Systems and Methods for Tracking Controlled Items
According to the present disclosure, a method for monitoring a controlled item may include providing a server communicatively coupled to memory, wherein said memory stores certain predetermined values for a given genetic identifier of a strain of a controlled substance. The genetic identifier may be comprised of the cannabinoid profile of that specific strain. The method may also include storing a set of growth factors. These growth factors and predetermined values may be used to properly track the growth cycle of a controlled substance to avoid diversion. The method may provide for the automatic updating of predetermined values to accurately adjust for variance.
The present invention relates generally to an inventory management system and method and, more particularly, to a cloud-based validation, security and tracking system for a controlled item and method thereof.
Unless otherwise indicated herein, the materials described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
Various systems and methods have been employed to assist in the inventory management of a controlled item(s), such as drugs or medical supplies to be dispensed to patients of the medical facility, ammunition and military grade weapons to be dispensed to law enforcement agents, military personnel, and the likes. Such systems have a common goal of helping to maintain accurate records while attempting to reduce the burden of managing all of the information associated with the stocking and distribution of such control items. Such systems have been useful when applied in certain situations, but have been lacking in other areas.
For example, in efforts to affect control over grow operations and dispensaries of cannabis and cannabis based products, some states like Colorado impose strict rules requiring the tracking of individual cannabis plants each with a unique radio frequency identification (RFID) tags as well as the weighing and cataloging of all plant material, including freshly harvested (wet) cannabis. Law enforcement officers have been using such RFID systems to identify grow operations where there is an unusually high loss of marijuana between the growing, harvesting and processing steps. This is part of an effort to ensure that the cannabis industry is following guidelines set out by federal prosecutors to keep cannabis and cannabis based products from being diverted to the black market run by criminals and international drug cartels.
However, as such, RFID systems used in the cannabis industry to date have been basically similar to the systems employed in big-box stores, like Wal-Mart, Home Depot, etc. This methodology has been shown to be cumbersome, not cost efficient, and not particularly effective in accurately tracking the inventory of such controlled items. For example, reports of audit investigations, comparing the amount of cannabis a store has actually on hand with that read by the RFID system, in multiple cases found stores with far more cannabis than they were disclosing. By withholding plants from the tracking system, store owners can avoid paying taxes on those sales, which could also take place on the black market.
Accordingly, a need exists for an inventory management system for a controlled item that improves workflow validation, security, and tracking as well as cost efficiency to manage controlled items of any size volume or staffing level.
SUMMARYIn one embodiment, a method for tracking a controlled item can include providing a tracking server comprising one or more processors communicatively coupled to memory having a first memory partition and a second memory partition. The first memory partition can be segregated from the second memory partition. A genetic code of a strain of a controlled item can be transformed with a cryptographic hash function, automatically with the one or more processors, into a core code. The genetic code can be stored on the first memory partition and the core code can be stored on the second memory partition. The core code can be associated with a label code and a weight on the first memory partition. The weight can be indicative of a produced amount of the strain of the controlled item. An image of a label having an identification portion indicative of the label code and dispensed weight data can be received. The label code can be extracted, automatically with the one or more processors, from the identification portion of the image of the label. The weight associated with the core code can be reduced, automatically with the one or more processors, based upon the dispensed weight data.
In another embodiment, a system for tracking a controlled item can include a tracking server, and one or more client devices. The tracking server can include one or more processors communicatively coupled to memory. The one or more client devices communicatively coupled to the tracking server. The tracking server can execute machine readable instructions to transform with a cryptographic hash function a code comprising a genetic code of a strain of a controlled item into a core code. An image of a label having an identification portion indicative of a label code can be received from the one or more client device. Phase data indicative of a life cycle phase of the strain of the controlled item and quantity data indicative of an amount of the strain of the controlled item can be received from the one or more client device. The label code can be extracted from the identification portion of the image of the label. The core code can be associated with the label code, the first phase data, and the first quantity data on the memory. Subsequent phase data indicative of a subsequent life cycle phase of the strain of the controlled item and subsequent quantity data indicative of a subsequent amount of the strain of the controlled item can be received from the one or more client device. An acceptable amount of the strain of the controlled item can be determined based upon the quantity data and the subsequent phase data. An alert can be communicated to the one or more client device, if the subsequent quantity data is beyond the acceptable amount.
