Patent Publication Number: US-2023153693-A1

Title: Livestock and feedlot data collection and processing using uhf-band interrogation of radio frequency identification tags for feedlot arrival and risk assessment

Description:
CROSS-REFERENCE TO RELATED PATENT APPLICATION(S) 
     This patent application claims priority to, and is a continuation of, U.S. non-provisional application Ser. No. 17/364,510, filed on Jun. 30, 2021, U.S. non-provisional application Ser. No. 16/852,826, filed on Apr. 20, 2020 (now U.S. Pat. No. 11,055,633, issued on Jul. 6, 2021), and U.S. non-provisional application Ser. No. 16/569,503, filed on Sep. 12, 2019 (now U.S. Pat. No. 10,628,756, issued on Apr. 21, 2020), the contents of all of which are incorporated in their entirety herein. In accordance with 37 C.F.R. § 1.76, a claim of priority to each of these applications and patents is included in an Application Data Sheet filed concurrently herewith. 
    
    
     FIELD OF THE INVENTION 
     The present invention relates to feedlot data collection and processing. Specifically, the present invention relates to data collection using ultra-high frequency interrogation of radio frequency identification (RFID) tags, and application of machine learning techniques to discern and predict animal health issues and other conditions relative to geographical regions, feedlots, pastures, pens, and other enclosures for livestock. 
     BACKGROUND OF THE INVENTION 
     Existing technology for electronically tracking herds of livestock typically involve storing data on radio-frequency identification tags, and using scanners to interrogate and obtain data from those tags. Present scanning techniques, however, have disadvantages that limit its utility in collecting and processing livestock-related information. For example, scanning distance using low-frequency interrogation systems is on the order of centimeters, meaning that the interrogation devices must be in close proximity to the livestock and RFID tags from which data is to be collected. Further, low-frequency scanners can only scan one RFID tag at a time, do not allow for simultaneous interrogation of multiple tags in a single instance or sweep. 
     This has the practical limitation of limiting the data pipeline of collections over a large geographical area. Therefore, obtaining such information and moving it into cloud-based storage paradigms is not common practice in the livestock management industry, because the issues described above severely impact the ability to perform advanced data analytics on livestock over wider geographical areas. 
     Another problem faced by the livestock industry is a limited ability to process data collected by interrogating radio-frequency identification tags for large numbers of livestock over a wide geographical area, and analyzing such information by region, by farm, by feedlot, by pasture, by pen, or by any other such metric. In other words, the combined nature of collecting data and analyzing livestock across a wide area means that an application utilizing artificial intelligence techniques in a data mining process that folds RFID tag data with additional data sources representing weather, markets, and other relevant information, is limited by the ability to interrogate tags and obtain data needed for such analytics. 
     Solutions to the problems above are key due to increased attention on food security and traceability. Therefore, being able to track and process livestock in a combined approach that is able to quickly obtain and store data across wide distances and for multiple regions is helpful for many reasons, such as monitoring animal health, understanding and promoting improvements in livestock growth and milk production, modeling feed intake rate and inventory needs over the course of a growing season or feeding period, and enhancing food system sustainability. 
     There is therefore a need in the existing art for improvements in collecting livestock data over a wide geographical area and in the ability to analyze livestock data attributes using such data, in an approach that applies artificial intelligence techniques to predictive data analytics and which combines RFID tag data with other data to better understand and manage the many issues attendant to maintaining a livestock population. 
     BRIEF SUMMARY OF THE INVENTION 
     The present invention is an agricultural data collection framework, provided in one or more systems and methods for evaluating conditions of livestock across multiple geographical locations. The agricultural data collection framework uses ultra-high frequency interrogation of RFID tags to collect individual animal data across multiple regions, farms, feedlots, pastures, pens, and any other location or enclosure where animals are maintained, and incorporates artificial intelligence techniques to develop machine learning base models for statistical process controls around each animal for tracking and managing livestock, and for analyzing animal conditions such as health, growth, nutrition, and behavior. 
     Application of ultra-high frequency bulk reading of RFID tags enables interrogating multiple tags at the same time, and detection of a known grouping of objects such as livestock across multiple locations. Such an interrogation paradigm enables process support for applying analytical, algorithmic tools to determining normality at an individual animal basis or for a specific location, and prioritizing and delivering resources when intervention is needed in response to deviations from such a normality, due at least in part because of the greater range associated with reading RFID tags over ultra-high frequency bands. The use of UHF-band interrogation addresses temporal issues with such a large-scale collection approach, and enables advanced data analytics involving applications of artificial intelligence and machine learning in a data mining process that combines the collected livestock data with additional, relevant data sources. Such a framework, it is to be noted, is not limited to livestock populations, but is usable in any agricultural environment in which RFID tags are deployed to store information. 
