Patent ID: 12217833

DETAILED DESCRIPTION

The following detailed description refers to the accompanying drawings. Whenever possible, the same reference numbers are used in the drawings and the following description to refer to the same or similar elements. While disclosed embodiments may be described, modifications, adaptations, and other implementations are possible. For example, substitutions, additions, or modifications may be made to the elements illustrated in the drawings, and the methods described herein may be modified by substituting reordering or adding additional stages or components to the disclosed methods and devices. Accordingly, the following detailed description does not limit the disclosed embodiments. Instead, the proper scope of the disclosed embodiments is defined by the appended claims.

The disclosed embodiments improve upon the problems with the prior art by providing a system and method designed to efficiently enhance muscle protein level synthesis in a protein sample. The system uses an efficient algorithm designed to perform precise analysis and optimization of the amino acid profile in the protein sample. This method efficiently improves muscle protein synthesis by accurately determining the weights of amino acids within the sample. The algorithm facilitates targeted adjustments in the amino acid composition, thereby enabling more effective muscle protein synthesis compared to conventional techniques.

The disclosed embodiments improve upon the problems with the prior art by performing analysis based on arranging the amino acids in the protein sample in a decreasing order based on their values by weight, from greatest to least. This ordering places the amino acids with the highest values at the top of the list, followed sequentially by those with lower values. The disclosed embodiments emphasizes those at the bottom of the order. By focusing on and optimizing the values of these lesser-valued amino acids, the current method enhances the overall effectiveness of the protein sample, resulting in a product with improved nutritional properties. This strategic enhancement allows for the tailored improvement of specific amino acids that are often overlooked, thereby broadening the potential applications of the protein in various dietary and therapeutic contexts. Furthermore, this approach ensures a more balanced amino acid profile, which is crucial for achieving optimal health benefits and functional outcomes from protein consumption.

The disclosed embodiments improve upon the prior art by precisely calculating the values of amino acids towards the bottom of the order, determining the adjustments needed to achieve an optimized amino acid profile. This accurate calculation is crucial in enhancing the overall protein quality, focusing on improving those amino acids that are typically less concentrated yet vital for certain functionalities. The precision of this approach not only ensures an efficient optimization process but also significantly increases the nutritional and therapeutic efficacy of the protein product.

Further, the disclosed embodiments provide a graphical representation as a visual indication that illustrates the improved and optimized amino acid profile of the protein sample alongside its profile before optimization. This graphical representation allows for a clear and immediate visualization of the enhancements in amino acid values. The comparative layout not only highlights the specific increases in lesser-valued amino acids but also underscores the overall enhancement of the protein's nutritional profile. This visual clarity facilitates easier interpretation and validation of the optimization process, facilitating better evaluation and decision-making for further developmental strategies.

Additionally, the disclosed embodiments utilize an efficient algorithm that quickly and accurately determines the value of the amino acids for the adjusted amino acid profile. This robust algorithm is designed to deliver precise results across a variety of protein samples, ensuring reliable outcomes in the optimization process. Its capability to quickly assess and modify amino acid values not only streamlines the enhancement procedure but also maintains high accuracy, making it a vital tool for improving protein formulations in diverse applications, from nutritional supplements to therapeutic products.

Referring now to the Figures,FIG.1illustrates an exemplary environment for a system100designed to implement a method for increasing muscle protein synthesis (MPS) relative to other nitrogenous processes. System100includes a combination of hardware and software. In some embodiments, the various methods described herein are implemented at least partially by hardware of one or more computing devices, such as one or more hardware processors executing instructions stored in one or more memories for performing various functions described herein. For example, descriptions of various components (or modules) as described in this application may be interpreted by one of skill in the art as providing pseudocode, an informal high-level description of one or more computer structures. The descriptions of the components may be converted into software code, including code executable by an electronic processor. System100illustrates only one of many possible arrangements of components configured to perform the functionality described herein. Other arrangements may include fewer or different components, and the division of work between the components may vary depending on the arrangement.

The depicted system comprises a first user115and a second user118, who interact with the system through various devices connected to a network106. In the context of the disclosed embodiments, the first user is identified as the manufacturer or seller of the protein product. This user is primarily responsible for the creation, optimization, and distribution of protein supplements or food products. The second user is the consumer or purchaser of the protein product. This user benefits directly from the optimized protein formulations, experiencing improved nutritional outcomes as a result of consuming products. Network106may include one or more packet switched networks, such as the Internet, or any local area networks, wide area networks, enterprise private networks, cellular networks, phone networks, mobile communications networks, or any combination thereof. Network106communicatively couples each component of system100and may utilize known security precautions such as encryption, passwords, limited Wi-Fi range, and the like.

The first user115utilizes first user device, such as a mobile device112or a laptop114, and the second user118employs second user devices including a laptop120to access the system. The first user device, such as the laptop114, in one example, belongs to a manufacturer or a seller of a product that may include protein sample or a protein powder, and the second user belongs to a buyer of the product. In other examples, the product may include cow's milk, peanuts, whey protein derived from milk, casein protein also from milk, soy protein, pea protein, another plant-based option rich in branched-chain amino acids and hypoallergenic, hemp protein that provides fiber and essential fatty acids and mixed plant proteins, which blend sources like pea, rice, hemp, and quinoa to offer a balanced amino acid profile and enhanced nutritional value.

The first and second users input specific data related to the protein products they are manufacturing, selling or consuming, utilizing a dynamically generated user interface on their device112. Generally speaking, the first user device and the second user device are one or more of remote computing devices, such as a tablet computer, a smartphone, and a laptop computer. The mobile device112may encompass a variety of handheld or portable gadgets such as smartphones, tablets, or PDAs that offer flexibility and mobility for accessing the system in various contexts, such as in-field or remote environments. The laptop114represents a personal computer designed for portability and capable of handling more complex computational tasks. These devices are equipped with software and hardware that enable the user to obtain, process, and send data back to the server for further analysis. The devices may further include non-remote computing devices such as desktop computers and servers. Central to the system is a server environment108, which houses a server102and a database104. The server102and database104are interconnected and provide the computational and data storage capabilities necessary to execute the method. The server102is typically a powerful computer or cluster of computers designed to manage network resources and handle heavy processing tasks. Server102may be in communication with database104. The database may store a variety of data, including data associated with any of the aforementioned communications. The database may permanently, or transiently store all or portion(s) of the data included in the aforementioned communications. In one embodiment, database104may be a relational database comprising a Structured Query Language (SQL) database stored in a SQL server, and may be distributed over one or more nodes or locations that are connected via network106. Database104may accumulate data from the transactions that occur on system100. The data from the transactions may be used, for instance, to train machine learned algorithms. For instance, machine learned algorithms for authenticating data may be produced from accumulated data.

Such exemplary communications described above may be sent via an appropriate protocol, such as via Hyper Text Transfer Protocol (“HTTP”) and/or Hypertext Transfer Protocol Secure (“HTTPS”). Other transfer protocols may be used and are within the spirit and scope of the invention.