These and additional features provided by the embodiments described herein will be more fully understood in view of the following detailed description, in conjunction with the drawings.
The embodiments set forth in the drawings are illustrative and exemplary in nature and not intended to limit the subject matter defined by the claims. The following detailed description of the illustrative embodiments can be understood when read in conjunction with the following drawings, where like structure is indicated with like reference numerals and in which:
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The smart phone 102 can include memory 106 communicatively coupled to the one or more processors 104 (generally depicted as double arrowed lines). The memory 106 described herein may be RAM, ROM, a flash memory, a hard drive, or any device capable of storing machine readable instructions. Accordingly, the smart phone 102 can implement a mobile operating system as machine readable instructions stored on the memory 106 and executed by the one or more processors 104. Specific examples of mobile operating systems include, but are not limited to, Android, iOS, Blackberry OS, Windows Phone, Symbian, and the like.
Additionally, it is noted that the functions, modules, and processes described herein can be provided as machine readable instructions stored on memory 106 and executed by the one or more processors 104. The machine readable instructions can be provided in any programming language of any generation (e.g., 1GL, 2GL, 3GL, 4GL, or 5GL) such as, e.g., machine language that may be directly executed by the processor, or assembly language, object-oriented programming (OOP), scripting languages, microcode, etc., that may be compiled or assembled into machine readable instructions and stored on a machine readable medium. Alternatively, the functions, modules, and processes described herein may be written in a hardware description language (HDL), such as logic implemented via either a field-programmable gate array (FPGA) configuration or an application-specific integrated circuit (ASIC), and their equivalents. Accordingly, the functions, modules, and processes described herein may be implemented in any conventional computer programming language, as pre-programmed hardware elements, or as a combination of hardware and software components.
The smart phone 102 can include a display 108 communicatively coupled to the one or more processors 104 for providing optical signals and conveying visual feedback to users of the smart phone 102. In some embodiments, the display 108 can be configured to selectively illuminate a plurality of pixels to provide the optical signals. Accordingly, the display can comprise light emitting diodes (LED or OLED), liquid crystal display (LCD), liquid crystal on silicon (LCOS), or the like. Additionally, the display 108 can be configured to operate as a touch screen for accepting tactile input via visual controls. Accordingly, the display 108 can include a touch detector such as, for example, a resistive sensor, capacitive sensor, or the like. It is noted that the term “signal,” as used herein, can mean a waveform (e.g., electrical, optical, magnetic, or electromagnetic), such as DC, AC, sinusoidal-wave, triangular-wave, square-wave, and the like, capable of traveling through a medium. It should be understood that the term “optical” can refer to various wavelengths of the electromagnetic spectrum such as, but not limited to, wavelengths in the ultraviolet (UV), infrared (IR), and visible portions of the electromagnetic spectrum.
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The smart phone 102 can include radio frequency hardware (RF hardware) 112 communicatively coupled to the one or more processors 104 for communicatively coupling the smart phone 102 with a cellular network. Suitable cellular networks include, but are not limited to, technologies such as LTE, WiMAX, UMTS, CDMA, and GSM. In some embodiments, the RF hardware 112 can include components suitable for communicating voice information and data signals such as, for example, modems, attenuators, antennas, antenna switches, amplifiers, receivers, transceivers, or combinations thereof. Accordingly, the smart phone 102 described herein can utilize a cellular network to communicate signals over the Internet or World Wide Web.