     It is one objective of the present invention to provide a system and method of large-scale collection of livestock data for evaluation of animal conditions. It is another objective of the present invention to provide a system and method of applying advanced data analytics to such a large-scale collection of data. It is yet another objective of the present invention to utilize ultra-high frequency interrogation of RFID tags affixed to livestock for such a large-scale collection of data over multiple regions, feedlots, farms, pastures, pens, or other enclosures where animals are maintained in multiple geographical locations. 
     It is another objective of the present invention to augment livestock data obtained from such UHF-band interrogation of RFID tags with other data relative to the animal condition being evaluated, such as environmental data, nutrition data, regional data, animal-specific data, market data, and other producer-augmented or generated data. It is still another objective of the present invention to provide a framework for data collection and analytics that includes a determination of normality at an individual animal basis or for a specific location for animal conditions such as health, growth, nutrition, and behavior. It is yet another objective of the present invention to generate alerts, predictions, and a targeted processing or application schedule for prioritizing and delivering resources when intervention is needed based on such a determination of normality, and deviations therefrom. 
     Other objects, embodiments, features, and advantages of the present invention will become apparent from the following description of the embodiments, which illustrate, by way of example, principles of the invention. 
    
    
     
       BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS 
       The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate several embodiments of the invention and together with the description, serve to explain the principles of the invention. 
         FIG.  1    is a system diagram illustrating components in an agricultural data collection and processing framework for analyzing data attributes in livestock tracking and management according to one embodiment of the present invention; and 
         FIG.  2    is a flowchart of steps in a process of performing an agricultural data collection and processing framework for analyzing data attributes in livestock tracking and management according to one embodiment of the present invention. 
     
    
    
     DETAILED DESCRIPTION OF THE INVENTION 
     In the following description of the present invention, reference is made to the exemplary embodiments illustrating the principles of the present invention and how it is practiced. Other embodiments will be utilized to practice the present invention and structural and functional changes will be made thereto without departing from the scope of the present invention. 
     The present invention is, as noted above, an agricultural data collection and processing framework  100 , provided in one or more systems and methods for utility in precision agriculture, and specifically for livestock tracking and management. The agricultural data collection and processing framework  100  utilizes ultra-high frequency (UHF) band interrogation of RFID tags associated with livestock, and analyzes livestock tracking and management characteristics in evaluating an animal condition, and uses those characteristics to determine and predict data attributes for an allocation and prioritization of livestock-related resources over a wide geographical area for responsiveness to animal conditions. 
     UHF scanning of RFID tags provides immediate advantages over low-frequency alternatives. The scan distance is much greater, on the order of meters rather than centimeters with low-frequency scanning. Also, UHF scanning allows for simultaneous scanning of multiple tags in a single sweep, whereas other technologies can only scan one tag at a time. Further, high frequency scanners are able to penetrate deeper, for example through metals used for animal enclosures, increasing accuracy in the processing of data collected by being able to reach more RFID tags for interrogation. 
     The agricultural data collection framework  100  contemplates that many different modeling approaches may be applied in the present invention, and such approaches may also be referred to or described herein as an application of both statistical process controls for change detection algorithms, and artificial intelligence and machine learning, to combined analytics involving the collection and processing of livestock data. These different modeling approaches are used in the framework  100  to determine a normality for a specific animal or for specific location as it pertains to a modeled animal condition, and predicting or otherwise generating one or more outcomes using such a normality determination. Regardless, the present invention enables improved accuracy in predicting data attributes that impact attributes in a livestock life cycle such as health, growth and milk production. Outputs from the framework, whether in the form of predictions, alerts, or otherwise, assist in allocating and prioritization usage of resources for livestock tracking and management. Further, the present invention allows producers of livestock to ensure that animals receive the diet, nutrition, health supplements, and veterinary care needed in response to such predictions and/or allocations and prioritizations. 
