Patent Description:
This disclosure relates generally to food safety and sanitation risk and compliance systems and methods.

Document <CIT> discloses relevant background art. Embodiments disclosed herein can analyze data from a variety of sources and output information that can provide insights into specific risk factors in a way that can facilitate targeted action to address such risk factors. As such, embodiments disclosed herein can allow a user to proactively reduce risks relating to food safety and sanitation. Furthermore, embodiments disclosed herein can allow a user to allocate finite resources to certain risk factors specific to that user that are most likely to result in the highest reduction in overall food safety and sanitation risk.

One exemplary embodiment includes a food safety risk system. This system embodiment includes a server connected to a network. The server includes a data collection module, a database interaction module, and a predictive analysis module. The data collection module is configured to collect data, via the network, from one or more data sources, the data being related to food safety risk and sanitation compliance tracking of a food establishment. The database interaction module is configured to store, into a database, data collected by the data collection module and to retrieve data from the database. The predictive analysis module is configured to analyze data in the database and calculate, based on the analyzed data, a probability of the food establishment violating a health code.

In a further embodiment, the server also includes a report generation module. The report generation module is configured to generate a report including the probability of the food establishment violating the health code. In some such examples, the report generator is configured to transmit the report to a client device for display.

In the above system embodiments, the probability of the food establishment violating the health code can include a predictive risk score for the food establishment. For example, the probability of the food establishment violating the health code can include the predictive risk score for the food establishment and a plurality of individual risk indicators for the food establishment. Each of the plurality of individual risk indicators for the food establishment can provide an assessment of risk relative to other food establishments. Examples that can be included as the plurality of individual risk indicators for the food establishment include personal hygiene, cleaning and sanitation, time and temperature, and documentation. Further examples that can be included as the plurality of individual risk indicators for the food establishment include cross-contamination, pest control, date marking, and other, or miscellaneous, data.

Another exemplary embodiment includes a method implemented on at least one server connected to a network. This method embodiment can include the step of collecting data, via the network, from one or more data sources, the data being related to food safety risk and sanitation compliance tracking of a food establishment. This method can further include the steps of storing, into a database, the collected data, retrieving data from the database, analyzing data in the database, and calculating, based on the analyzed data, a probability of the food establishment violating a health code.

In a further embodiment, the method can include a step of generating a report including the probability of the food establishment violating the health code. This further embodiment may also include the step of transmitting the report to a client device for display.

In the above method embodiments, the probability of the food establishment violating the health code can include a predictive risk score for the food establishment. For example, the probability of the food establishment violating the health code can include the predictive risk score for the food establishment and a plurality of individual risk indicators for the food establishment. Each of the plurality of individual risk indicators for the food establishment can provide an assessment of risk relative to other food establishments. Examples that can be included as the plurality of individual risk indicators for the food establishment include personal hygiene, cleaning and sanitation, time and temperature, and documentation. Further examples that can be included as the plurality of individual risk indicators for the food establishment include cross-contamination, pest control, date marking, and other, or miscellaneous, data.

A further embodiment includes a non-transitory computer-readable medium including instructions. When executed by a computer, these instructions cause the computer to collect data, via a network, from one or more data sources, the data being related to food safety risk and sanitation compliance tracking of a food establishment. When executed by a computer, these instructions can further cause the computer to store, into a database, the collected data, retrieve data from the database, analyze data in the database, and calculate, based on the analyzed data, a probability of the food establishment violating a health code.

In a further embodiment, when executed by a computer, the above instructions can further cause the computer to generate a report including the probability of the food establishment violating the health code. These instructions, when executed by a computer, may also cause the computer to transmit the report to a client device for display.

In the above embodiments of the non-transitory computer-readable medium including instructions, the instructions, when executed by a computer, can cause the computer to calculate the probability of the food establishment violating the health code to include a predictive risk score for the food establishment. For example, the probability of the food establishment violating the health code can be calculated to include the predictive risk score for the food establishment and a plurality of individual risk indicators for the food establishment. Each of the plurality of individual risk indicators for the food establishment can be calculated to provide an assessment of risk relative to other food establishments. Examples that can be calculated as the plurality of individual risk indicators for the food establishment include personal hygiene, cleaning and sanitation, time and temperature, and documentation. Further examples that can be included as the plurality of individual risk indicators for the food establishment include cross-contamination, pest control, date marking, and other, or miscellaneous, data.