The smart phone 102 can include a Global Positioning System (GPS) receiver 114 communicatively coupled to the one or more processors 102. The GPS receiver 114 can be configured to provide signals indicative of the location of the smart phone 102. Specifically, the GPS receiver 114 can receive signals encoded with location data, time data or both from a plurality of GPS satellites, when the GPS receiver 114 has an substantially unobstructed line of sight to the GPS satellites.
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The smart phone 102 can include a biometric sensor 118 communicatively coupled to the one or more processors 104. The biometric sensor 118 can be configured to sense a physiological characteristic of a user and encode the characteristics into a signal indicative of the physiological characteristic. For example, the biometric sensor 118 can be configured to detect fingerprints. Accordingly, the biometric sensor 118 can include a fingerprint sensor such as, for example, an optical fingerprint sensor, an ultrasonic fingerprint sensor, a capacitive fingerprint sensor, or the like. The fingerprint sensor can be positioned at different locations on the smartphone 102 as a separate input component or integrated within an input component 120 such as, for example, a home button or as part of a touch screen sensor array of the display 108. Alternatively or additionally, the biometric sensor 118 can include other sensors such as, for example, a facial recognition sensor, a blood vessel sensor, a retinal sensor, a pore sensor, a voice recognition sensor, or the like. In other embodiments, the biometric sensor 118 can be implemented as a stand-alone electronic device (e.g., a finger biometric chip or chipset).
The smart phone 102 can include one or more input component 120 for sensing tactile input and encoding the input into a signal indicative of the input. Suitable examples of the input component 120 can include a microphone, a button, a knob, a switch, a resistive sensor, capacitive sensor, a microphone, a keyboard, or the like. Alternatively or additionally, the display 108 can be configured to receive user input and operate as the input component 120. In addition to the aforementioned components, the smart phone 102 can comprise one or more additional components communicatively coupled to the one or more processors 104 without departing from the scope of the embodiments described herein. Suitable additional components include, but are not limited to, speakers, accessory lights (e.g., LED), motion sensors, or the like.
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The enterprise application can be read from the memory 20 and executed by the one or more processors 202. In some embodiments, the features of the enterprise application can be served to the one or more client devices 100 as an application running in a web browser. For example, the embodiments provided herein can be designed and implemented as a web-based application, hosted by the tracking server 200, utilizing a browser provided on the one or more client devices 100. Alternatively or additionally, the features of the enterprise application can be provided as a standalone application (applet, mobile app, etc.) configured to communicate with the tracking server 200. Access to the tracking server via the internet 12 can utilize a secure hypertext transfer protocol secure (HTTPS) for over-the-wire encryption to ensure data privacy, and to provide users with confirmation that the site providing the application is legitimate through the normal secure socket layer (SSL) handshaking protocols.
The system 10 can include an analyzer apparatus 30 configured to uniquely identify the composition of a controlled item 20. Optionally, the analyzer apparatus can be communicatively coupled to the tracking server 200 via the internet 12. The analyzer apparatus 30 can include a chemical compound analyzer in embodiments where the controlled item 20 comprises, for example, explosives or munitions. Alternatively or additionally, the analyzer apparatus 30 can include a Deoxyribonucleic acid (DNA) sequence analyzer in embodiments where the controlled item 20 comprises, for example, biological agents, genetically modified seed, livestock, cannabis plants, or the like. Accordingly, the analyzer apparatus 30 can output a code 22 corresponding the chemical composition or genetic code (e.g., DNA sequence) of the controlled item 20. The code 22 can be used to uniquely identify particular strains of the controlled item 20.