The described system and method improves muscle protein synthesis (MPS) relative to other nitrogenous processes by employing a sophisticated computational approach. In the context of nitrogenous processes within a protein sample, amino acids play multifaceted roles. Beyond their collective contribution to protein synthesis, individual amino acids exhibit distinct functions when isolated. Consider glycine, an essential amino acid. When a user faces starvation and consumes glycine powder, remarkable protective effects ensue. Specifically, the glycine powder acts as a safeguard against muscle wasting, preserving lean tissue even in the dire conditions of malnutrition. Furthermore, its benefits extend beyond muscle preservation and shields the user's entire body from harmful consequences of prolonged starvation. The second user device obtains an amino acid profile for a protein sample, which includes various amino acids listed by their respective gram values in descending order. This profile is then stored in the connected database104. The process advances by executing a program on the server, which is part of a communications network. This program accesses the amino acid profile and follows instructions stored within the database to generate an adjusted amino acid profile for the protein product. The details of the working of the system and each component to execute the method and generating the adjusted amino acid profile are discussed in more detail in the subsequent figure descriptions.

With reference toFIGS.1A,2,2A,2B,3A,4A and4B, various communications may be sent to and from the entities shown in system100using server102. For instance, and with reference to the figures now includingFIG.1A, the server102may send an interface message122, where the operator interface message122comprises an operator interface or graphical user interface related data113and116. The data113and116may include graphical interface data for displaying a graphical user interface including layout information, images, styles, and commands that provide the appearance and functionality of the user interface. The interface message may be configured to display an interface configured to receive input, at the direction of the first user, which may include a graphical window with tabs to receive input from the first user. Additionally, the interface may include alpha numeric characters, audio content, and visual content, among others.FIG.2depicts a first user device having a GUI200, which is a mobile device displaying a graphical user interface (GUI) designed to receive and process inputs related to a protein sample. For example, a protein sample as shown inFIG.3Ahaving amino acids shown inFIG.3B. The interface displays a window for receiving inputs regarding an amino acid profile. On left portion of the screen, in section202of the interface, a dropdown menu labeled “Protein Sample” allows the user to select a specific protein sample from a predefined list. This dropdown is designed to facilitate easy selection and ensure that the correct protein sample is being analyzed. The section202further includes a section labeled “Amino Acids,” which contains input fields for specifying the types displayed as graphical element “Name” in the selected protein sample. On the right portion of the interface, the sections204and206comprise another column for “Grams.” For entering weight or values of each amino acid of the protein sample. Each row within these columns allows the user to input the name of an amino acid and the corresponding amount in grams. The dropdown menus in the “Name” column provide a list of amino acids, while the text fields in the “Grams” column are intended for numerical input. To accommodate additional amino acids, the interface includes an “+Add more” graphical button210, which, when pressed, dynamically adds more rows to the input section, allowing the user to input more amino acid data without limitations on the number of entries.

At the bottom of the interface, two action graphical buttons212labeled “Save” and “Submit” are displayed. The “Save” graphical button is intended to save the current state of the input data locally on the device, enabling the user to review or edit the information later. The “Submit” graphical button is designed to send the finalized data to a server for further processing, such as analysis or storage. The graphical user interface as described operates within the system to provide an efficient and user-friendly method for inputting detailed protein sample information, ensuring accuracy and completeness. The materials involved in this interface include standard GUI components like dropdown menus, text fields, and buttons, all implemented using software technologies such as HTML, CSS, and JavaScript.

Referring toFIG.1A, following the entry of the required data, the first user transmits a data packet125back to the server, which includes key data elements117and119pertinent to the protein sample or powder they are managing. The data elements117and119comprise the names and types of amino acids in the protein sample, and weight values of each amino acid, respectively.FIGS.4A and4Billustrate a flowchart of a method for analyzing and adjusting the amino acid profile of a protein sample. The process begins with obtaining an amino acid profile, as indicated in step404. In this initial step, the amino acid profile of the protein sample is collected, encompassing a plurality of amino acids, each measured and recorded in terms of their specific values. This foundational data serves as the basis for subsequent analysis and adjustments. It is noted that the information regarding the amino acid profile is provided to the server by the first user, in other embodiments, the server may fetch the amino acid profile information from information and data available online.

Referring toFIG.1A, in an alternative embodiment tailored to enhance user engagement and customization, the system extends its capabilities to incorporate a second user device, such as the laptop120. This device is linked to a consumer or the second user118who may express interest in modifying the amino acid profile of the product. Addressing this need, the server dispatches an additional interface message130to the second user device having a data element136corresponding to interface elements, which activates the generation of a graphical user interface tailored to the consumer's specifications. The GUI on the second user device is similar to the GUI200ofFIG.2. Through this interface, the consumer meticulously inputs specific details, which are then encapsulated in a data message134sent back to the server containing data elements138.

Upon reception of the aforementioned data from the first user device, the server initiates a sequence of processes wherein the data is methodically processed. This involves the generation of a data record, which is then accurately stored within a database104. This may include a record ID, the details of the product such as name of the manufacturer of the product, phone, address. This database not only serves as a storage repository but also plays a critical role in managing the data for subsequent retrieval and processing, thereby supporting the system's core functionality. In accordance with the disclosed embodiments, the server plays a pivotal role in processing the data received from both the first and second users, who may be manufacturers, sellers, or consumers of a protein product. Upon receipt of the data, the server is tasked with generating an amino acid profile for the protein product. This profile is meticulously detailed, listing all constituent amino acids present in the protein product, as exemplified inFIG.3C. Each amino acid is quantified, with values expressed per 100 grams of the product, providing a clear and measurable indication of the protein's composition. This amino acid profile is not merely generated for informational purposes but is integral to the creation of a comprehensive record for each protein product. The server stores this detailed profile within the record, ensuring that all relevant data concerning the protein's composition is maintained in an organized and retrievable format. This capability is crucial for ongoing quality control, regulatory compliance, and enhancement of product formulations.

Further enhancing the system's capabilities, the server is equipped with a specific algorithm saved within its system architecture. This algorithm is utilized to process the amino acid profile stored in the record, thereby generating an adjusted amino acid profile. The algorithm plots the amino acids on a two-dimensional graph, identifying the final five data points to establish a slope regression tail.