       FIG.  1    is a block diagram illustrating system components of the agricultural data collection and processing framework  100  for combined analytics in analyzing an animal condition  160 , and determining and predicting data attributes for livestock tracking and management over a wide geographical area. The framework  100  applies a plurality of input data  110  to one or more mathematical processes within a multi-faceted machine learning platform. These processes may include standardized models, and may also include one or more models customized according to proprietary formulas. Regardless, the application of artificial intelligence and machine learning enables such mathematical processes to be trained to identify data that is relevant to particular attributes of an animal condition  160 , and adjust outcomes accordingly. Further, the application artificial intelligence and machine learning may enable the framework  100  to select a particular or most appropriate model or models, or combinations thereof, for specific or desired outputs. Regardless, the framework  100  generates output data  170  that includes predictions  174 , alerts  176 , or other information relevant to livestock tracking and management  172 , and may be configured to produce a wide range of information attendant to such livestock tracking and management  172 . 
     The data collection aspect of the framework  100  collects input data  110  by interrogating radio-frequency identification (RFID) tags  104 . Each head of livestock  102  has at least one RFID tag  104  coupled to it, which stores relevant information about the animal to which it is coupled. RFID tags  104  are interrogated using ultra-high frequency (UHF) scanners or readers  150 , which are part of a plurality of data processing components  144  (not shown in  FIG.  1   ) within a computing environment  140  in which the systems and methods described herein are performed for analytical processing, such as applying one or more process or mathematical models within a component(s) configured to develop machine learning base models and  162  perform statistical process controls in one or more change detection algorithms  152  on relevant input data  110 . The computing environment  140  may include one or more processors  142  and a plurality of software and hardware components, and the one or more processors  142  and plurality of software and hardware components may be configured to execute program instructions or routines to perform the functions performed within the plurality of data processing components  144 . 
     It is to be understood that the plurality of data processing components  144  are shown in  FIG.  1    by their specific, respective reference numerals as indicated below. It is to be further understood that these components  144  are part of the larger computing environment  140 , and constitute one or more structures, hardware, firmware, or software, such as algorithms, routines, sub-routines, and the like, that are specifically configured to execute particular functions within the agricultural data collection and processing framework  100 . It is to be additionally understood that the data processing components  144 , and the respective elements of the present invention that together that comprise these specifically-configured components, may interchangeably be referred to as “components,” “modules,” “algorithms” (where appropriate), and any other similar term that is intended to indicate an element for carrying out a specific data processing function. 
     The data processing components  144  also include a data retrieval and initialization module  151 , which is configured to ingest, receive, request, or otherwise obtain input data  110 , whether it be from interrogating RFID tags  104 , or from additional sources as described further herein. This data retrieval and initialization module  151  may also be configured to condition or format raw input data  110  from the RFID tags  104 , and from such additional sources, so as to be prepared for the artificial intelligence and machine learning  162  and statistical process control and change detection algorithms  152  aspects of the framework  100 . 
     In the agricultural data collection and processing framework  100  of the present invention, information obtained by the UHF readers  150  from the RFID tags  104  may also include geographical information  111 , which correlates the livestock information in a RFID tag  104  with location data. Data about livestock  102  may therefore be geo-tagged with information identifying a region  112 , a feedlot  113 , a pen  114 , a farm  115 , or any other type of enclosure or location where livestock  102  are maintained. Geographical location data  111  may be correlated with Global Positioning System (GPS) and tracking data for enhancement of the input data  110 , and therefore a RFID tag  104  may include one or more GPS data points representative of the region  112 , feedlot  113 , pen  114 , or farm  115  in which the tag  104  is located. The framework  100  may therefore utilize components such as a GPS-enabled receiver in conjunction with UHF readers  150  to detects signals relative to the geographical location to compute the tag&#39;s precise position on Earth using the one or more GPS data points. The GPS-enabled receiver may thereby extract and determine the geographical location of the tag  104  from the GPS data points. 
     UHF interrogation of RFID tags  104  may be initiated by the data retrieval or initialization component  151 , or may occur automatically and independently thereof. Regardless, data obtained as a result of this UHF interrogation is then transferred and stored by the data retrieval and initialization module  151  for further processing as discussed below. 