In some embodiments, the code 22 can comprise a genetic code. The genetic code can correspond to DNA sequence identification of STR (short term repeat) loci provided in a recognized DNA sequence format such as, for example, EMBL. For example, the analyzer apparatus 30 can be configured to generate the code 22 using a DNA sequence technique such as, but not limited to, fluorescence detection following electrophoretic separation. The code 22 can be used to uniquely identify strains of the controlled item 20. In some embodiments, closely related marijuana strains can be authenticated using STR loci, which can comprise short, repetitive sequence elements 3-7 base pairs in length. The repeats are well distributed throughout the cannabis genome and can be a source of highly polymorphic markers, which can be detected using the polymerase chain reaction. Alleles of STR loci can be differentiated by the number of copies of the repeat sequence contained within the amplified region and can be distinguished from one another using fluorescence detection following electrophoretic separation.
It is noted that plants with identical genetics can have widely different compound percentage formations. For example, two genetically similar OG Kush plants grown in different substrates, hydroponics vs. soil, in different grow environments (Temperature and CO2) can have substantially divergent Tetrahydrocannabinol (THC) and Cannabidiol (CBD) content. Additionally, the drying/curing climate for the final product can have a broad effect on compounds such as Cannabinol (CBN). In some embodiments, the analyzer apparatus 30 can be configured to perform a quantitative analysis such as, for example, gas or liquid chromatography, to identify the specific cannabinoids and their percentage concentrations. Accordingly, the code 22 can further include chemical concentration information.
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In some embodiments, after the core code 208 is generated, the core code 208 can be held digitally strictly within memory 204, i.e., core code 208 can be stored without being provided in print. The core code 208 can be associated with one or more additional instances of data such that the instances of data are linked in a manner that allows for the retrieval of each of the instances from information provided by one of the instances. Accordingly, each instances of the data can be identified as being related to the core code 208. For example, the core code 208 can be provided in a relational manner such as, for example, spreadsheet data, database data, or any format suitable to organize the data for use with a relational system. Optionally, memory 204 can have a first memory partition 212 and a second memory partition 214 that are segregated from one another. The core code 208 can be stored in the first memory partition 212 and can be accessible to a client device 100. Thus, along with each core code 208, information (i.e., data) may be stored and located (e.g., via querying for any data associated with the core code 208) in the first memory partition 214. The code 22 can be stored in the second memory partition 214, which is not accessible by a client device 100. Since the code 22 is inaccessible to the client device 100, the memory segregation can increase the security of data stored on memory 204. Moreover, the tracking server 200 can use the code generation module 210 to link the code 22 to the core code 208, if necessary. Accordingly, data security can be improved to limit counterfeiting, while not significantly impacting the operation of the tracking server 200.
The tracking server 200 can be configured to generate or track label codes 216. The label codes 216 can be provided on memory 204. For example, the label codes 216 can be provided on the first memory partition 212 of memory 204. In some embodiments, the one or more processors 202 can automatically execute a label code module 218 to generate the label codes 216. The label codes 216 can be covert and randomly serialized in order to prevent sequential duplication or product counterfeiting. Each label code 216 can be encoded for a reclusive cyclic redundancy check.
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In some embodiments, a user interface can be provided upon the display 108 of the client device 100. The access input can be received by the client device and transmitted to the tracking server 200. The tracking server 200 can validate the access input to the account information 226 to provide selective access to features. In some embodiments, the client device 100 can automatically detect location data of the client device 100 and provide the detected location data as part of the access input. For example, the client device 100 can use the GPS receiver 114 to determine the detected location data. The tracking server 200 can provide a user interface 130 upon the display 108 of the client device 100. The user interface 130 can provide objects indicating that the user's geo-reference location (geo-tagging) is being located. The user interface 130 can have an approval control 132 configured to allow the user to provide input to selectively determine whether to transmit the detected location data. The authentication module 224 can compare the detected location data to the location information associated with the user log-in credentials to validate the access input. If the detected location data and the location information associated with the user log-in credentials indicated are the same or about the same, i.e., within a predetermined range (e.g., about 10 meters). The use of detected location data can mitigate unauthorized access using guessed or absconded user log-in credentials.