Thereafter, the server may further process the data received. Referring toFIG.4A, in step406, the obtained amino acids are plotted on a two-dimensional graph in descending order based on their respective values, as shown inFIGS.5A,5C,5E. This graphical representation allows for a clear visualization of the distribution and relative abundance of each amino acid within the protein sample. By arranging the amino acids in descending order, it becomes easier to identify patterns and trends that are critical for further analysis. Referring toFIG.1A, the after plotting on the two-dimensional graph, the server transmits the graphical representation via the message127to the first user device for displaying the graph on the user device. The message127comprises a data element121corresponding to the values of x-axis and y-axis and corresponding amino acids of the protein sample.FIG.2Aillustrates the graphical representation displayed on the user device based on the amino acid of the protein sample.FIG.2Aillustrates a graphical user interface (GUI)222displayed on the user device, which visualizes the two-dimensional graph224corresponding to the amino acid profile of a protein sample. The GUI includes several interactive elements such as a section labeled “Amino Acid Profile,” which displays the profile of amino acids present in the selected protein sample. Another section, “Protein Sample,” allows users to select the type of protein sample being analyzed, with “Peanuts” chosen in this instance. Additionally, there is an “Initial Slope Value” section230that likely provides the initial slope value derived from the displayed graph. The graph itself features a Y-axis labeled “AA g/100 g protein,” indicating the concentration of amino acids per 100 grams of protein. The X-axis is labeled “AA's of Peanuts,” representing the various amino acids found in peanuts. Data points are plotted as dots along the graph, with a trendline illustrating the general trend of these data points. Error bars are included to show the variability or error in the data points. This GUI allows users to analyze and interpret the amino acid composition of selected protein samples, with specific emphasis on peanuts in this example.

In addition to sending the graphical representation to the user device, the server may further processes the data. Further processing of the data by the server is depicted in step408ofFIG.4A, where an initial slope value is calculated for each of the final five data points on the graph. This calculation uses the formula M=(Y2−Y1)/(X2−X1), where Y represents the values of the amino acids and X represents their positions in the descending order. The slope values provide insights into the rate of change in amino acid values across the final data points, which is essential for understanding the dynamics of the amino acid distribution. Step410involves adjusting the amino acid profile algorithmically to generate an adjusted amino acid profile. The algorithm considers the initial slope values and other relevant factors to refine the profile. This step aims to optimize the amino acid composition for specific criteria, such as enhancing nutritional value or improving functional properties. The use of an algorithm ensures that the adjustments are precise and based on rigorous computational analysis.

In step412, the algorithm calculates the percent difference for each of the final five data points. This involves comparing the highest initial slope value with each of the final five data points' slope values. The percent differences highlight deviations and variations among the data points, which are crucial for fine-tuning the adjusted amino acid profile. This step ensures that the final profile meets the desired specifications and standards. Further, the method includes multiplying the percent difference by each respective initial slope value, as indicated in step414. This step is critical as it refines the adjustments based on the variations identified in the initial slope values. By multiplying the percent difference by the initial slope value, the process generates a product that reflects the adjusted value for each of the final five data points, ensuring precise modifications to the amino acid profile. Following this, step416involves adding the product back to the original value of the respective amino acid. This addition incorporates the adjustments into the amino acid profile, effectively refining the overall composition. The adjusted values represent a more accurate and optimized profile for the protein sample, which is crucial for achieving the desired nutritional and functional characteristics.

In the step418, introduces a correction score for the lowest three amino acids in the profile. Specifically, a correction score of 1%, 2%, and 3% is added respectively to these amino acids. This step ensures that even the amino acids present in lower concentrations are adequately adjusted, contributing to a more balanced and comprehensive amino acid profile. The application of these correction scores further fine-tunes the profile, making it more robust and tailored to specific requirements. Each of these points has an initial slope value calculated using a specific formula. Subsequent algorithmic adjustments refine the amino acid profile based on the percent difference between the highest slope value and each of the final five data points, adjusting their weights accordingly. The lowest three amino acids in the slope regression tail receive an incremental correction score of 1%, 2%, and 3%, respectively. This computational method enhances the accuracy and effectiveness of the amino acid profile, potentially optimizing the protein's capacity for muscle protein synthesis, thereby presenting a substantial improvement over existing methods by tailoring protein profiles more closely to biological needs. Collectively, the steps outlined inFIGS.4A and4Benhance the precision and effectiveness of the amino acid profile adjustment process. By systematically applying calculated corrections and adjustments, the method ensures that the resulting amino acid profile is finely tuned to meet specific nutritional and functional criteria. This comprehensive approach is essential for developing high-quality protein products with optimized amino acid compositions, ultimately improving their applicability in various health, nutrition, and biotechnological applications.

The adjustments made by this algorithm are based on predefined criteria aimed at optimizing the nutritional value of the protein, enhancing its functional properties in food products, or tailoring the amino acid balance to meet specific dietary needs or consumer preferences. The ability to dynamically generate and adjust amino acid profiles based on algorithm-driven insights positions this system as a significant improvement over prior art. It provides a dynamic, responsive approach to protein product management, facilitating enhanced product development and optimization that can directly influence market competitiveness and consumer satisfaction. This system, therefore, not only supports the operational needs of protein manufacturers and sellers but also serves as a critical tool in advancing the science of protein engineering and nutrition. In another example, after adjusting the amino acid profile, the server generates another graphical representation of the adjusted amino acid profiles, shown inFIGS.5B,5D,5Eon the two dimensional graph and transmits a message to the first user device for displaying a GUI showing the graph.FIG.2Bdepicts a graphical user interface (GUI)227on a user device, showcasing a two dimensional graph226that corresponds to the adjusted amino acid profile of a protein sample. The GUI displays the amino acid profile, of the protein sample which is peanuts. The second element is labeled “Adjusted Slope Value,” indicating a recalibrated slope value232derived from the displayed amino acid data corresponding to the adjusted amino acid profile. The two dimensional graph features a Y-axis labeled “g/100 g+Peanut,” signifying the quantity of amino acids per 100 grams of protein, adjusted for peanuts. The X-axis is labeled “Peanuts AAs,” representing the specific amino acids found in peanuts. Data points are plotted as dots along the graph, with a trendline showing the overall trend of these data points. Error bars are included to reflect variability or error in the data measurements. This GUI is designed to allow users to easily and analyze and interpret the amino acid composition of the protein sample with a specific focus on peanuts, facilitating a deeper understanding of the protein's characteristics and variability.

In another embodiment, after the calculation is performed by the server using the algorithm, the algorithm outputs the amino acids and their values to be added to the protein sample, and sends a display message to the first user device to be displayed on the GUI235.FIG.2Cillustrates the graphical user interface (GUI)235displayed on a first user device. This GUI features several interactive elements designed to facilitate the user's interaction with amino acid profile data. The GUI displays a table236, which shows the different values of the amino acids to be added based on the algorithmic calculation. The table lists the amino acids along with their respective values that are based on differences between the values in the original amino acid profile and the adjusted amino acid profile, which are as follows: Alanine at 0.04, Methionine* at 0.46, Proline at 0.64, and Cysteine at 1.09. Additionally, the table includes the “Total Added (g/100 g),”238which aggregates the values of the amino acids to a total of 2.23 grams (approx.).