     The data retrieval and initialization module  151  is also configured to ingest, receive, request, or otherwise obtain additional information that aids the framework  100  in processing the input data  110  collected from RFID tags  104 , by augmenting livestock data and geographical data  111  with other data that is relevant to evaluating, modeling and diagnosing an animal condition  160 . This additional information may include environmental data  117 , regional data  120 , nutrition data  123 , regional animal-specific or model-specific data  124 , producer-augmented data  129 , and reader attributes  133 , and regardless of its type, may include any information not temporally gathered directly or on site, such as for example market pricing (such as livestock commodities data for live cattle, feeder cattle, corn, and milk future prices), disease outbreaks in other geographies, etc. It should be noted however that in some cases this additional information may be stored on the RFID tags  104 , regardless of the time or place it was gathered or generated. 
     Environmental data  117  includes both ambient climatological or meteorological information relative to where livestock  102  are maintained or where a RFID tag  104  resides, as well as spatial and other non-weather physical conditions. For example, environmental data may include weather and climate information  118 , such as temperature, precipitation, humidity, barometric pressure and other weather-related characteristics for the area or location where the RFID tag  104  resides or the livestock  102  is maintained. Weather and climate information may also include short and long term weather predictions and forecasts for that same area or location. Environmental data  117  may also, as suggested above, be parsed by location  119 , and may indicate a type and size of pen or enclosure (for example, an indication that livestock are kept in a barn or freestall, and the size of each), field and pasture conditions (for example, USDA Drought Monitor), and available grazing vegetation, where the livestock  102  are maintained. 
     Regional data  120  may further include trend and diagnosis information for the region where a RFID tag  104  resides, or where livestock  102  are maintained. Such trend and diagnosis information may provide health information and forecasts for the livestock by region which may impact, growth and behavior going forward, and which may influence growth and dairy production modeling. For example, regional data  120  may indicate that respiratory treatments within a particular data collection region are up 30% for the present quarter, or that a diagnosis of foot rot is expected to be 15% higher in the next quarter due to higher regional precipitation the last 30 days. 
     Nutritional information  123  may provide feed and forage data for a particular region or animal. For example, nutritional information may indicate that a predominant feed type specifically consists of some percentage of dry matter, or may more broadly provide a breakdown of feed nutrient percentages over time. Nutritional data  123  may also provide the mathematical formula by which weight gain allowance from energy intake is analyzed, such as for example in a net energy gain model or net energy required for maintenance model. Nutritional data  123  may also provide the mathematical formula by which milk production from energy intake is analyzed, such as for example in a net energy lactation model. Nutritional data  123  may also indicate what supplements or pharmaceuticals have been provided as part of a feed mix, and when. 
     Model or animal-specific data  124  includes information that identifies, and is particular to, an animal or group of animals, and which enables an arrival or risk assessment that can serve as a starting point where producer has entered all known information on the animal, before any processing data or additional decisions are made. Specific examples of this arrival or risk assessment information in model-specific data  124  may include an origin  125 , a changing value such as its current weight or age  126 , a gender or breed  127 , and a DNA or lineage  128 . It may also include information such as purchase weight and location, as well as a distance traveled, weaning status, vaccination status, shrink (pay weight less arrival weight), and other information that enables a robust risk assessment where a series of decision tree questions are utilized to categorize a health risk that influences other processing protocols. The arrival and risk assessment may therefore provide the agricultural data collection framework  100  with a complete animal health history. It should be noted that this arrival and risk assessment data may be procured from many sources, such as directly from an RFID tag  104  itself, from a reference database maintained or stored separately, or provided by third party sources such as another user of the framework  100  or from a third party or separate system integrated with the framework  100 . 
     The processing of input data  110  in the framework  100  may be further augmented with producer-augmented information  129  that may include many different types of data. The producer-augmented information  129  may include RFID tag-related data  130 , such as for example an identification of correlated events relative to livestock  102 . RFID tag-related data  130  may also indicate events such as new RFID tags  104  being added to the geographical location being monitored, events such as RFID tags  104  being removed from the geographical location due to tag defect or destruction or animal death, events that represent a replacement of a RFID tag  104  or assumption of a previous history with a new tag  104 , and events indicative of tag breakage or multiple tags present on the same animal. 
     Other producer-augmented information  129  may include feed-related data  131  such as feed delivery properties. Such properties may include a time of delivery, a composition of a ration, an amount of feed delivery (and an amount of each component of a ration delivered at a particular time, and a bunk score. Still other producer-augmented information  129  may include a geographical topology  132  representing the location in which a tag  104  is located. This may include a region size and other details of a coverage region, such as terrain characteristics, a presence and location of available water, field boundaries, and other relevant information. 