According to the embodiments described herein, the account information 226 can include biometric data associated with the user log-in credentials such as, but not limited to, one or more fingerprints. The client device 100 can collect biometric input with the biometric sensor 118 and automatically provide the biometric input as part of the access input. The authentication module 224 can compare the biometric input to the biometric data associated with the user log-in credentials to validate the access input. If the biometric input does not match the biometric data associated with the user log-in credentials, access to the non-public features of the system 10 can be refused. If the authentication module 224 validates a user as an administrator, the method 300 can proceed to process 304.
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The cycle management object 186 can provide a summary of the grow data 228. The grow data 228 can include data that can be used to characterize the life cycle of the controlled item 20. For example, the grow data 228 can include strain data 230 indicative of the strain corresponding to the code 22 and the core code 208, phase data 232 indicative of a life cycle phase of the controlled items 20 associated with the core code 208, quantity data 234 indicative of an amount of the controlled items 20 associated with the core code 208.
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Alternatively or additionally, upon receiving input with the start cycle control 188, a new cycle object 190 can be provided upon the display 108 of the client device 100. The new cycle object 190 can include a core selection control 192 for selecting one of the core codes 208. For example, the core selection control 192 can be provided as a drop down list populated with the strain data 230 and core codes 208 associated with the grower. As noted above, the strain data 230 and the core codes 208 can be derived from a sample of the controlled item 20. Thus, the strain data 230 and the core codes 208 can be generated prior to starting the life cycle in the system 10. The new cycle object 190 can include a quantity control 194 for providing quantity data of the controlled item 20 associated with the corresponding to the core code 208. For example, at the start of the growth cycle, the quantity information can describe the number of plants or clones of the strain. Accordingly, the grow data 228, the label code 216, and the core code 208 can be associated with one other in the system 10.
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In some embodiments, the tracking server 200 can include a life cycle module 238 configured to automatically determine an acceptable amount of the strain of the controlled item 220. For example, the life cycle module 238 can be configured to predict the acceptable amount at each life cycle phase based upon the grow data 228 collected from previous phases. In some embodiments, the grow data 228 can include environmental data, such as electrical data (hours/wattage) of light provided, temperature, humidity, or the like. The environmental data can be provided by one or more environmental sensors 32 communicatively coupled to the tracking server 20. Alternatively or additionally, the environmental data provided by a public utility, manual data entry, or any other internet connected source. The life cycle module 238 can include predictive algorithms that anticipate a timeline of acceptable amounts based upon the strain of plant material being used and the grow data 228. For example, the predictive algorithms can track storage conditions, progress and/or predict expected losses and harvest yields based on the grow data 228. In some embodiments, equations can be automated to predict an expected harvest yield based on the wattage of light provided to a cannabis crop, e.g., 1 gram/watt*number of plants−expected harvest yield. Accordingly, the system 10 can monitor for any security issues, grow problems, missing inventory, etc.
Each plant has a reasonably definitive timeline based on each phase of growth, from cloning to vegetative to flower phase. For example, each strain can be correlated inherent time markers. A particular strain can have a known life cycle such as, for example, about 12 to about 16 weeks, with a defined pace for each growth phase. Should the quantity data for a phase fall outside the acceptable amount range for the phase, an alert can be issued and marked on the timeline. Generally, the number of plants should reduce as the plants move through phases as part of the selection process (only the best specimens move on). Should the number increase in any phase, an alert can be issued. In some embodiments, the alert can be communicated to the client device 100, if the subsequent quantity data is beyond the acceptable amount for that phase in the life cycle. Alternatively or additionally, the system 10 can record an explanation of the unacceptable quantity data, which can be used to clear the alert.
Moreover, the quantity data at the harvest phase and the package phase can be determined based upon previous phases. For example, plant count and weight can be correlated by the life cycle module 238. In some embodiments, a baseline can be measured for yield based on strain, number of plants, and grow methodology. Additionally, a plus or minus percentage can be assigned for each grower based upon previous yields. Should new phase results fall outside the range, an alert can be issued as well. At any time, through audits or alerts, inventory can be reconciled. Predictive logic and trend lines can be established for each facility, offering real time and future harvest data.