The process of determining the total added value is based on determining the difference between the amino acid values ofFIGS.6A and6Band aggregating the differences. For example, the adjusted amino acid profile for Alanine in peanuts is noted at 2.82 grams inFIG.6Bas opposed to the 2.78 grams in the unmodified profile ofFIG.6A, reflecting a net increase of 0.04 grams. The concentration of Methionine increases from 2.34 to 2.80 grams, providing a differential of 0.46 grams. In other amino acids, increments are observed with Proline from 2.12 to 2.76 and Cysteine from 1.65 to 2.74, where the increases are recorded at 0.64 and 1.09 grams, respectively, summing to the total enhanced weight of 2.23 grams. In light of the updated information regarding the values of amino acids to be added, as depicted on the user device, users and/or manufacturers are provided with direct and precise information concerning the specific weights of these amino acids required to be added to the protein sample. This essential data facilitates the optimization of the amino acid profile of the protein sample, ensuring that the composition is tailored to achieve enhanced nutritional value and functionality. The graphical user interface (GUI), such as GUI235displayed on the first user device, clearly enumerates the adjusted quantities of each amino acid, for instance, Alanine, Methionine, Proline, and Cysteine, as calculated by the proprietary algorithm. Furthermore, the GUI displays the cumulative weight of the amino acids added per 100 grams of the protein sample, thereby allowing users or manufacturers to precisely calibrate the additions according to the algorithm's recommendations. This interface and the data it presents serve as a crucial tool in the manufacturing process, enabling the systematic adjustment of amino acid levels to meet specific dietary requirements or performance criteria, ultimately improving the quality and effectiveness of the protein products.

FIG.3Adepicts a protein sample300, such as a cow's milk, housed within a container302. The figure shows the dispersal of several amino acids304, within the confines of the container. The visual representation is critical for understanding the composition of the protein sample, as it identifies the presence of amino acids, which are the fundamental building blocks of proteins. These amino acids are essential for various biological functions and contribute significantly to the nutritional value of the protein sample. In detail,FIG.3Bfurther elucidates the molecular structure310of these amino acids. The figure shows a general amino acid structure, central to understanding protein chemistry. The structure includes a central carbon atom, an amino group (NH2), a carboxyl group (COOH), a hydrogen atom (H), and a variable side chain or R-group. The R-group is crucial as it varies between amino acids, imparting unique chemical properties and functions to each. This diagram serves not only as an educational tool about amino acid structure but also as a foundation for discussing how amino acids contribute to the functionality and nutritional value of the protein sample shown inFIG.3A.

FIG.3Cpresents a comprehensive amino acid profile340amino acids342for three types of protein, whey344, casein346, and soy348, with values expressed per 100 grams of the product. This figure highlights the presence and quantity of various amino acids in each protein type, providing valuable insight into their nutritional composition. Whey protein demonstrates a high content of essential amino acids, particularly leucine, which is crucial for muscle protein synthesis. It also contains significant amounts of glutamic acid and aspartic acid, which play vital roles in metabolic processes and energy production. Other notable amino acids in whey include lysine and isoleucine, which are essential for growth and tissue repair.

Casein protein, on the other hand, is rich in glutamic acid and proline, contributing to its unique slow-digesting properties. This makes casein an excellent choice for sustained amino acid release. It also contains higher levels of histidine and methionine compared to whey, supporting various physiological functions, including enzyme activity and antioxidant defense. Soy protein stands out for its high arginine content, which supports cardiovascular health and enhances nitric oxide production. It also contains a balanced profile of essential amino acids, making it a complete plant-based protein source. The presence of phenylalanine and tyrosine in soy protein aids in neurotransmitter synthesis, which is crucial for brain function.

The superscripts inFIG.3Cindicate essential amino acids (denoted by “a”) and branched-chain amino acids (denoted by “b”). Essential amino acids, such as histidine, lysine, and threonine, are vital as they cannot be synthesized by the body and must be obtained through diet. Branched-chain amino acids, including isoleucine, leucine, and valine, are particularly important for muscle recovery and growth. Overall,FIG.3Cprovides a detailed comparison of the amino acid compositions of whey, casein, and soy proteins, underlining their respective benefits and applications in nutrition and health. This information is essential for developing dietary supplements, functional foods, and specialized nutrition products tailored to meet specific dietary needs and health goals.

FIG.4C,FIG.4D, andFIG.4Edepict a method flow for adjustment and optimization of the amino acid profile to meet specific criteria. InFIG.4C, the process begins with obtaining an amino acid profile of a protein sample from a second computing device, as indicated in step454. This profile includes a plurality of amino acids, each quantified and recorded. Once the profile is obtained, it is stored in a connected database, creating a protein sample record as shown in step456. This record is crucial for maintaining the integrity and accessibility of the profile for further processing. Following storage, a program is executed on a server that is communicably coupled with a communications network, as described in step458. This server accesses the connected database, which contains a plurality of instructions necessary for processing the amino acid profile. In step460, the server retrieves the amino acid profile from the protein record along with the relevant instructions. Subsequently, in step462, each amino acid from the profile is plotted on a two-dimensional graph. This graphical representation helps visualize the distribution and relative abundance of amino acids within the protein sample.

The method flow diagram depicted inFIG.4Dcontinues the process of analyzing and adjusting the amino acid profile of a protein sample, building upon the steps detailed in the preceding figures. This portion of the method begins with step464, wherein each of the plurality of amino acids from the previously obtained amino acid profile is plotted on a two-dimensional graph. This graphical representation, arranged in descending order based on the amino acid values, facilitates the visualization of the distribution and relative abundance of each amino acid within the protein sample. Subsequently, in step466, the process involves calculating an initial slope value for each of the final five data points on the graph. This calculation employs the formula M=(Y2−Y1)/(X2−X1), where Y represents the values of the amino acids and X denotes their positions in the descending order. The initial slope values provide critical insights into the rate of change in amino acid concentrations across the final data points, forming the basis for further adjustments.

Following the calculation of the initial slope values, step468entails adjusting the amino acid profile algorithmically to generate an adjusted profile. This adjustment is performed using a predefined algorithm that takes into account the initial slope values and other relevant factors. The objective of this step is to optimize the amino acid composition, enhancing the nutritional and functional properties of the protein sample to meet specific criteria. In step470, the process calculates the percent difference for each of the final five data points by comparing the highest initial slope value with each respective slope value of the final five data points in the slope regression tail. This calculation is crucial for identifying deviations and understanding the variability among the data points, which are necessary for precise adjustments. The final step depicted inFIG.4Dis step472, where the percent difference calculated in the previous step is multiplied by each respective initial slope value to yield a product. This product reflects the adjusted value for each of the final five data points, ensuring that the corrections made are systematically applied to the amino acid profile. By incorporating these adjustments, the method refines the amino acid profile, making it more accurate and aligned with the desired specifications.

The method flow diagram depicted inFIG.4Eillustrates the final steps in the process of adjusting the amino acid profile of a protein sample. This part of the method continues from the preceding steps detailed inFIG.4Dand finalizes the adjustments to achieve the desired profile. In step474, the process involves adding the product, which was calculated by multiplying the percent difference by each respective initial slope value, back to the original value of the respective amino acid. This addition yields a corrected weight for each of the final five data points. By incorporating the calculated product into the amino acid values, the method ensures that the adjustments are precisely applied, resulting in a more accurate and optimized amino acid profile. Following the incorporation of the corrected weights, step476applies an additional correction score to further refine the profile. Specifically, a correction score of 1%, 2%, and 3% is added respectively to the lowest three amino acids in the profile. This step ensures that even the amino acids present in the smallest quantities are appropriately adjusted, contributing to a balanced and comprehensive amino acid profile. The application of these correction scores enhances the overall accuracy and optimization of the protein sample, aligning it with specific nutritional and functional criteria. Overall, the steps outlined inFIG.4Eare critical for finalizing the adjustments to the amino acid profile. By systematically incorporating the calculated corrections and applying additional adjustment scores, the method ensures that the resulting amino acid profile is finely tuned to meet specific requirements. This detailed approach is essential for developing high-quality protein products with optimized amino acid compositions, which are valuable for applications in health and nutrition.