The following drawings are illustrative of particular embodiments of the present invention and therefore do not limit the scope of the invention. The drawings are intended for use in conjunction with the explanations in the following description. Embodiments of the invention will hereinafter be described in conjunction with the appended drawings, wherein like numerals denote like elements.

The following detailed description is exemplary in nature and is not intended to limit the scope, applicability, or configuration of the invention in any way. Rather, the following description provides some practical illustrations for implementing exemplary embodiments of the present invention. Examples of constructions, materials, and/or dimensions are provided for selected elements. Those skilled in the art will recognize that many of the noted examples have a variety of suitable alternatives.

Currently, many food establishments (e.g., restaurants, meat processing plants, grocery stores, etc.) use manual processes for tracking their compliance with various health regulations at the county, state, and federal levels. For example, some food establishments undergo a periodic (e.g., monthly) self-audit, during which one or more persons checks the food establishment for health regulation compliance. Upon completion of the self-audit, the managers of the food establishment may receive a compliance "score" and/or an audit report that details the conditions of the food establishment and how those conditions pass or fail the associated health regulations.

The "self-audits" are a series of questions that may relate to one or more departments/areas in a food establishment. Each question may also be associated with one or more sections of one or more health regulations relevant to the question. The answer to each question may result in zero or more "findings. " In an embodiment, the number of findings possible for a given question has no upper bound. A finding may be one of two types: a "critical" finding, which represents a health code violation that is sufficiently severe to warrant closing the food establishment, and a "recommended practice" finding, which represents a health code violation that does not warrant closing the food establishment, but which the food establishment must correct.

Although these "self-audits" may provide useful insight into health compliance problems that have occurred in the past, they do little to help a food establishment to predict the probability of future violations of health codes. However, analyzing data integrated from <NUM>) data from the self-audits, <NUM>) data from health department inspections, and <NUM>) data from sensors of the environment of the food establishment, may be used to calculate a probability of the food establishment violating health codes in the future.

A comprehensive food safety and sanitation risk and compliance system is disclosed. In an embodiment, data from various sources is integrated into a database (or data store). The sources of data may include one or more of the following: self-audits (e.g., performed by a food establishment itself or by a third party), pest elimination services, health department inspections, dispensing equipment that monitors sanitation compliance, and various sensors within and/or near the food establishment. The data is analyzed to calculate a probability of a food establishment violating health codes in the future. Various preventative measures may then be performed in response to the calculated probability.

These solutions combined will increase the insights into risk factors and root causes of foodbome illness vectors, and facilitate customers' ability to reduce such risks in a more proactive way. This platform may be customized for multiple food safety market segments. Furthermore, this platform may also be customized to any enterprise that requires people in a multitude of remote sites to collect data and utilize analysis from these diverse data sources.

<FIG> illustrates a system <NUM> for food safety risk and sanitation compliance tracking, according to an example embodiment. The system may include a food establishment, one or more servers, one or more data stores, one or more client devices, and one or more interconnected networks (e.g., the Internet).

In an embodiment, the food establishment may be a retail store, a quick serve restaurant, a restaurant, a deli, a bakery, etc. The food establishment may include one or more sensors, and one or more dispensers (e.g., chemical dispensers). The one or more sensors may include one or more of thermometers, hygrometers, barometers, etc. The one or more sensors may track and report one or more physical, chemical, and/or environmental conditions, such as temperature, pressure, humidity, etc. The one or more dispensers may track and report dispensing events, which may include the matter dispensed (e.g., a liquid chemical compound, baking soda, water, etc.), the quantity dispensed, and a timestamp of the dispensing event. The system may use data from the one or more dispensers to identify areas of risk and/or overuse, track labor and utility usage, and to identify and alert when critical issues occur and/or are likely to occur.

In an embodiment, the one or more data stores may store data from one or more sources, such as pest control data, health department inspection data, self-audit data, self-reported data, equipment care and maintenance data, etc. Incorporating multiple sources of data helps to improve the system's predictive ability and its outcome validation.

In an embodiment, the mobile app allows the user to enter various types of data (e.g., answers to self-audit questions). For example, the mobile app can generate and present a list of task items to be checked, such as via user input at the mobile app, in order to guide the completion of the self-audit (e.g., by the food establishment itself or by a third party). In an embodiment, the mobile app displays one or more of: data produced by one or more sensors of the food establishment, data produced by one or more self-audits and/or health department inspections, and results of analyzing the data in the database pertaining to the food establishment. In an embodiment, the client portal is integrated into the mobile app. In an embodiment, the customer self-audit/data gathering and task management tool is integrated into the mobile app.