It is to be appreciated that during a phase of the life cycle of the strain, an event can occur that results in the loss of one or more plants, thereby reducing the quantity data. In some embodiments, the grow data management object 198 can include an event control 240 for adding event data to the grow data 228, e.g., events resulting in the loss of plants. In some embodiments, if the subsequent quantity data decreases, the event control 240 can be automatically provided. The event control 240 can associate event data that impacts the quantity data with contextual information such as, for example, a description of the event, a photograph of the event, or a video of the event. For example, a video can be made of the destruction of all plants that are not passing on to the next phase, showing a clipping of each stem, or the like. Pictures of damaged plants may also be recorded in the event record. Alternatively or additionally, the event data can be time stamped. In some embodiments, the grow data management object 198 can include an event history control 242 for displaying the event data associated with the core code 208.
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Upon harvest of multiple, genetically similar plants, all material can be catalogued and weighed to, for example, comply with state requirements. In addition, it is to be appreciated that each label code 216 is a unique code. Thus, each package is associated with a unique label code 216, which can be associated with the core code 208, the account information 226, and the grow data 228. After validating, total inventory data 229 can be updated according to the packaged quantity of the controlled item 20 (e.g. cannabis) and the inventory summary object 142 (
Moreover, the system 10 can be configured such that the functions of the packaging object 244 are only available at the location associated with the account information 226. For example, the labels 220 can only be associated with the core data 208 within the confines of the grow facility associated with the account information 226. Attempts to associate label code 216 with a core code 208 outside of the grow facility can automatically cause the system 10 to generate an alert to all designated personnel such as, for example, all administrators, law enforcement agents, government personal, or any other communication channels associated with the account information 226. Likewise, offending client devices 100, until the alert is cleared by an administrator.
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The POS object 266 can include a product control 268 for receiving input indicative of a selection of a controlled substance 20. In some embodiments, the product control 268 can provide a drop down box listing all of the label codes 216 or the core codes 208 associated with the dispensary. Accordingly, the controlled item 20 can be selected based upon the label codes 216, the core codes 208, or both. Alternatively or additionally, the product control 268 can be configured to capture an image of the label 220 associated with the package holding the controlled item 20 such as, for example, a label 220 attached to a package holding cannabis. The image can be communicated to the tracking server 20 and automatically decoded to identify the label code 216, the core code 208, or both. The POS object 266 can include a weight control 270 for receiving dispensed weight data indicative of the amount of the controlled substance being sold. In some embodiments, the system 10 can comprise a scale 24 communicatively coupled to the client device 100. Upon receiving input with the POS control weight control 270, the client device 100 can cause the scale 24 to detect a weight. The weight detected by the scale 24 can be provided as the dispensed weight data. Alternatively or additionally, the dispensed weight can be received by the weight control 270 control as tactile input, scanned from a barcode, captured as an image, or the like. After the sale is complete, the amount of the total inventory data 229 can be reduced by the dispensed weight, i.e., a dispensed weight of the controlled substance 20 is no longer tracked by the system 10.
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The order object 274 can include an inbound control 284 configured to associate the packages of the controlled item 20 with the dispensary and update the grow data 228. Upon receiving input with the inbound control 284, an image of the label 220 of each of the packages of the controlled item can be imaged by the client device 100. The images of the labels 220 can be transmitted to the tracking server 200, and the label codes 216 can be automatically extracted by the label code module 218. Accordingly, the order status data of the grow data 228 associated with the label codes 216 can be updated to shipped. Additionally, the label codes 216 can be associated with the dispensary. Moreover, since each of the dispensaries can be associated with location data, geo-reference mismatches can be automatically detected and an alert can be issued.