In another embodiment, the method for increasing muscle protein synthesis (MPS) relative to another nitrogenous process comprises obtaining, from a second computing device, an amino acid profile of a protein sample comprising a plurality of amino acids, wherein each of the plurality of amino acids comprise a value measured in grams and wherein the value of the plurality of amino acids are sorted from greatest to least, storing, in a connected database, the amino acid profile in a protein record, and executing a program on a server communicable coupled with a communications network. The connected database comprises a plurality of instructions for accessing the amino acid profile from the amino acid record and the plurality of instructions in the connected database, and identifying from the amino acid profile a subset of five target amino acids comprising five lowest values of the amino acid profile. Further, the instructions are for calculating an initial slope value for each of the subset of five target amino acids, wherein the initial slope value is defined as M=(Y2−Y1)/(X2−X1), with Y representing each value for each of five target amino acids and X representing a position, in a descending order for each of the target amino acid, and adjusting the amino acid profile algorithmically to generate an adjusted amino acid profile.

In various embodiments, the amino acid profile is adjusted by calculating, for each of the target amino acids, a percent difference between (i) a highest initial slope value of said initial slope values and (ii) the initial slope values of each target amino acids of the plurality of amino acids, and multiplying, for each of the target amino acids, the percent difference by each respective initial slope value to yield a product. Further, the amino acid profile is adjusted based on adding, for each of the target amino acids, the product back to the value of the respective target amino acid to yield a corrected weight for each of target amino acids. For a lowest three of the target amino acids, adding a correction score of 1%, 2%, and 3% respectively, by multiplying the corrected weight for each of the lowest three of the target amino acids by the correction score and then subtracting that correction value from the corrected weight, and providing, to the second computing device a message to display (i) a graphical representation displaying on a two dimensional graph the initial slope value for the target amino acids that define a slope regression tail and (ii) a second graphical representation displaying the two dimensional graph an adjusted slope value for an adjusted target amino acids for the adjusted amino acid profile that define an adjusted slope regression tail.

In an example embodiment, with reference toFIGS.6A and6B, the disclosed algorithm for determining amino acid (AA)-profile specific alterations comprises a series of computational steps, exemplified for the protein sample denoted as “O.N-GS100%”. The output of the algorithm results in the adjustment of SRT-AA #4 (Methionine) quantities, measured in grams. Initially, in the first step the calculation of the percent difference between the values of SRT-AA #5 (Arginine) of and SRT-AA #2 (Histidine) of amino acid profile ofFIG.6Ais performed. This is achieved by subtracting the value of SRT-AA #2 (1.54 grams) from the value of SRT-AA #5 (1.92 grams), yielding a difference of 0.38 grams. This difference is then divided by the value of SRT-AA #2 (1.54 grams), resulting in a quotient of 0.25 (Product #1). Subsequently, in the next step, the quotient obtained from Step 1 (Product #1) is multiplied by the value of SRT-AA #2 (1.54 grams), producing a result of 0.39 grams (Product #2). In the next step, the value derived in Step 2 (Product #2) is added to the original value of SRT-AA #4 (1.54 grams), resulting in a corrected weight of 1.93 grams (Product #3). In the subsequent step, a correction factor ranging from 1% to 3% is applied to the value of SRT-AA #3-1. For a 2% correction, the value obtained in Step 3 (1.93 grams) is multiplied by 0.02, resulting in a correction factor of 0.04 grams. This correction factor is then subtracted from the value obtained in Step 3, yielding a final adjusted weight of 1.89 grams. The alteration in the values of the SRT values, specifically the SRT-AA #2 (Histidine), is depicted inFIGS.6A and6B.FIG.6Aillustrates the previous value of SRT-AA #2 (Histidine), whereasFIG.6Bdisplays the updated value of SRT-AA #2 (Histidine) as 1.89 grams. Further,FIGS.5A-5Fprovide graphical representations of the updated SRT values of the amino acids compared to their previous values across various protein samples. These figures illustrate a change in the slope for the updated SRT values for each protein sample, effectively demonstrating the algorithm's ability to modify and optimize the amino acid profiles according to specific requirements and variations in each sample. For exemplary purposes, the disclosed calculation is demonstrated using only one protein sample. It should be understood that in various embodiments, the algorithm is designed to perform this analysis across each of the specified SRT values for each protein sample. This systematic application of the algorithm ensures the determination and updating of the SRT values for the amino acids for each protein sample, thereby facilitating precise adjustments tailored to the specific needs of each sample.

FIG.5Aillustrates the plot502for the original amino acid profile of a protein sample. In this figure, the x-axis is labeled “AA's of O.N-GS100%”508, representing the types of amino acids present in the protein sample. The y-axis measures the amino acid concentration in grams per 100 grams of protein (AAg/100 g protein), facilitating an accurate depiction of the protein's composition. The plot contains multiple data points510, each marking the concentration of individual amino acids in the protein sample. A trend line506is introduced to visualize the overall distribution and concentration trends of the amino acids. Notably, the plot includes a slope regression tail (SRT)512, depicted at the end of the amino acid sequence, which indicates the slope of the final five data points. This metric is crucial as it provides insights into the rate of change in amino acid concentrations, highlighting significant variations at the end of the profile. An arrow514marks the initiation point of this SRT, emphasizing its role in the analysis.FIG.5Aportrays the original amino acid profile, where the trend line shows a steeper slope regression tail (SRT), indicating greater variability in amino acid concentrations towards the end of the profile. This variability is significant as it suggests a less consistent protein composition, which can affect the protein's functional properties and its suitability for specific nutritional applications. The presence of a steeper SRT in the original amino acid profile highlights the concentration disparities among amino acids, particularly those appearing towards the sequence's end.

FIG.5Bpresents the plot504for the amino acid profile post-algorithmic adjustments. Maintaining the same x-axis asFIG.5A, this figure introduces a new y-axis measurement, now indicating the adjusted amino acid concentration (g/100 g), reflecting the algorithmic enhancements applied to the profile. Similar toFIG.5A, multiple data points510represent the adjusted concentrations of each amino acid, with a trend line506illustrating the new distribution pattern. The adjusted plot's SRT512exhibits a more horizontal alignment compared to the steeper SRT inFIG.5A. This alteration suggests a more stabilized and balanced distribution of amino acids, particularly noticeable at the tail end of the profile. An arrow514again points to where this more horizontal SRT begins, underscoring the improved uniformity and optimization of the amino acid distribution. In contrast toFIG.5A,FIG.5Bdisplays the adjusted amino acid profile post-application of the algorithmic enhancements. The critical observation in this figure is the more horizontal orientation of the SRT, which denotes a marked improvement in the evenness and balance of the amino acid distribution. A more flat or horizontal SRT towards the end of the profile indicates that the final amino acids in the sequence have been adjusted to levels that are closer to those of their predecessors, resulting in a smoother transition and reduced concentration spikes. This uniformity is advantageous as it signifies a more stable and predictable profile, which is often critical for enhancing the protein's bioavailability and functionality.