     Additional producer-augmented information  129  may include management information such as vaccination and treatment history, production technology use or sorting history. Further health-related management information may include confirmed diagnoses, confirmed recovery from illness, treatments used to address diagnoses and illnesses, etc. 
     The processing of input data  110  in the framework  100  may also be augmented with reader attributes  133 . These attributes  133  may include absolute or relative reader location details, antenna power settings, date and time attributes, and a tag RSSI (Receive Signal Strength Indicator). Ambient conditions sensed around the tag  104  may also be included, such as temperature and moisture, and as noted below, sensors and other hardware may be utilized in conjunction with tags  104  to provide information about such ambient conditions. 
     Input data  110  may be further augmented in another embodiment of the present invention using hardware devices that are associated with or proximate to RFID tags  104 . For example, an inclinometer may be utilized to measure an angle of inclination of livestock at various times of a day, for example when presumed to be feeding, to further and more accurately evaluate characteristics such as head down duration and eating rate, as well as to more accurately determine feeding and non-feeding times and intervals where univariate or multi-variate models of such characteristics are applied. It is therefore to be understood that the present invention may incorporate input data  110  that come from not only from third party sources, but also sensors and other hardware devices than may be utilized in conjunction with livestock  102 . 
     Regardless of the type of input data  110  that is ingested to augment data from RFID tags  104 , the data retrieval and initialization component  151  provides the information to an artificial intelligence engine, which is configured to develop one or more machine learning base models  162  of one or more characteristics impacting the animal condition  160 . The one or more machine learning base models  162  include algorithms that identify additional information from the input data  110  and obtain such additional information from one or more sources as noted above. 
     The machine learning base models  162  then assign weights  164  to the input data  110 . These weights  164  represent biases in the input data  110  relative to the animal condition  160 , and may be assigned based on multiple variables or factors, such as for example a prior response or responses to an animal condition  160  being modeled, either in the form of a specific treatment or within a geographical location similar to that within which the animal condition  160  is being modeled. Regardless, the weights  164  are aggregated to generate a weighted vector of learning data  166  that is provided to the statistical process controls in the change detection algorithms performed by the component  152 . 
     The statistical process controls and change detection algorithms  152  apply one or more mathematical processes to the output of the machine learning base models  162  to evaluate the animal condition  160  and generate a corresponding profile. These mathematical processes are applied to perform change detection, at least by identifying tracking and management characteristics  172  of the livestock  102  and a normality  156  of the animal condition  160 . These mathematical processes at least include a statistical analysis  153 , a sequential analysis  154 , and a cumulative summation (CUSUM model)  155 . Regardless of the mathematical process or model used to evaluate the input data  110  and the weighted vector of learning data  166 , they may be derived from existing, standardized models, and may also include models that are customized to incorporate unique characteristics based upon the input data  110  discussed above and the specific animal condition  160  being profiled. 
     The resultant profile of the animal condition  160  is then applied across the multiple geographical locations in which the one or more animals are located to determine a normality  156  relative to a specific animal in the one or more animals, and identify differences in the animal condition  160  for a specific geographical area. The framework  100  is therefore configured to develop statistical process controls and perform change detection analyses around each animal for a determination of normal at an individual animal basis, so that opportunities for intervention where the artificial intelligence engine identifies deviations from such normality determinations can be quickly performed. The present invention may therefore be understood to be, in one aspect thereof, a framework  100  for evaluating animal health that tries to identify healthy animals and healthy conditions rather than sick or unhealthy ones, so that conditions outside of normal parameters can be classified as such and diagnosis, treatment, and prevention proceeds from there as a starting point. 
     The profile of the animal condition  160 , and livestock tracking and management characteristics  172  therein, may be generated as output data  170  as discussed further below, and may also be provided back to the machine learning base models  162  and used to adjust and/or train a base model  168 . The framework  100  therefore “learns” from outcomes of the statistical process control and change detection algorithms  152  to improve the weights and correlations  164  assigned to the input data  110 , and the corresponding weighted vector of learning data  166  for each animal condition  160  modeled. Therefore, the framework  100  incorporates a feedback loop in the form of adjustments to the base model  168  that enables validation of the statistical process control and change detection algorithms  152  and the predictions  174  and alerts  176  generated therefrom as output data  170 . 
     The output data  170  includes, as noted above, predictions  174  and alerts  176  that are the result of livestock tracking and management characteristics  172  in the profile of the animal condition  160 . The livestock tracking and management characteristics  172 , predictions  174  and alerts  176  may be provided to users via a display, such as a graphical user interface, interactive or otherwise, for example via a support tool or other mechanism. 