The dispensary portal 262 can include an inventory control 284. Upon receiving input with the inventory control 284, a listing of controlled items 20 on hand can be provided. For example, individual strains and amounts (e.g., by weight) can be listed based upon the core codes 208 and total inventory data 229 associated with the dispensary. As provided herein, the entire life cycle of the controlled item 20 from growth through retail transaction is tracked according to label codes 216 associated with core codes 208. Thus, the embodiments described herein allow for real time data that includes real time inventory information of all facilities using the system 10 and real time expected tax revenue information based on the tracked plants growing, the update inventory information for harvested material, shipping products, packaged inventory on hand, and actual tax revenue based on the calculation of sales information from all purchases. Additionally, the real time data can be archived. Data mining can be used to create a baseline for production (typical harvest weights for a particular strain, by a certain grower) and used to predict upcoming harvest data and timing of available cannabis.
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It should now be understood that the embodiments described herein address at least four primary concerns for cannabis cultivation and distribution, which include determining whether a strain is accurately represented and safe, whether a strain has been grown in one of an approved location, whether all of the cannabis have been grown legally, and where have all the plants stayed in the approved locations. Additionally, the systems described herein can be implemented without requiring grow facilities or dispensaries to purchase expensive equipment. For example, client devices with network access and running an appropriate application can leverage the functionality of a cloud computing device. For example, the label codes described herein can be readable by a camera associated with portable computing devices such as, for example, smart phones, tablets, or any other device to track particular cannabis strains as well as when and where it was grown.
The systems and methods provided herein include security measures. Unique codes can be geographically date tagged with genetic markers. For growers, accurate plant count can be managed throughout the entire growth cycle. Security issues and event logs can be embedded in the growth cycle. Predictive red flags can be implemented by a cloud based device. For instance, if 53 lbs. of Kosher Kush from Region #6 is shown in inventory of 3 dispensaries but only 50 lbs. is known to be grown for use in the locality, an alert can be issued. Audits can be done with genetic samples sent to the lab for verification. The original embedded genetic markers can be tested against the new samples to resolve the discrepancy. An expanded, similar system can be used for tracking hemp, extracts and infusions. When fully utilized, an effective, secure, real time inventory summary can be created. With early adoption a comprehensive baseline can be established for each individual grow facility and, subsequently, an entire locality (e.g., state).
It is noted that the terms “substantially” and “about” may be utilized herein to represent the inherent degree of uncertainty that may be attributed to any quantitative comparison, value, measurement, or other representation. These terms are also utilized herein to represent the degree by which a quantitative representation may vary from a stated reference without resulting in a change in the basic function of the subject matter at issue.
While particular embodiments have been illustrated and described herein, it should be understood that various other changes and modifications may be made without departing from the spirit and scope of the claimed subject matter. Moreover, although various aspects of the claimed subject matter have been described herein, such aspects need not be utilized in combination. It is therefore intended that the appended claims cover all such changes and modifications that are within the scope of the claimed subject matter.
Claims
1. A method for monitoring a controlled item, the method comprising:
- providing a server comprising one or more processors communicatively coupled to memory comprising at least one memory partition;
- associating a code comprising a genetic identifier of a strain of a controlled item with a predetermined yield;
- associating the predetermined yield with a set of growth factors and a set of predetermined growth factor values;
- receiving from a client device at least one input associated with the code;
- storing the at least one inputs on the at least one memory partition; and
- performing at least one diagnostic function based on the at least one input.
2. The method of claim 1 wherein said growth factors comprises one or more of:
- a plant medium;
- a temperature;
- a humidity;
- a light intensity;
- a light duration;
- a nutrient schedule;
- a watering schedule; and
- a trimming schedule.
3. The method of claim 1 further comprising;
- receiving from the client device data of the growth factors; and
- wherein said diagnostic function comprises: comparing the data to the growth factor values; and transmitting an expected yield to the client device based upon the comparison and predetermined yield.