In an embodiment, the system may be used to determine if the automatically collected data differs from the manually collected data. For example, if the automatically collected data significantly differs from the manually collected (e.g., self-audit or health department inspection) data, the system may send one or more alerts and/or notifications to inform one or more individuals that there may be a problem with the food establishment's sensors and/or dispensers.

<FIG> illustrates various modules that may be executed by a food safety risk and sanitation compliance tracking system, for instance at a server <NUM> of the system <NUM>, according to an example embodiment. For example, the one or more servers may execute one or more of the following:.

<FIG> illustrates a flow of information <NUM> through the food safety risk and sanitation compliance tracking system <NUM>, according to an example embodiment. Various data streams (e.g., self-audit data, data from outside sources (e.g., health department data), resource and equipment data (e.g., from sensors and dispensers), and customer-supplied data is analyzed by the predictive analysis module. The predictive analysis module identifies trends and predictive indicators. The trends and predictive indicators are used to establish an action plan, which is communicated to various devices. The devices (and/or individuals using the devices) implement the actions in the action plan, and the system tracks the implemented action plan for performance.

In an embodiment, the predictive analysis module assumes a correlation (e.g., a pseudo-linear relationship) between the quantity of findings resulting from a health department inspection and the probability that at least one of the findings is a "critical finding. " Because of this, the predictive analysis module may use Bayesian algorithms to calculate probabilities of health code violations.

As previously stated, each question of a self-audit may be associated with one or more sections of one or more health regulations relevant to the question. In an embodiment, there can be only one unique finding per combination of self-audit, question of the self-audit, and area of the food establishment. For example, a self-audit question relates to personal hygiene, and relates to <NUM> of <NUM> departments in a store; thus, during any given audit of this store, there are <NUM> opportunities for this question to result in a finding. If only <NUM> finding for this question is documented, then there is a <NUM>% (<NUM>/<NUM>) probability that the question will result in a finding for that store.

Continuing with this example, if there are <NUM> self-audit questions that relate to personal hygiene, and each of the <NUM> questions may apply to one or more departments of the store, rolling up the <NUM> questions would result in <NUM> (<NUM> departments x <NUM> questions) opportunities for a finding. If there is only <NUM> finding documented, then there is a <NUM>% (<NUM>/<NUM>) probability of a finding.

In an embodiment, the system gathers data for all self-audits and health inspections of a food establishment, and compares the self-audit data with the health inspection outcomes. Using this analysis, the predictive analysis module may calculate, for the food establishment in question, the probability of a future inspection resulting in one or more findings.

The predictive analysis module may use a classifier to predict probabilities of future health code violations. To train the classification model, health department inspection data is aggregated and input into the classification model. When the classification model is used to predict probabilities of future health code violations, current data from the various data streams is input into the classification model. In an embodiment, a food establishment's risk factors are weighted (e.g., risk factors A, B, and C are inconsequential individually, but together, they signal a significant food safety risk.

Data produced by health department inspections ("HDI") may include (for each inspection): the date(s) of the inspection, the name(s) of the inspector(s), the beginning and ending time(s) of the inspection(s), the name of the food establishment inspected, geographical coordinates (e.g., latitude and longitude) of the food establishment inspected, a quantity of critical findings resulting from the inspection, a quantity of recommended practice findings, one or more sections of a health code related to a finding, etc. In an embodiment, sanitation compliance data is integrated into the database. The sanitation compliance data may be generated from various sources, including chemical dispensers that communicate data related to dispensing events (e.g., type of dispensing event and timestamp). In an embodiment, the chemical dispensers may communicate wirelessly. In an embodiment, the system executes an algorithm that interprets the event data from the dispensers to determine compliance insights (e.g., store #<NUM> sanitized its floor only <NUM> days of the last <NUM> days).

Data from sensors and dispensers may vary in importance. For example, if a coffee pot's thermometer measures a temperature that is too low, the coffee in the pot may spoil more quickly than if the temperature was within an acceptable temperature range; however, if a thermometer that measures rotisserie chickens indicates that the temperatures of a cooked rotisserie chicken is too low, the bacteria within the rotisserie chicken may not have been adequately destroyed, resulting in a possible food safety risk for a customer of the food establishment.

