a zone is closed or inoperable is discounted. At SI 26, capacity for a zone is capped at the nominal occupancy limit. At SI 30, analysis is normalized by inverting the weighting percentages e.g. the sum of weightings having the percentage influences of 2.2%, -1% and 3% are summed as follows to obtain a weighting lever: (1 + 0.022)*(l+(-0.01))*(l+0.03) = 1.042133. This value can be used to determine normalised occupancy.

At SI 32, the weighted occupancy level is calculated for at least one of the zone 10, estate 30 and portfolio 34. The weighted occupancy level percentage can also be calculated. The average and/or median level of occupancy can be determined. The weighted occupancy level can be calculated at a granular level for each ‘window’ or ‘snapshot’ of data captured e.g. calculating the weighted occupancy level and/or weighted occupancy level percentage for a 15-minute interval. At SI 34, the occupancy levels can be mapped to each zone 10 in an estate 30 or portfolio 34 and mapped to each of the sources of emissions in each zone e.g. the display screens and the associated playback devices. Occupancy levels can be mapped to other zones from which measurements were taken from detectors 14 or monitors 28 in order to benchmark or compare occupancy levels and/or occupancy levels can be mapped to other zones that have no detectors of occupancy levels. At SI 36, parameter weightings are applied to zones 10 and their associated screens 16 and/or playback devices such that a determination of the estimated impact is adjusted in accordance with the individual parameters of each zone. The weightings of the parameters applied to the mapped occupancy levels tuning of the determination of the estimation of the occupancy levels. This can be achieved at each zone and stored as “modelled data” at SI 38.

With an occupancy level determined at a zone level and/or at the level of a screen 16 and/or associated playback device, the actual emission data recorded from the source e.g. screen 16 is obtained at S140. This process counts the occupants that were in a zone at the time an emission e.g. a playback via a screen 16 occurred or determines based on an estimation and weightings an estimated number of occupants. Further, the playback schedule and playback logs can be processed to determine if an omission occurred. A monitor 28 can be used for this purpose. With the emissions determined, this can be correlated with the modelled occupancy data at SI 42. In other words, using the determined, estimated or adjusted occupancy levels for a zone 10 and the corresponding emission records e.g. playback from a screen 16, an impact upon those occupants can be determined. Records of the impacts can be stored at SI 44.

Detailed Example

Figure 13 - improves upon the process of Figure 12, in which additional processes S139a to 139c are added to utilise external measurements and/or data. As described elsewhere herein, statistical analysis of the influence of the parameters, including technical parameters of zones 10, can determine weightings, which can be correlated with measured levels of occupancy to determine average base occupancy percentages and, subsequently estimated occupancy percentages, as per Figure 11. It follows that adjusting the parameters and/or the sources of emissions e.g. screens driven by playback devices, that an impact can be set. Data associated with parameters and occupancy counters can be supplemented with external data sources.

In SI 39a, the stored modelled data at S138 is conditioned for alignment e.g. correlation with the data from external sources of data and/or measurements. Only one source is illustrated in Figure 13, although a plurality of sources can be used. In one example, the external data can be provided by a telecommunication provider that, through historical and/or live data, and trilateration of telephone signals and/or GPS data, can support the determination of how many occupants are in a zone 10. The data can be aligned at SI 39b, and the verification performed at SI 26 can be repeated at SI 39c.

Detailed Example

Overall, through statistical analysis, parameters that influence occupancy levels can be determined. Through at least one of counting, estimating and using third-party data or measurements the occupancy of a zone can be determined for a given time period e.g. a 15-minute window.

The impact or level of exposure of emissions from a source of stimulant and/or resource upon a zone can be measured, e.g. the impact upon occupants of said zone. The impact can be a measurable level of exposure e.g. concentration level emitted from a source, or frequency/count of exposure to events. The source can be an environmental factor, a means of communication or a resource. The impact can relate to levels of exposure for safety monitoring, utilization or exposure to information. The level or count of the impact can be measured for individuals and/or the total number of occupants measured or estimated to be in the zone that are subject to collective number of occupants. The impact level can vary according to the status of the source and/or the parameters of the zone.

Correlating the occupancy levels of a zone with the measured emissions from a source enables the impact to be determined and/or adjusted. The impact can be at least one of determined and/or adjusted for specific time- windows. To set an impact level, parameters and/or the source of emissions can be adjusted.