4. The method of claim 1 further comprising;
- storing a set of expected yields associated with at least one growth phase on the at least one memory partition;
- receiving from a client device data of actual yields associated with the at least one growth phase; and
- wherein said diagnostic function comprises: comparing the actual yields to the expected yields; and notifying an alerted device if the actual yields vary from the expected yields by an amount greater than a predetermined tolerance.
5. The method of claim 4 wherein said alerted device comprises one or more of:
- the client device;
- a law enforcement device; and
- a regulatory body device.
6. The method of claim 1 wherein said predetermined yield comprises an average of acceptable yield data from representative growth cycles of the strain.
7. The method of claim 6 further comprising:
- storing a set of expected yields associated with at least one growth phase on the at least one memory partition;
- receiving from the client device new data of actual yields associated with the at least one growth phase and data of the actual growth factor values; and
- wherein said diagnostic function comprises: comparing the actual yields to the expected yields; comparing the actual growth factor values to the predetermined growth factor values; and updating the expected yield by appending the acceptable yield data with the new data of actual yields and recalculating an average if the actual yields and actual growth factor values do not vary from the expected yields and predetermined growth factor values by an amount greater than a predetermined tolerance.
8. The method of claim 1 wherein said genetic identifier comprises a quantity of at least one cannabinoid present in the strain.
9. A system for monitoring a controlled item comprising:
- a server comprising one or more processors communicatively couples to memory; and
- one or more client device communicatively coupled to the server; wherein the one or more processors of the server executes machine readable instructions to:
- associate a code comprising a genetic identifier of a strain of a controlled item with a predetermined yield;
- associate the predetermined yield with a set of growth factors and a set of predetermined growth factor values;
- receive from a client device at least one input associated with the code;
- store the at least one inputs on the at least one memory partition; and
- perform at least one diagnostic function based on the at least one input.
10. The system of claim 8 wherein said growth factors comprises one or more of:
- a plant medium;
- a temperature;
- a humidity;
- a light intensity;
- a light duration;
- a nutrient schedule;
- a watering schedule; and
- a trimming schedule.
11. The system of claim 8 wherein said one or more processors of the server executes machine readable instructions to:
- receive from the client device data of the growth factors; and
- wherein said diagnostic function: compares the data to the growth factor values; and transmits an expected yield to the client device based upon the comparison and predetermined yield.
12. The system of claim 8 wherein said one or more processors of the server executes machine readable instructions to:
- store a set of expected yields associated with at least one growth phase on the at least one memory partition;
- receive from a client device data of actual yields associated with the at least one growth phase; and
- wherein said diagnostic function: compares the actual yields to the expected yields; and notifies an alerted device if the actual yields vary from the expected yields by an amount greater than a predetermined tolerance.
13. The system of claim 11 wherein said alerted device comprises one or more of:
- the client device;
- a law enforcement device; and
- a regulatory body device.
14. The system of claim 8 wherein said predetermined yield comprises an average of acceptable yield data from representative growth cycles of the strain.
15. The system of claim 13 wherein said one or more processors of the server executes machine readable instructions to:
- store a set of expected yields associated with at least one growth phase on the at least one memory partition;
- receive from the client device new data of actual yields associated with the at least one growth phase and data of the actual growth factor values; and
- wherein said diagnostic function: compares the actual yields to the expected yields; compares the actual growth factor values to the predetermined growth factor values; and updates the expected yield by appending the acceptable yield data with the new data of actual yields and recalculating an average if the actual yields and actual growth factor values do not vary from the expected yields and predetermined growth factor values by an amount greater than a predetermined tolerance.
Type: Application
Filed: Jan 6, 2020
Publication Date: May 7, 2020
Inventors: Aram Kovach (Westerville, OH), Garrett Greenlee (Columbus, OH), Gabriel Ronai (Szodliget)
Application Number: 16/735,636