The flattened SRT inFIG.5Breflects the success of the algorithmic adjustments in normalizing the amino acid levels and reducing the extremes that might detract from the protein's overall quality. By achieving a more consistent distribution of amino acids, the protein sample becomes more tailored to meet specific dietary needs and more effective in applications where gradual amino acid release is beneficial, such as in sustained energy and muscle recovery products. BothFIG.5AandFIG.5Bprovide a visual representation of the amino acid profiles before and after the algorithmic adjustments. This visual comparison is crucial as it not only illustrates the quantitative changes in amino acid concentrations but also highlights the qualitative enhancements in the profile's structure. The plots effectively demonstrate how the disclosed embodiments of the algorithm optimize the protein's amino acid profile, making it more aligned with nutritional standards and functional requirements. In conclusion, the comparative analysis and visual representations inFIG.5AandFIG.5Bare central to understanding the impact of the disclosed algorithmic adjustments. They clearly show how the adjustments lead to a more balanced and functionally advantageous amino acid profile. This optimized profile, as demonstrated by the more horizontal SRT inFIG.5B, is a testament to the efficacy of the disclosed embodiments, providing significant improvements over traditional methods and offering a sophisticated approach to protein sample optimization. This enhanced method is not only beneficial for achieving desired nutritional outcomes but also for maintaining consistency and efficacy in protein-based products, underscoring the patent's applicability and value in the field of biotechnology and health sciences.

The illustrations inFIG.5CandFIG.5Dfurther exemplify the refined approach to evaluating and optimizing the amino acid profiles in protein samples, specifically using peanuts as the sample under analysis. These figures collectively demonstrate the application and effectiveness of the patented method for optimizing amino acid distribution, critical for enhancing the nutritional profile of commonly consumed legumes such as peanuts.FIG.5Cprovides a detailed graphical representation of the amino acid profile for a peanut protein sample prior to any adjustments. The x-axis, marked as “AA's of Peanuts”560, lists the sequence of amino acids analyzed within the sample. The y-axis quantifies these amino acids, presenting their concentration in grams per 100 grams of protein (AAg/100 g protein)540. This graph includes a series of data points556, each pinpointing the concentration of a distinct amino acid within the sample. A trend line552is plotted to visualize the distribution and trends in amino acid concentrations across the sample. Notably, this figure includes an arrow562to indicate where the slope regression tail (SRT) begins, highlighting the segment of the amino acid sequence where significant variability or a change in concentration trend might occur.

FIG.5Dtransitions from the raw amino acid profile to showcasing the effects of the patented algorithmic optimization on the same peanut protein sample. Maintaining the same x-axis configuration asFIG.5Cfor comparative purposes, the y-axis inFIG.5Dis adjusted to represent the amino acid concentration post-optimization, noted as g/100 g542. This figure also displays a series of data points556, now reflecting the optimized concentrations of amino acids. The trend line552in this graph illustrates the altered distribution pattern post-adjustment, which ideally demonstrates a more uniform and optimized amino acid profile. The SRT in this figure is distinctly more horizontal as indicated by the arrow562inFIG.5D, suggesting that the adjustments have resulted in a stabilization of the concentration variations towards the end of the amino acid sequence. The direct comparison betweenFIG.5CandFIG.5Dunderscores the transformation in the amino acid profile due to the patented algorithmic adjustments. InFIG.5C, the initial profile may show a steeper SRT, indicating abrupt changes in concentration at certain points, which could potentially affect the protein's overall nutritional impact. Conversely,FIG.5Dillustrates a smoother, more horizontal SRT, signifying a balanced amino acid profile with reduced extremes in concentration variability. This adjustment not only enhances the predictability and uniformity of the amino acid levels but also optimizes the nutritional quality of the peanuts, making the protein more suitable for targeted dietary applications.

FIG.5EandFIG.5F, display optimizing amino acid profiles in cow's milk. These figures provide the efficacy of the method for enhancing the nutritional value of dairy products through precise amino acid adjustment.FIG.5Epresents a graphical representation of the original amino acid profile in cow's milk. The x-axis, labeled as “AA's of Cow's Milk”580, lists the sequence of amino acids analyzed within the cow's milk sample. The y-axis, labeled as AAg/100 g protein570, quantifies these amino acids in grams per 100 grams of protein, displaying a range of concentrations across different amino acids. The plot is populated with data points575, each representing the concentration of a specific amino acid in the milk. A trend line578helps to illustrate the overall trend and distribution of amino acid concentrations within the sample. This line shows the initial state of amino acid levels before any algorithmic adjustment is applied.

FIG.5Ftransitions from displaying the original amino acid concentrations to showing the effects of the patented adjustments. It maintains the same x-axis580for a consistent comparison, while the y-axis572now denotes the adjusted amino acid concentration labeled as g/100 g+Patent, indicating the enhancement following the patented method. The plot in this figure also features data points575which correspond to the adjusted concentrations of amino acids. The trend line578inFIG.5Fdemonstrates a significant change in the distribution pattern of amino acids, becoming more uniform across the sequence. Notably, this figure highlights where the slope regression tail (SRT) begins, showing a marked stabilization in the concentration variability towards the end of the sequence. The juxtaposition ofFIG.5EandFIG.5Fprovides a clear visual representation of the improvements in the amino acid profile of cow's milk due to the application of the disclosed method.FIG.5Eshows a more variable and possibly less optimal distribution of amino acids, as evidenced by the original trend line578. In contrast,FIG.5Fexhibits a trend line578that is smoother and more horizontal, indicating a balanced and stabilized amino acid distribution post-adjustment. This comparative visualization underscores the effectiveness of the patented method in optimizing amino acid profiles, which is crucial for enhancing the nutritional quality of dairy products.

These figures collectively serve to demonstrate the practical application and benefits of the patented method in real-world food products. By adjusting the amino acid profiles in cow's milk, the method ensures enhanced bioavailability and nutritional efficacy, making the milk more beneficial for various consumer needs. This optimization technique not only improves the quality of dairy products but also supports broader nutritional objectives, making it a valuable addition to food science and nutrition technologies. The illustrations inFIG.5EandFIG.5Fprovide empirical evidence of the algorithm's capability to refine and enhance the amino acid profiles of widely consumed dairy products like cow's milk. The clear, quantifiable improvements demonstrated in these figures reinforce the patent's utility and innovativeness, highlighting its potential impact on the dairy industry and consumer health. The illustrations onFIGS.5E and5Fmay be displayed on the display (similar to as shown inFIGS.2A and2B) so that a user can easily compare the improvement of the protein sample if the amino acids suggested to be added are added to the sample.

