Selectively disposing of physical asset

Abstract

To promote learning such as a user behavior, a context base scenario and taste by using artificial intelligence and / or a machine learning system, and a technique.SOLUTION: Techniques regarding selectively disposing of a physical asset are provided. For example, one or more embodiments described herein can include a system, which can include a memory that can store computer executable components. The system can also include a processor, operably coupled to the memory, and that can execute the computer executable components stored in the memory. The computer executable components can include a disposition component that can select a disposition for a physical asset of an organization based on a characteristic of the physical asset and a machine learning model trained based on dispositions of other physical assets of the organization. The computer executable components can include an assignment component that can assign the physical asset to the disposition, resulting in an assigned disposition.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] One or more embodiments relate to managing physical assets of an enterprise, and more particularly, to selecting a method for disposition of physical assets. Summary of the Invention [Problem to be solved by the invention]

[0002] To facilitate taking automated actions with a high degree of confidence, artificial intelligence and / or machine learning systems and techniques are utilized to facilitate learning of user behavior, context-based scenarios, preferences, and the like. [Means for solving the problem]

[0003] The following presents a summary to provide a basic understanding of one or more embodiments of the present disclosure. This summary is not intended to identify key or critical elements or to delineate the scope of any particular embodiments or any claims. Its sole purpose is to present concepts in a simplified form as a prelude to the more detailed description presented later. In one or more embodiments described herein, a system, computer-implemented method, apparatus and / or computer program product is provided that can utilize an intermediate data set to generate a test diffusion model.

[0004] According to some embodiments described herein, a system is provided. The system may include a memory that stores computer-executable components. The system may also include a processor operatively coupled to the memory, the processor may execute the computer-executable components stored in the memory. The computer-executable components may include a treatment component that may select a treatment for a physical asset of an organization based on characteristics of the physical asset and a machine learning model trained based on treatments of other physical assets of the organization. The computer-executable components may further include an assignment component that may assign the physical asset to a treatment, resulting in an assigned treatment.

[0005] In additional or alternative embodiments, the characteristics may include a maintenance history of the physical asset, including maintenance performed on the physical asset. In additional or alternative embodiments, the characteristics may include a material composition of the physical asset, where the machine learning model is further trained based on material compositions of other physical assets of the organization. In additional or alternative embodiments, the disposition may be further based on government regulations associated with the disposal of the physical asset comprised of the material composition. In additional or alternative embodiments, the disposition may be further based on an estimated environmental impact of the disposal of the physical asset comprised of the material composition.

[0006] In additional or alternative embodiments, the disposition may include transporting the physical asset to a geographic location for disposal, where the estimated environmental impact may include an estimated environmental impact for the geographic location. In additional or alternative embodiments, the computer-executable components may further include a reverse logistics component that determines reverse logistics associated with the disposition, where the reverse logistics component may include transporting the physical asset back to an original supplier of the physical asset. In additional or alternative embodiments, the characteristics may further include a cost of disposition of the physical asset to the organization, where the machine learning model is further trained based on respective costs of disposition of other physical assets of the organization. In additional or alternative embodiments, the computer-executable components may further include a disposition component that facilitates disposing of the physical asset according to the assigned disposition.

[0007] According to one or more example embodiments, a computer-implemented method is provided. The computer-implemented method may include selecting a disposition for a physical asset of an organization based on characteristics of the physical asset and a machine learning model trained based on dispositions of other physical assets of the organization. The computer-implemented method may further include assigning the physical asset to the disposition, resulting in the assigned disposition.

[0008] In additional or alternative embodiments, the characteristics may include a maintenance history of the physical asset, including maintenance performed on the physical asset. In additional or alternative embodiments, the characteristics may include a material composition of the physical asset, where the machine learning model is further trained based on the material compositions of other physical assets of the organization. In additional or alternative embodiments, the disposition may be further based on government regulations associated with the disposition of physical assets comprised of the material composition.

[0009] In additional or alternative embodiments, the disposition may be further based on an estimated environmental impact of disposing of the physical asset comprised of the material composition. In additional or alternative embodiments, the disposition may include transporting the physical asset to a geographic location for disposal, where the estimated environmental impact may include an estimated environmental impact for the geographic location. In additional or alternative embodiments, the computer-implemented method may further include determining, by the device, reverse logistics associated with the disposition, which may include transporting the physical asset back to an original supplier of the physical asset.

[0010] According to another example embodiment, a computer program product for selectively disposing of physical assets is provided. The computer program product may include a computer-readable storage medium having program instructions embodied therein. The program instructions may be executable by a processor to cause the processor to select a disposition for a physical asset of an organization based on characteristics of the physical asset and a machine learning model trained based on dispositions of other physical assets of the organization. In a different embodiment, the program instructions may further include assigning the physical asset to a disposition, resulting in the assigned disposition.

[0011] In additional or alternative embodiments, the characteristics may include a maintenance history of the physical asset, including maintenance performed on the physical asset. In additional or alternative embodiments, the characteristics may include a material composition of the physical asset, where the machine learning model is further trained based on the material compositions of other physical assets of the organization. In additional or alternative embodiments, the disposition may be further based on government regulations associated with the disposition of physical assets comprised of the material composition.