Figure 14 is a schematic of a system 100 embodying the present invention and capable of executing a method embodying the present invention. The system 100 includes a bus 102, at least one processor 104, at least one communication port 106, a main memory 108 and/or a removable storage media 110, a read only memory 112 and a random access memory 114. The components of system 100 can be configured across two or more devices, or the components can reside in a single system 100. The system can also include a battery 116. The port 106 can be complimented by input means 118 and output connection 120. The processor 104 can be any such device such as, but not limited to, an Intel(R), AMD(R) or ARM processor. The processor may be specifically dedicated to the device. The port 106 can be a wired connection, such as an RS -232 connection, or a Bluetooth connection or any such wireless connection. The port can be configured to communicate on a network such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which the system 100 connects. The read only memory 112 can store instructions for the processor 104.

The bus 102 communi cably couples the processor 104 with the other memory 110, 112, 114, 108 and port 106, as well as the input and output connections 118, 120. The bus can be a PCI /PCI- X or SCSI based system bus depending on the storage devices used, for example. Removable memory T1

110 can be any kind of external hard-drives, solid state drives, flash drives, for example. The device and components therein are provided by way of example and does not limit the scope of the invention. The processor 104 can implement the methods and perform any of the calculations described herein. The processor 104 can be configured to retrieve and/or receive information from a remote server, such as a server that hosts the database described herein, or other devices, such as sources, sensors, monitors, and/or the like as described herein.

The system 100 can also include an application program interface (API) 122 for managing the sources and/or storing the parameters of the or each zone, which can be achieved from a user’s device e.g. via an app on a mobile device. The system can include a footfall counter 124 for measuring and recording the footfall through a doorway of a zone. A plurality of sources 126 to 132 can be managed and/or monitored. Via the system a user can access the database to use the records for recording and/or obtaining footfall data, source data and/or parameter data. The database can be a graphical database.

While several embodiments of the present disclosure have been described and illustrated herein, those of ordinary skill in the art will readily envision a variety of other means and/or structures for performing the functions and/or obtaining the results and/or one or more of the advantages described herein, and each of such variations and/or modifications is deemed to be within the scope of the present disclosure. More generally, those skilled in the art will readily appreciate that all parameters, dimensions, materials, and configurations described herein are meant to be exemplary and that the actual parameters, dimensions, materials, and/or configurations will depend upon the specific application or applications for which the teaching of the present disclosure is/are used. Those skilled in the art will recognize or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the disclosure described herein. It is, therefore, to be understood that the foregoing embodiments are presented by way of example only and that, within the scope of the appended claims and equivalents thereto, the invention may be practiced otherwise than as specifically described and claimed. The present invention is directed to each individual feature, system, article, material, and/or method described herein. In addition, any combination of two or more such features, systems, articles, materials, and/or methods, if such features, systems, articles, materials, and/or methods are not mutually inconsistent, is included within the scope of the present invention.

The indefinite articles “a” and “an,” as used herein in the specification and in the claims, unless clearly indicated to the contrary, should be understood to mean “at least one.” The phrase “and/or,” as used herein in the specification and in the claims, should be understood to mean “either or both” of the elements so conjoined, i.e., elements that are conjunctively present in some cases and disjunctively present in other cases. Other elements may optionally be present other than the elements specifically identified by the “and/or” clause, whether related or unrelated to those elements specifically identified unless clearly indicated to the contrary. Thus, as a non-limiting example, a reference to “A and/or B,” when used in conjunction with open-ended language such as “comprising” can refer, in one embodiment, to A without B (optionally including elements other than B); in another embodiment, to B without A (optionally including elements other than A); in yet another embodiment, to both A and B (optionally including other elements); etc.

As used herein in the specification and in the claims, “or” should be understood to have the same meaning as “and/or” as defined above. For example, when separating items in a list, “or” or “and/or” shall be interpreted as being inclusive, i.e., the inclusion of at least one, but also including more than one of a number or list of elements, and, optionally, additional unlisted items. Only terms clearly indicated to the contrary, such as “only one of’ or “exactly one of,” or, when used in the claims, “consisting of,” will refer to the inclusion of exactly one element of a number or list of elements. In general, the term “or” as used herein shall only be interpreted as indicating exclusive alternatives (i.e. “one or the other but not both”) when preceded by terms of exclusivity, such as “either,” “one of,” “only one of,” or “exactly one of.” “Consisting essentially of,” when used in the claims, shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “at least one,” in reference to a list of one or more elements, should be understood to mean at least one element selected from any one or more of the elements in the list of elements, but not necessarily including at least one of each and every element specifically listed within the list of elements and not excluding any combinations of elements in the list of elements. This definition also allows that elements may optionally be present other than the elements specifically identified within the list of elements to which the phrase “at least one” refers, whether related or unrelated to those elements specifically identified. Thus, as a nonlimiting example, “at least one of A and B” (or, equivalently, “at least one of A or B,” or, equivalently “at least one of A and/or B”) can refer, in one embodiment, to at least one, optionally including more than one, A, with no B present (and optionally including elements other than B); in another embodiment, to at least one, optionally including more than one, B, with no A present (and optionally including elements other than A); in yet another embodiment, to at least one, optionally including more than one, A, and at least one, optionally including more than one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitional phrases such as “comprising,” “including,” “carrying,” “having,” “containing,” “involving,” “holding,” and the like are to be understood to be open-ended, i.e., to mean including but not limited to. Only the transitional phrases “consisting of’ and “consisting essentially of’ shall be closed or semi-closed transitional phrases, respectively, as set forth in the United States Patent Office Manual of Patent Examining Procedures, Section 2111.03. Use of ordinal terms such as “first,” “second,” “third,” etc., in the claims to modify a claim element does not by itself connote any priority, precedence, or order of one claim element over another or the temporal order in which acts of a method are performed, but are used merely as labels to distinguish one claim element having a certain name from another element having a same name (but for use of the ordinal term) to distinguish the claim elements.