In an embodiment, some individuals (e.g., field representatives of a service company) have access to the system. For each food establishment, the system may inform one or more individuals of the food establishment's current risk score, the conditions giving rise to the current risk score, and a list of issues that, if addressed, would result in the largest reduction to the food establishment's current risk score.

A report can be generated that provides risk factor information relating to one or more customer stores. <FIG> shows one embodiment of such a report. <FIG> and <FIG> show another embodiment of such a report, where <FIG> illustrates an overall customer report and <FIG> illustrates certain aspects of that customer report for a selected customer store. For instance, the food safety risk and sanitation compliance tracking system <NUM> can generate and display such reports at, for instance, one or more client devices. In one example, the predictive analysis module at the server can analyze data input into the database and calculate, based on the analyzed data, a probability of a store (e.g., food establishment) violating a health code, and the report generator module at the server can generate the report for display at one or more client devices. For instance, in this example, the predictive analysis module at the server can analyze data input into the database and calculate a predictive risk score and individual risk indicators for each of one or more stores, and the report generator module at the server can generate the report with the calculated predictive risk score and individual risk indicators for each of one or more stores for display at one or more client devices.

<FIG> illustrates a report <NUM> for a food establishment, according to an example embodiment. A food establishment's report may include an indication whether data from one or more data streams is trending positively or negatively. The report may include an indication whether a food establishment's risk is increasing, decreasing, or unchanged. In the embodiment illustrated in <FIG>, information for store #<NUM> is displayed. The "Current High Risk Scores" section of the report lists store numbers, the current risk score for each store number, and an indication whether the current risk score has increased, decreased, or remained unchanged from the previous report. The stores listed in a selected store's report may be associated with the selected store by geography, by organizational structure, or by some other association.

In an embodiment, a food establishment's report may indicate the food establishment's performance in one or more categories of a health code. In an embodiment, a food establishment's report may indicate the food establishment's performance in one or more categories of a health code relative to other food establishments belonging to the same organizational entity (e.g., brand, chain, company, division, etc.) as the food establishment.

In the embodiment illustrated in <FIG>, the food establishment's relative performance in each category (relative to other food establishments) is illustrated as an arc. The length of an arc is inversely proportional to the food establishment's relative performance in that category. In an embodiment, an arc's color may indicate the food establishment's relative performance in that category.

In an embodiment, training programs are tailored to the risk factors identified in a food establishment's report. The training programs may be delivered to employees of the food establishment via its client portal. In an embodiment, links to training programs may be delivered to employees of the food establishment upon a trigger condition being detected (e.g., floor not cleaned at the end of the day).

<FIG> and <FIG> show a report <NUM> for a food establishment, according to another example embodiment. As noted, the food safety risk and sanitation compliance tracking system <NUM> can generate and display the report <NUM> at, for instance, one or more client devices.

The report <NUM> includes a store selection panel <NUM>. The store selection panel <NUM> can receive user input specifying one or more specific customer stores, and the report <NUM> can generate other information shown in the report <NUM> according to the specified one or more customer stores (e.g. food establihsments) at the store selection panel <NUM>. In the illustrated example, "all" stores are selected at the store selection panel <NUM>. As such, the server of the system <NUM> can retrieve input information from the data store relating to the selected one or more stores at the store selection panel <NUM>, process this specified information, and generate the report <NUM>.

The report <NUM> also includes a risk category display <NUM>. The risk category display <NUM> provides a breakdown of customer stores according to the predictive risk category of these stores. As shown in the example here, the risk category display <NUM> breaks the customers stores into three categories that signify low, moderate, and high predictive risk. Any category of the risk category display <NUM> can be selected by a user and, upon such selection, the customer stores in the selected category can be displayed in the report <NUM>. As such, the risk category display <NUM> can allow a user to view a subset of customer stores in isolation. For instance, a user may select the high category predictive risk stores from the risk category display <NUM> and the report can then display detailed information for the customer stores in the high category predictive risk. This may allow a user to selectively assess detailed information relating to an individual category of stores and devote finite resources to addressing risk in this category of stores.