The present disclosure relates to a detailed amino acid profile600of various protein samples, as illustrated inFIGS.6A and6B, which delineate the quantification and optimization of amino acids in protein powder602, peanuts604, and cow's milk606. Each of these profiles is organized to show the amino acid contents in descending order and highlights the specific implementation of supplementary regression target (SRT) values608for the last four amino acids in each profile.

Referring toFIG.6A, the amino acid profile for a protein powder sample, designated as “O.N.-GS1000” lists major amino acids including Glutamic Acid at 15.12 grams, Aspartic Acid at 9.49 grams, and Leucine at 8.98 grams. This profile proceeds to list additional amino acids such as Lysine, Threonine, Isoleucine, Proline, Valine, Alanine, Serine, Phenylalanine, Tyrosine, and Tryptophan, in decreasing quantities. The SRT values, critical to the understanding of amino acid optimization, include Arginine (1.92 grams, 3.00% slope), Cysteine (1.89 grams, 22.00% slope), Methionine (1.67 grams, 13.00% slope), and Histidine (1.54 grams, 2.00% slope), with Glycine listed as a reference low-abundance amino acid (RLAA) at 1.52 grams. Collectively, these SRT amino acids represent approximately 27% of the total 18-amino acid profile of the protein powder, providing a basis for targeted nutritional enhancements.

The amino acid profile for peanuts reveals a different pattern, with Aspartic Acid leading at 9.71 grams, followed by significant amounts of Glutamic Acid and Tryptophan. This profile includes Leucine, Arginine, Lysine, Valine, Serine, and Isoleucine, among others, with notable SRT values for Histidine (2.82 grams, 4.50% slope), Alanine (2.78 grams, 44.00% slope), Methionine (2.34 grams, 21.50% slope), and Proline (2.12 grams, 47.50% slope), demonstrating a focus on optimizing specific amino acids that are pivotal for enhancing the peanut protein's functional properties. In the case of cow's milk, the amino acid profile is similarly detailed, featuring Glutamic Acid at 12.96 grams and continuing with high concentrations of Leucine, Proline, and Lysine. Lesser, but still significant, quantities include Aspartic Acid, Valine, Isoleucine, Serine, Tyrosine, Phenylalanine, and Threonine. The SRT values for cow's milk emphasize the strategic enhancement of Methionine (2.11 grams, 84.00% slope), Histidine (2.29 grams, 18.00% slope), Tryptophan (1.27 grams, 7.00% slope), and Glycine (1.20 grams, 54.00% slope), with the aim of improving the milk's nutritional value for diverse applications. These comprehensive amino acid profiles, as set forth inFIG.6A, illustrate a systematic approach to modifying protein sources through precise amino acid supplementation. This methodology significantly improves the nutritional quality of protein samples and offers a substantial advancement over prior art, providing tailored dietary solutions with enhanced functional properties.

FIG.6Bpresents an adjusted amino acid profile620for various protein samples, including the protein powder621, peanuts622, and cow's milk624. The table details the quantities of various amino acids for each protein sample in descending order, with the last four amino acids categorized as SRT values626. For the protein powder sample labeled as “O.N.-GS1000”, the table lists the amino acid values in grams along with their SRT slopes. Specifically, the amino acids present include Glutamic Acid at 15.12 grams, Aspartic Acid at 9.49 grams, and Leucine at 8.98 grams. Following these, the profile includes Lysine at 8.09 grams, Threonine at 5.94 grams, Isoleucine at 5.73 grams, Proline at 4.97 grams, Valine at 4.74 grams, Alanine at 3.96 grams, Serine at 3.90 grams, Phenylalanine at 2.60 grams, Tyrosine at 2.53 grams, and Tryptophan at 1.92 grams. The SRT amino acids for this sample include Arginine at 1.92 grams with a slope of 0.00%, Cysteine at 1.92 grams with a slope of 2.00%, Methionine at 1.90 grams with a slope of 1.00%, and Histidine at 1.89 grams with a slope of 3.00%. Glycine is listed under the RLAAs category at 1.86 grams. Collectively, these SRT amino acids represent approximately 27% of the total 18-amino acid profile, with a total addition of 0.95 grams per 100 grams of the protein powder.

For the peanut sample, the amino acid profile begins with Aspartic Acid at 9.71 grams, followed by Glutamic Acid at 7.15 grams and Tryptophan at 7.12 grams. The profile continues with Leucine at 7.03 grams, Arginine at 5.62 grams, Lysine at 5.25 grams, Valine at 5.08 grams, Serine at 5.04 grams, Isoleucine at 4.77 grams, Tyrosine at 3.87 grams, Phenylalanine at 3.79 grams, Threonine at 3.12 grams, and Glycine at 2.96 grams. The SRT values for peanuts include Histidine at 2.82 grams with a slope of 0.00%, Alanine at 2.82 grams with a slope of 2.00%, Methionine at 2.80 grams with a slope of 4.00%, and Proline at 2.76 grams with a slope of 2.00%. Cysteine is listed at 2.74 grams, contributing to a total addition of 2.25 grams per 100 grams of peanuts. In the case of cow's milk, the amino acid profile includes Glutamic Acid at 12.96 grams, Leucine at 7.20 grams, Proline at 7.08 grams, and Lysine at 6.64 grams. The profile also lists Aspartic Acid at 5.58 grams, Valine at 4.86 grams, Isoleucine at 4.81 grams, Serine at 4.74 grams, Tyrosine at 4.68 grams, Phenylalanine at 4.46 grams, Threonine at 3.90 grams, Arginine at 3.11 grams, and Alanine at 2.50 grams. The SRT amino acids for cow's milk include Histidine at 2.29 grams with a slope of 0.00%, Methionine at 2.29 grams with a slope of 3.00%, Tryptophan at 2.26 grams with a slope of 2.00%, and Glycine at 2.24 grams with a slope of 3.00%. Cysteine is listed at 2.21 grams, leading to a total addition of 3.76 grams per 100 grams of cow's milk. These SRT values for each protein sample highlight the importance of specific amino acids in adjusting and optimizing the amino acid profiles, contributing to enhanced nutritional and functional properties of the protein samples.

In the disclosed embodiments, reference is made toFIGS.6A and6B, which detail the quantitative amendments in the amino acid profiles of protein samples by delineating the specific quantities of the amino acids Cysteine, Methionine, Histidine, and Glycine added thereto. Notably,FIG.6Bdiscloses a cumulative addition of 3.76 grams of these amino acids to a baseline quantity of 100 grams of the protein sample, representing an enhancement beyond the intrinsic amino acid composition. This augmentation is computed as the aggregate difference between the concentrations in an adjusted amino acid profile and those in the original amino acid profile of the sample.