The invention also consists in any individual features described or implicit herein or shown or implicit in the drawings or any combination of any such features or any generalisation of any such features or combination.

Claims

1. A computer-implemented method comprising: using a database comprising a record of zone data, said zone data including at least one of: a determined occupant count of a zone; a level of utilization and/or exposure to a source of stimulant and/or resource in the zone; and at least one parameter of the zone; and at least one of: determining, forecasting, and setting an impact level of the source upon the zone based at least on the zone data.

2. The method of claim 1, wherein using the database comprises generating, maintaining, providing, updating, storing, accessing or processing: i) the database; and/or ii) the record that is stored within, or in association with, the database.

3. The method of any preceding claim, wherein the record includes at least one of: the status of the source; a level of the source; the format of the source; a count of the sources; and the connectivity to the sources of the stimulant and/or resource.

4. The method of any preceding claim, wherein the at least one parameter has an effect on at least one of: i) the determined occupant count; and ii) utilization and/or exposure to the source.

5. The method of any preceding claim, wherein the determined occupant count is determined from at least one of: an occupant counter located at the or each access point to the zone; a location sensor configured to determine at least one of the presence and position of an occupant in the zone; an estimate based on records of the occupant count of the zone and/or another zone. The method of claim 4 or 5, wherein the at least one parameter comprises: the nominal capacity of the zone; the accessibility to the zone; the environmental conditions of the zone; and the environmental conditions external to the zone. The method of any preceding claim, wherein the record amortises the determined occupant count and/or a level of utilization and/or exposure to the source level of utilisation over a period of time. The method of any preceding claim, wherein the database includes a plurality of records for a plurality of zones and the determining and/or forecasting and/or setting of the impact level for a zone comprises using at least one record and/or zone data of another zone. The method of claim 8, wherein the at least one record of another zone is selected based on a level of similarity with the zone. The method of claim 9, wherein the level of similarity is determined by comparing at least one of: at least one parameter, wherein said parameter has an effect on at least one of the occupant count, utilization and exposure; historical data of the database and/or the at least one record; the period of time in which the forecast is determined; weightings of the at least one parameter; real-time information of at least one of the occupant count and the level of utilization and/or exposure to the stimulant and/or a resource, in the zone or in the another zone.

11. The method of any preceding claim, comprising determining an occupant count of a zone, the level of utilization and/or exposure to the source, and applying a weighting factor derived from the at least one parameter of the zone.

12. The method of any of preceding claim, further comprising controlling the source in a zone for setting an impact level of the source upon the zone.

13. The method of claim 12, wherein controlling the source comprises causing or signalling the source to change its emission or broadcasting of the stimulant and/or resource in response to a determination that an initial impact is different to a pre-determined impact.

14. The method of claim 13, wherein the causing or signalling the source comprises causing or signalling the source to increase, or decrease, at least one of the: frequency, size, intensity, volume, colour saturation, duration, opacity, concentration, and power of its emission or broadcast depending on the determination of the difference between the initial impact and the pre-determined impact.

15. The method of any of preceding claim, wherein the source of stimulant and/or resource in the zone is a broadcast communication, and preferably an advertisement and/or a public information announcement such as a health warning.

16. Computer equipment comprising: memory comprising one or more memory units; and processing apparatus comprising one or more processing units, wherein the memory stores code arranged to run on the processing apparatus, the code being configured so as when on the processing apparatus to perform the method of any of claims 1 to 15.

17. A computer program embodied on computer-readable storage and configured so as, when run on one or more processors, to perform the method of any of claims 1 to 15.