The report <NUM> further includes a store risk panel <NUM>. The store risk panel <NUM> can list specific customer stores <NUM> and, for each specific customer store, a predictive risk score <NUM> and a risk change indicator <NUM>. The predictive risk score <NUM> can be computed based on various data input into the system, as described elsewhere herein, and can represent a probability of the store (e.g., food establishment) violating a health code. The predictive risk score can represent the relative likelihood that the associated customer store will have more than a predetermined number of health inspection findings, if such inspection were to be currently undertaken. As such, the higher the predictive risk score, the higher the likelihood that the associated customer store would currently have more than a predetermined number of health inspection findings. The risk change indicator can represent a change in the predictive risk score. The risk change indicator can designate (e.g., using an up/down, sideways arrow and/or red, green, or neutral color) whether the predictive risk score for the associated customer store has increased, decreased, or remained constant over a preset past period of time (e.g., since the last report was run, since the last inspection, over the last month, quarter, etc.). In the example shown here, the report <NUM> can also include a total number of customer stores <NUM> that have had an increase in the predictive risk score, a decrease in the predictive risk score, and that have had no change in the predictive risk score over the preset past period of time.

The report <NUM>, as shown here, can additionally include a risk indicator panel <NUM>. The risk indicator panel can include a number of individual risk indicators <NUM>. Each risk indicator <NUM> can provide an assessment of performance in a specified food risk category for a customer store (e.g., food establishment), or grouping of customer stores if so selected. In some cases, each individual risk indicator <NUM> can represent a probability of a store (e.g., food establishment) violating a health code in the corresponding category for that individual risk indicator <NUM>. In the illustrated example, there are risk indicators <NUM> for each of personal hygiene, cross-contamination, cleaning and sanitation, time and temperature, pest control, date marking, documentation, and other. Also in the illustrated embodiment, each risk indicator <NUM> can display the performance of a customer store, or grouping of customer stores if so selected, in the individual risk indicators, for instance relative to other, non-selected customer stores or relative to a predefined standard for each risk indicator. Here, each risk indicator <NUM> is represented by an arc and accompanying a gauge, with the gauge placed at a location on the arc according to the performance for the specified risk indicator. The act can include distinguishable (e.g., color-coded, pattern-coded) portions along it, with each distinguishable portion corresponding to a different level of risk of incurring a finding during a health inspection of the store(s).

The report <NUM> also includes an action portion <NUM>. The action portion <NUM> can specify particular action items that a user can take to reduce the predictive risk score <NUM> for a specified customer store, or grouping of specified customer stores. For instance, if the personal hygiene risk indicator <NUM> is relatively high, the action portion <NUM> may specify particular action items (e.g., sensors indicate hand washing soap dispenser is being used five times an hour, but use should be increased to ten times an hour based on the number of personnel working at the store; train employees on hand washing procedure and frequency; increase the frequency of self-audits) that can be taken at the selected customer store(s) to reduce the personal hygiene risk indicator <NUM>, and ultimately reduce the overall predictive risk score <NUM>. The report <NUM> can generate particular action items for display in the action portion <NUM> based on instructions stored within the system described herein. Such instructions can, for example, include specified actions associated with each risk indicator in the report. In one embodiment, the action portion can include a portion with one or more particular action items directed to the food establishment and another portion with one or more particular action items directed to someone other than the food establishment (e.g., a third party service provider).

In some cases, it can be useful for the report <NUM> to include a geographic display <NUM>. The geographic display <NUM> can show the location of customer stores along with a relative indication (e.g., color, shape, etc.) of each displayed store's predictive risk. This can allow the report to convey whether stores in a particular risk category (e.g., high risk) are geographically concentrated, which may be useful in assessing remedies to reduce risk factors for such stores.

<FIG> shows the report <NUM> generated for a specific store (e.g., food establishment) <NUM> selected by a user from the store risk panel <NUM>. Once selected, the specific store <NUM> may be displayed in isolation within the store risk panel <NUM>. As shown here, the specific selected store <NUM> has the risk change indicator <NUM> showing that the predictive risk score <NUM> has increased over the past predetermined period of time. The display of the total number of customer stores <NUM> can also be isolated to the specific selected store <NUM>. In other examples, two or more stores can be selected by a user from the store risk panel <NUM> and the specific selected two more stores can be displayed as described herein.