     Many manifestations of the livestock tracking and management characteristics  172 , predictions  174  and alerts  176  in the output data  170  are contemplated and within the scope of the present invention. In one aspect of the present invention, the output data  170  may be used to develop and application schedule  180  for delivery of a response in an intervention to deviations from the normality  156  as discussed above, and to allocate and prioritize resource usage  181  for such a response. Output data  170  may also include specific information derived from the livestock tracking and management characteristics  172 , predictions  174  and alerts  176 , such as for example a pre-diagnosis of health issues  182 , identification of disease trends  183 , peak livestock weights  184 , behavioral patterns  185  (for example, grazing behavior suggestive of inadequate pasture), and indications of specific health events  186 , such as calving  187 , estrus  188 , and injury  189 . The output data  170  may further be processed to identify environmental interactions  190  that affect other livestock models, such as growth models and dairy production models. 
     Many other services and outcomes are possible, and may be provided either by directly by the framework  100  itself, or through one or more application programming interfaces (APIs). For example, the framework  100  may include modules configured to generate predictions  174  and alerts  176  to marketing organizations interested in when cattle will come of weight in the future, manufacturers of particular feed components interested in when medicine, additives and supplements should be re-ordered, nutritionists and veterinary visits scheduled, and buyers or auctioneers of livestock notified. It is to be understood that many types of predictions  174  and alerts  176  are possible within the present invention, and it is not to be limited to any one type of prediction  174  or alert  176  mentioned herein. The present invention may, as suggested above, also enable one or more additional and specific APIs to provide particular information or services and generate specific outcomes from the output data  170  and the livestock tracking and management characteristics  172 , predictions  174  and alerts  176  that are generated from the framework  100 . 
     In one example where the framework  100  of the present invention may be applied, UHF readers of scanners  150  are deployed across a feedlot  113  that includes one or more pens  114 , alleys, loading/unloading areas, and other places where livestock  102  may be located. Deployment locations for the UHF readers/scanners  150  may include all regions of the feedlot  113 , so that no areas are explicitly excluded. This includes areas with “attractants” such as water and feed sources, as well as areas without (for example, holding pens may be provided with water or food). The enables input data  110  for animals in geographic areas marked as being without attractants during feeding times to also be data points of interest for the machine learning base models  162 . 
     RFID tags  104  are read in real time by the UHF reader devices  150 . Input data  110  collected from the tags  104  may be provided to an aggregated storage mechanism, such as a relational database, along with reader attributes  133  and any other relevant data points collected from the feedlot  113 . Such input data  110  may be directly presented to the aggregated storage mechanism from the readers  150  using network, cellular, Wi-Fi, Bluetooth, or other comparable communications network. Alternatively, input data  110  may be presented to the aggregated storage mechanism through the use of data pass-through devices, which are devices which collect data from the readers  150  and act as a liaison to pass the data to the aggregated storage mechanism. Examples of pass-through devices include tablets, cellular devices, a point (“smart”) scale head or other device capable of collecting data from the readers  150  and passing information to the storage mechanism using an IP network such as Wi-Fi, or serial communications protocol such as Bluetooth, NFC (near-field communication), or the like. Pass-through devices may include “smart” phones or other computing devices, and may be transitory devices mounted to trucks, tractors, other agricultural implements, manned or unmanned, as well as to manned or unmanned flight vehicles. Regardless, in such an example input data  110  is pooled and combined with optional on-demand sources of additional information for the artificial intelligence engine in the one or more machine learning base models  162 , and for the statistical process controls and change detection algorithms  152 . 
     In an exemplary approach, the input data  110  for evaluating a particular animal condition  160  may include DNA (genetic history) and lineage (origin) of livestock  128 , and any treatment histories derived therefrom; water tank data (such as frequency and duration of water consumption), feed bunk data (such as frequency and duration of feed ration consumption). The one or more machine learning base models  162  takes these inputs develops correlations and weights  164  to generate the weighted vector space of learning data  166  based on any actual, historical producer-specified treatments provided for the animal condition  160 . This learning process is followed by a real-time predictive analysis performed by the statistical process controls and change detection algorithms  152  to identify livestock tracking and management characteristics  172  where there is a possible deviation from a normality  156  for a particular geographical location (such as the feedlot  113 ) to identify sick livestock before they show any visual signs of stress or illness or otherwise become in need of treatment. The set of input data  110  may be further augmented with data outside of the producer&#39;s feedlot  113 , such as with nutritional ration data, weather data, treatments at other locations in a supply chain, for example cow/calf, backgrounder or stocker operations, or a calf ranch or heifer raiser, brands of treatments (generic versus commercial), etc. 