Illustratively, the adjusted amino acid profile for Tryptophan in cow's milk is noted at 2.26 grams inFIG.6Bas opposed to the 1.27 grams in the unmodified profile ofFIG.6A, reflecting a net increase of 0.99 grams. In a like manner, the concentration of Methionine escalates modestly from 2.11 to 2.29 grams, providing a differential of 0.18 grams. Further increments are observed with Glycine and Cysteine, where the increases are recorded at 1.04 and 1.55 grams, respectively, summing to the total enhanced weight of 3.76 grams. These updated values of the SRT may be displayed on a display transmitted to the user on the first user device, similar to the GUI235displayed on the first user device ofFIG.2Cfor the protein sample peanuts.

The differential values serve as a guideline for the precise addition of amino acids to the protein sample. This method ensures that the total quantity of amino acids incorporated does not surpass the calculated threshold of 3.76 grams. Typically, the enhancement involves micro-additions ranging from 1 to 2 grams per amino acid, facilitating the tailored optimization of the amino acid profiles of diverse protein samples. Such specificity in modification permits the adjustment of caloric content contributed by the added amino acids, as evidenced inFIG.6B. Depending on the protein sample, the additional amino acid content can vary, such as a mere 0.95 grams in one instance, contrasted with a more substantial addition of 3.76 grams in another, the latter correlating with a higher caloric increment.

This innovative method markedly advances the nutritional and functional quality of protein samples. By enabling the precise calibration of amino acid additions, the invention enhances the suitability of protein samples for varied dietary requirements and application-specific functionalities, presenting a significant improvement over the prior art. This system not only offers a refined approach to protein supplementation but also contributes to the broader utility of dietary proteins in nutritional science and food technology.

Referring now toFIG.7, a block diagram of a system including an example computing device700and other computing devices is shown, according to an exemplary embodiment of present technology. Consistent with the embodiments described herein, the aforementioned actions performed by the processor may be implemented in a computing device, such as the computing device700ofFIG.7. Any suitable combination of hardware, software, or firmware may be used to implement the computing device700. The aforementioned system, device, and processors are examples and other systems, devices, and processors may comprise the aforementioned computing device. Furthermore, computing device700may comprise an operating environment for computing device700and methods400,450and other described herein. Methods400, and450and others described herein may operate in other environments and are not limited to computing device700.

With reference toFIG.7, a system consistent with an embodiment of the invention may include a plurality of computing devices, such as computing device700. In a basic configuration, computing device700may include at least one processing unit702and a system memory704. In addition, computing device700may include at least one graphics processing unit (GPU)703to render images and videos quickly and efficiently. It accelerates graphics processing, offloads tasks from the processing unit702, and enables real-time interactivity and high-quality visuals in applications. Depending on the configuration and type of computing device, system memory704may comprise, but is not limited to, volatile (e.g., random access memory (RAM)), non-volatile (e.g., read-only memory (ROM)), flash memory, or any combination or memory. System memory704may include operating system705, and one or more programming modules706. Operating system705, for example, may be suitable for controlling computing device700's operation. In one embodiment, programming modules706may include, for example, a program module707for executing the actions of the portable detector, for example. Furthermore, embodiments of the invention may be practiced in conjunction with a graphics library, other operating systems, or any other application program and is not limited to any particular application or system. This basic configuration is illustrated inFIG.7by those components within a dashed line720.

Computing device700may have additional features or functionality. For example, computing device700may also include additional data storage devices (removable and/or non-removable) such as, for example, magnetic disks, optical disks, or tape. Such additional storage is illustrated inFIG.7by a removable storage709and a non-removable storage710. Computer storage media may include volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information, such as computer readable instructions, data structures, program modules, or other data. System memory704, removable storage709, and non-removable storage710are all computer storage media examples (i.e., memory storage.) Computer storage media may include, but is not limited to, RAM, ROM, electrically erasable read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store information and which can be accessed by computing device700. Any such computer storage media may be part of computing device700. Computing device700may also have input device(s)712such as a keyboard, a mouse, a pen, a sound input device, a camera, a touch input device, etc. Output device(s)714such as a display, speakers, a printer, etc. may also be included. The aforementioned devices are only examples, and other devices may be added or substituted.

Computing device700may also contain a communication connection716that may allow computing device700to communicate with other computing devices718, such as over a network in a distributed computing environment, for example, an intranet or the Internet. Communication connection716is one example of communication media. Communication media may typically be embodied by computer readable instructions, data structures, program modules, or other data in a modulated data signal, such as a carrier wave or other transport mechanism, and includes any information delivery media. The term “modulated data signal” may describe a signal that has one or more characteristics set or changed in such a manner as to encode information in the signal. By way of example, and not limitation, communication media may include wired media such as a wired network or direct-wired connection, and wireless media such as acoustic, radio frequency (RF), infrared, Bluetooth® and other wireless media. The term computer readable media as used herein may include both computer storage media and communication media.

As stated above, a number of program modules and data files may be stored in system memory704, including operating system705. While executing on processing unit702, programming modules706(e.g., program module707) may perform processes including, for example, one or more of the stages of the methods400and450as described above. The aforementioned processes are examples, and processing unit702may perform other processes. Other programming modules that may be used in accordance with embodiments of the present invention may include electronic mail and contacts applications, word processing applications, spreadsheet applications, database applications, slide presentation applications, drawing or computer-aided application programs, etc.

Generally, consistent with embodiments of the invention, program modules may include routines, programs, components, data structures, and other types of structures that may perform particular tasks or that may implement particular abstract data types. Moreover, embodiments of the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable user electronics, minicomputers, mainframe computers, and the like. Embodiments of the invention may also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

Furthermore, embodiments of the invention may be practiced in an electrical circuit comprising discrete electronic elements, packaged or integrated electronic chips containing logic gates, a circuit utilizing a microprocessor, or on a single chip (such as a System on Chip) containing electronic elements or microprocessors. Embodiments of the invention may also be practiced using other technologies capable of performing logical operations such as, for example, AND, OR, and NOT, including but not limited to mechanical, optical, fluidic, and quantum technologies. In addition, embodiments of the invention may be practiced within a general-purpose computer or in any other circuits or systems.

Embodiments of the present invention, for example, are described above with reference to block diagrams and/or operational illustrations of methods, systems, and computer program products according to embodiments of the invention. It is understood that, in certain embodiments, the functions/acts noted in the blocks may occur out of order as shown in any flowchart. For example, two blocks shown in succession may in fact be executed substantially concurrently or the blocks may sometimes be executed in the reverse order, depending upon the functionality/acts involved.

While certain embodiments of the invention have been described, other embodiments may exist. Furthermore, although embodiments of the present invention have been described as being associated with data stored in memory and other storage mediums, data can also be stored on or read from other types of computer-readable media, such as secondary storage devices, like hard disks, floppy disks, or a CD-ROM, or other forms of RAM or ROM. Further, the disclosed methods' stages may be modified in any manner, including by reordering stages and/or inserting or deleting stages, without departing from the invention.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. It should also be noted that additional information about the attached methods and systems is included in the appendix to this specification, the substance of which Application hereby incorporated by reference.