Selection of the specific store <NUM> can cause the report <NUM> to generate the risk indicator panel <NUM> for the specific store <NUM>. In this example, the risk indicator panel <NUM> includes individual risk indicators <NUM> each showing relative performance of the specific store <NUM> in the associated category relative to other, non-selected stores. As seen here, the risk indicator panel <NUM> shows that the specific store <NUM> is performing at a high level of risk (e.g., risk of a finding during a health inspection), relative to non-selected stores, in the indicator categories of time and temperature, date marking, documentation, and other. As such, the risk indicator panel <NUM> for the specific store <NUM> can indicate to a user that these categories can be addressed to lower the predictive risk score <NUM> for the specific store <NUM>.

In addition, selection of the specific store <NUM> can cause the report <NUM> to generate the action portion <NUM> for the specific store <NUM>. In one example, the action portion <NUM> can display suggested action items for those indicator categories that will result in the greatest reduction to the predictive risk score <NUM>. For instance, for the specific store <NUM>, the action portion <NUM> can display suggested action items for indicator categories of time and temperature, date marking, documentation, and other-those indicator categories in which the specific store <NUM> is performing at a high level of risk (e.g., risk of a finding during a health inspection), relative to non-selected stores.

The predictive risk score in <FIG> is shown as a numerical value. In some examples, this numerical value can be an absolute value indicative of the likelihood that a predetermined number of findings will occur if a health inspection were to take place at the current time. For instance, in <FIG> the predictive score is shown as a percentage representing the estimated probability that a predetermined number of findings (e.g., tow, three, four, five, etc.) will occur if a health inspection were to take place at the current time.

The inclusion of the predictive risk score <NUM> and the risk indicator panel <NUM> can allow a user to ascertain those stores and risk indicator categories, respectively, that can be addressed via action items (e.g., shown in the action portion <NUM>) to see the greatest reduction in risk of an adverse health inspection finding.

A variety of techniques can be used by the system to process the input data and output a predictive risk score and relative risk assessment for the various risk indicator categories. As one example, a number of models can be generated and run to simulate various outcomes based on the input data. For example, for each risk indicator category, a number of models can be run using the input data relating to that risk indicator category to simulate outcomes in that risk indicator category. These results can be aggregated (e.g., averaged) to arrive at the result for each risk indicator category, and then this result can be compared to the same in that risk indicator category for all other, non-selected stores to display the risk indicator for that category. Similarly, the predictive risk score for a store can be generated by aggregating the risk indicator categories for that store. For example, the risk indicator categories for a store can be averaged to provide the predictive risk score for that store. In some cases, risk indicator categories can be weighted in calculating this average where certain applications of the system are believed to include certain risk indicator categories that are more likely to lead to a higher risk profile for the store than other risk indicator categories.

In addition to the input data described previously herein, data relating to attributes of a store's location can be used as input data in the system. For instance, in some examples data input into the system for generating risk indicator categories and predictive risk scores can include data such as one or more of population in a vicinity of the location, income in the vicinity of the location, and tourist traffic in the vicinity.

As noted previously, the system can send alerts to a user. For example, the system's data store may include a listing of contact information associated with stores. In some cases, when a predictive risk score and/or one or more individual risk indicators changes (e.g., increases) to a predefined extent, the system can output an alert according to the associated contact information associated with the store(s).

<FIG> is a block diagram illustrating an example of a machine <NUM>, upon which any one or more example embodiments may be implemented. In a networked deployment, the machine <NUM> may operate in the capacity of a server machine, a client machine, or both in a client-server network environment. In an example, the machine <NUM> may act as a peer machine in a peer-to-peer (P2P) (or other distributed) network environment. The machine <NUM> may implement or include any portion of the systems, devices, or methods illustrated in <FIG>, and may be a computer, a server, or any machine capable of executing instructions (sequential or otherwise) that specify actions to be taken by that machine. Further, although only a single machine is illustrated, the term "machine" shall also be taken to include any collection of machines that individually or jointly execute a set (or multiple sets) of instructions to perform any one or more of the methodologies discussed herein, such as cloud-based computing, software as a service (SaaS), other computer cluster configurations, etc..

Examples, as described herein, may include, or may operate by, logic or a number of components, modules, or mechanisms. In an example, the software may reside on a machine-readable medium.

The machine <NUM> may include an output controller <NUM>, such as a serial (e.g., USB, parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection to communicate or control one or more peripheral devices (e.g., a printer, card reader, etc.).