     The learning data based on the actual producer treatment data is used in real time to predict animals with behaviors that would also lead to producer treatments of the current livestock  102 . These animals would be identified for the producer to do a “pre-check” health determination, allowing the producer to possibly prevent further outbreak or animal death. 
       FIG.  2    is a flowchart illustrating a process  200  for performing the framework  100  of the present invention. The process  200  begins at step  210  by interrogating RFID tags  104  as noted above with readers  150  utilizing a high-frequency communications band (UHF) to begin the processing of onboarding input data  110  relative to evaluating an animal condition  160 . This information is initially processed to determine what additional information may be requested and obtained to perform the various processing steps for evaluating the animal condition  160  at step  220 . 
     Detailed processing of the input data  110  in the framework  100  then begins at step  230 , with the development of machine learning base models  162  in the artificial intelligence component of the present invention. The models  162  evaluate the input data  110  and additional information for the animal condition  160  at issue (or for a particular geographical location) and identify biases and correlations between one or more variables which are used to assign weights  164 , at step  240 . These weighted  164  variables are used to compile a vector space of weighted learning data  166 . 
     At step  250 , the weighted vector data set  166  is applied to change detection algorithms  152  for statistical process, using as noted above one or more mathematical processes to identify a deviation from normality  156  for the animal condition  160 . At step  260 , the framework  100  and process  200  generate a profile of the animal condition  160  and tracking and management characteristics  172  relative the animal condition  160 . The process then filters and identifies data attributes for a particular animal condition  160  and for one or more geographical locations at step  270 , and in one aspect of the present invention generates a targeted application schedule  180  of resources based on the profile to address the animal condition  160  at step  280 . As noted above, this may include an allocation and prioritization of resources, and may further be present on a display for a user or producer to take further specific action. 
     Returning to  FIG.  1   , as noted above, the framework  100  for developing livestock tracking and management characteristics  172  for analyzing an animal condition  160  is a multi-faceted approach that performs, in one aspect thereof, different mathematical processes for evaluating change detection to determine a normality  156  and predict any deviations therefrom. These mathematical processes include a statistical analysis  153 , a sequential analysis  154  (a specific type of statistical analysis), and a cumulative sum analysis (a specific type of sequential analysis). The selection of the process to be utilized depends on the type of animal condition  160  being modeled, and on the types of input data  110 . And, as noted above, a particular process may be customized depending on similar characteristics (the type of animal condition  160 , and the type of input data  110 ). 
     For example, the present invention may evaluate an animal condition  160  such as the effectiveness and accuracy of monitoring feeding behavior patterns, which may be utilized to predict the onset of health issues such as bovine respiratory disease in beef cattle. The framework  100  may apply one or more cumulative summation (CUSUM) models  155  that are each configured to evaluate univariate traits as they pertain to feeding, such as for example bunk visit frequency, bunk visit duration, head down duration, eating rate, time to bunk, and non-feeding intervals, or any other feeding-related characteristic. It is to be understood that these characteristics may be obtained or derived from input data  110 , such as producer-augmented information  129 , within the framework  100 , and may not necessarily be obtained directly from RFID tags  104 . 
     Outcomes from these models may be used to construct multivariate factors that are also monitored using CUSUM. From these constructs, accuracy may be selected based on the weighted vector of learning data  166  for the most pertinent and accurate predictive analysis. In this manner, a statistical process control can be implemented for evaluating change detection within the framework  100  for an appropriate allocation and prioritization of resources to address animal conditions  160 . 
     Other uses of the output data  170  in the present invention are also possible, and within the scope of the present invention. In one embodiment, the output data  170  may be used to address specificity and sensitivity tolerances. In one example, the framework  100  may be used to match an operator labor resources or animal illness risk by adjusting output sensitivity and specificity to minimize candidate animals identified for treatment. In another example, the output data  170  specificity and sensitivity may be adjusted to inform a greater number of candidate animals due to high risk periods as determined environmental  117  or regional data  120  trends. 