The storage device <NUM> may include a machine-readable medium <NUM> on which is stored one or more sets of data structures or instructions <NUM> (e.g., software) embodying or utilized by any one or more of the techniques or functions described herein. In an example, one or any combination of the hardware processor <NUM>, the main memory <NUM>, the static memory <NUM>, or the storage device <NUM> may constitute machine-readable media.

Although the machine-readable medium <NUM> is illustrated as a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, and/or associated caches and servers) configured to store the one or more instructions <NUM>.

The term "machine-readable medium" may include any medium that is capable of storing, encoding, or carrying instructions for execution by the machine <NUM> and that cause the machine <NUM> to perform any one or more of the techniques of the present disclosure, or that is capable of storing, encoding or carrying data structures used by or associated with such instructions. Non-limiting machine-readable medium examples may include solid-state memories, and optical and magnetic media. Accordingly, machine-readable media are not transitory propagating signals. Specific examples of machine-readable media may include non-volatile memory, such as semiconductor memory devices (e.g., Electrically Programmable Read-Only Memory (EPROM), Electrically Erasable Programmable Read-Only Memory (EEPROM)) and flash memory devices; magnetic disks, such as internal hard disks and removable disks; magneto-optical disks; Random Access Memory (RAM); Solid State Drives (SSD); and CD-ROM and DVD-ROM disks.

The instructions <NUM> may further be transmitted or received over a communications network <NUM> using a transmission medium via the network interface device <NUM> utilizing any one of a number of transfer protocols (e.g., frame relay, Internet protocol (IP), transmission control protocol (TCP), user datagram protocol (UDP), hypertext transfer protocol (HTTP), etc.). Example communication networks may include a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), mobile telephone networks (e.g., cellular networks), Plain Old Telephone (POTS) networks, and wireless data networks (e.g., Institute of Electrical and Electronics Engineers (IEEE) <NUM> family of standards known as Wi-Fi®, IEEE <NUM> family of standards known as WiMAX®), IEEE <NUM>. <NUM> family of standards, Bluetooth®, Bluetooth® low energy technology, ZigBee®, peer-to-peer (P2P) networks, among others. In an example, the network interface device <NUM> may include one or more physical jacks (e.g., Ethernet, coaxial, or phone jacks) or one or more antennas to connect to the communications network <NUM>. In an example, the network interface device <NUM> may include a plurality of antennas to wirelessly communicate using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) techniques. The term "transmission medium" shall be taken to include any intangible medium that is capable of storing, encoding, or carrying instructions for execution by the machine <NUM>, and includes digital or analog communications signals or other intangible medium to facilitate communication of such software.

Conventional terms in the fields of computer systems and computer networking have been used herein. The terms are known in the art and are provided only as a non-limiting example for convenience purposes. Accordingly, the interpretation of the corresponding terms in the claims, unless stated otherwise, is not limited to any particular definition.

Although specific embodiments have been illustrated and described herein, it will be appreciated by those of ordinary skill in the art that any arrangement that is calculated to achieve the same purpose may be substituted for the specific embodiments shown. Many adaptations will be apparent to those of ordinary skill in the art. Accordingly, this application is intended to cover any adaptations or variations.

The drawings show, by way of illustration, specific embodiments that may be practiced. " Such examples may include elements in addition to those shown or described.

In this document, a sensor set may include one or more sensors, which may be of different types. Furthermore, two different sensor sets may include one or more sensors that belong to both sensor sets.

In this Detailed Description, various features may have been grouped together to streamline the disclosure. This should not be interpreted as intending that an unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment.

The above description is intended to be illustrative, and not restrictive. For example, the above-described examples (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used, such as by a person of ordinary skill in the art upon reviewing the above description.

Claim 1:
A system, comprising:
one or more data sources comprising one or more sensors located at a food establishment and connected to a network, the one or more data sources being configured to:
track one or more events at the food establishment; and
generate data based on the one or more events at the food establishment, the data being related to food safety risk and sanitation compliance tracking of the food establishment, wherein the data generated by the one or more sensors comprise sensor data; and
a server connected to a network, the server comprising:
a data collection module configured to collect the data, via the network, from the one or more data sources;
a database interaction module configured to store, into a database, data collected by the data collection module and to retrieve data from the database; and
a predictive analysis module configured to analyze data in the database using predictive analytics algorithms to identify one or more trends and one or more predictive indicators and calculate, based on the analyzed data, a probability of a future inspection resulting in a violation of a health code.