     It is to be understood that the word “livestock” in the present invention may refer to any type of livestock  102  for which tracking and management characteristics  172  in analysis of an animal condition  160  may be developed, and the scope of this disclosure is not to be limited to any one specific type of livestock  102  referred to herein, nor is it likewise to be limited to one condition for any one type of livestock referred to herein. Livestock  102  may therefore include, but not be limited in any way to, beef cattle, dairy cattle, hogs, poultry, sheep, goats, bison, horses, etc. The present invention is therefore applicable to all types of livestock  102 , and the modeling approach discussed herein may be adjusted depending on the type of livestock  102  being modeled. 
     The systems and methods of the present invention may be implemented in many different computing environments  140 . For example, the statistical process control and change detection algorithms  152  may be implemented in conjunction with a special purpose computer, a programmed microprocessor or microcontroller and peripheral integrated circuit element(s), an ASIC or other integrated circuit, a digital signal processor, electronic or logic circuitry such as discrete element circuit, a programmable logic device or gate array such as a PLD, PLA, FPGA, PAL, and any comparable means. In general, any means of implementing the methodology illustrated herein can be used to implement the various aspects of the present invention. Exemplary hardware that can be used for the present invention includes computers, handheld devices, telephones (e.g., cellular, Internet enabled, digital, analog, hybrids, and others), and other such hardware. Some of these devices include processors (e.g., a single or multiple microprocessors), memory, nonvolatile storage, input devices, and output devices. Furthermore, alternative software implementations including, but not limited to, distributed processing, parallel processing, or virtual machine processing can also be configured to perform the methods described herein. 
     The systems and methods of the present invention may also be partially implemented in software that can be stored on a storage medium, executed on programmed general-purpose computer with the cooperation of a controller and memory, a special purpose computer, a microprocessor, or the like. In these instances, the systems and methods of this invention can be implemented as a program embedded on personal computer such as an applet, JAVA® or CGI script, as a resource residing on a server or computer workstation, as a routine embedded in a dedicated measurement system, system component, or the like. The system can also be implemented by physically incorporating the system and/or method into a software and/or hardware system. 
     Additionally, the data processing functions disclosed herein may be performed by one or more program instructions stored in or executed by such memory, and further may be performed by one or more modules configured to carry out those program instructions. Modules are intended to refer to any known or later developed hardware, software, firmware, artificial intelligence, fuzzy logic, expert system or combination of hardware and software that is capable of performing the data processing functionality described herein. 
     The foregoing descriptions of embodiments of the present invention have been presented for the purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Accordingly, many alterations, modifications and variations are possible in light of the above teachings, may be made by those having ordinary skill in the art without departing from the spirit and scope of the invention. For example, the input data  110  may be augmented with data collected hardware devices in association with or proximate to a RFID tag, such as an inclinometer. It is therefore intended that the scope of the invention be limited not by this detailed description. For example, notwithstanding the fact that the elements of a claim are set forth below in a certain combination, it must be expressly understood that the invention includes other combinations of fewer, more or different elements, which are disclosed in above even when not initially claimed in such combinations. 
     The words used in this specification to describe the invention and its various embodiments are to be understood not only in the sense of their commonly defined meanings, but to include by special definition in this specification structure, material or acts beyond the scope of the commonly defined meanings. Thus if an element can be understood in the context of this specification as including more than one meaning, then its use in a claim must be understood as being generic to all possible meanings supported by the specification and by the word itself. 
     The definitions of the words or elements of the following claims are, therefore, defined in this specification to include not only the combination of elements which are literally set forth, but all equivalent structure, material or acts for performing substantially the same function in substantially the same way to obtain substantially the same result. In this sense it is therefore contemplated that an equivalent substitution of two or more elements may be made for any one of the elements in the claims below or that a single element may be substituted for two or more elements in a claim. Although elements may be described above as acting in certain combinations and even initially claimed as such, it is to be expressly understood that one or more elements from a claimed combination can in some cases be excised from the combination and that the claimed combination may be directed to a sub-combination or variation of a sub-combination. 
     Insubstantial changes from the claimed subject matter as viewed by a person with ordinary skill in the art, now known or later devised, are expressly contemplated as being equivalently within the scope of the claims. Therefore, obvious substitutions now or later known to one with ordinary skill in the art are defined to be within the scope of the defined elements. 
     The claims are thus to be understood to include what is specifically illustrated and described above, what is conceptually equivalent, what can be obviously substituted and also what essentially incorporates the essential idea of the invention.