[0012] Other embodiments may become apparent from the following detailed description when taken in conjunction with the drawings. [Brief description of the drawings]

[0013] In certain embodiments, the present invention will be described with reference to the accompanying figures. The figures provided herein are intended to facilitate a clear understanding of the invention, and are not intended to limit the scope or functionality of the invention in any way.

[0014] [Figure 1] 1 illustrates a block diagram of an example system that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein.

[0015] [Diagram 2] 1 illustrates a block diagram of an example system that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein.

[0016] [Diagram 3] 1 illustrates a block diagram of an example system that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein.

[0017] [Figure 4] FIG. 1 illustrates a block diagram of an example system that may utilize artificial intelligence / machine learning techniques to facilitate selective disposition of physical assets, according to one or more embodiments described herein.

[0018] [Diagram 5] 1 illustrates a block diagram of an example system that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein.

[0019] [Figure 6] 1 illustrates a block diagram of an example system that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein.

[0020] [Figure 7]1 illustrates a flow diagram of an example, non-limiting computer-implemented method that may facilitate selective disposition of physical assets in accordance with one or more embodiments described herein.

[0021] [Figure 8] 1 illustrates an example computer program product (CPP) that may include executable instructions that, when executed by a processor of a system, may facilitate selective disposition of physical assets, according to one or more embodiments.

[0022] [Figure 9] An example of an environment for executing at least some of the computer code associated with performing the methods described herein includes a computing environment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0023] The following detailed description is merely illustrative and is not intended to limit the embodiments and / or the application or use of the embodiments. Furthermore, there is no intention to be bound by any express or implied information presented in the preceding Background or Overview section or Detailed Description section. One or more embodiments are described herein with reference to the drawings. Like reference numerals in the drawings are used to refer to like elements throughout. In the following description, for purposes of explanation, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it will be apparent that in various cases one or more embodiments may be practiced without these specific details.

[0024] 1 illustrates a block diagram of an example system 100 that can facilitate selective disposition of physical assets according to one or more embodiments described herein. System, apparatus, or process embodiments of the present disclosure may constitute one or more machine-executable components embodied in one or more machines, e.g., embodied in one or more computer-readable medium(s) associated with one or more machines. Such components, when executed by one or more machines (e.g., computers, computing devices, virtual machines), may cause the machines to perform the described operations. Repeated descriptions of similar elements and processes utilized in each embodiment are omitted for the sake of brevity.

[0025] As shown, system 100 may include a disposal system 102 that receives asset information 185 from an asset characteristic device 180 and receives disposal recommendations 195 from a recommendation model 190. In one or more implementations described further below with reference to FIG. 4 , the asset characteristic device 180 may provide training data 192 for training the recommendation model 190. In some embodiments, the disposal system 102 may include a memory 104, a processor 106, and computer-executable components 110 coupled to a bus 112.

[0026] It should be noted that when an element is referred to herein as being "coupled" to another element, one or more different types of coupling may be described. For example, when an element is referred to herein as being "coupled" to another element, one or more different types of coupling may be described, including, but not limited to, chemical coupling, communicative coupling, capacitive coupling, electrical coupling, electromagnetic coupling, inductive coupling, operative coupling, optical coupling, physical coupling, thermal coupling, and other types of coupling.

[0027] In one or more embodiments, the disposal system 102 may be operated using any suitable computing device or set of computing devices that may be communicatively coupled to the device, non-limiting examples of which may include, but are not limited to, a server computer, a computer, a mobile computer, a mainframe computer, an automated test system, a network storage device, a communication device, a web server device, a network switching device, a network routing device, a gateway device, a network hub device, a network bridge device, a control system, or any other suitable computing device. The device may be any device that may communicate information with the disposal system 102 and / or any other suitable device that may utilize information provided by the disposal system 102 and enable the computer-executable components 110, as described below. As shown, the computer-executable components 110 may include a disposition component 132, an allocation component 134, and any other components associated with the disposal system 102 that may combine to provide different functions as described herein.

[0028] The memory 104 may include volatile memory (e.g., random access memory (RAM), static RAM (SRAM), dynamic RAM (DRAM), etc.) and non-volatile memory (e.g., read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), etc.), which may utilize one or more memory architectures. Further examples of memory 104 are described below with reference to volatile memory 912 of FIG. 9. Such examples of memory 104 may be utilized to implement any of the embodiments described herein.

[0029] In one or more embodiments, memory 104 may store one or more computer- and machine-readable, writable, and executable components and instructions that, when executed by processor 106 (e.g., classical and quantum processors), may perform operations defined by the executable components and instructions. For example, memory 104 may store computer- and machine-readable, writable, and computer-executable components 110 and instructions that, when executed by processor 106, may perform various functions described herein for disposal system 102, including action component 132, allocation component 134, and other components described herein with or without reference to the various figures of one or more embodiments described herein.

[0030] The processor 106 may include one or more types of processors and electronic circuits (e.g., classical processors, and quantum processors) that may implement one or more computer- and machine-readable, writable, and executable components and instructions that may be stored in the memory 104. For example, the processor 106 may perform various operations that may be specified by such computer- and machine-readable, writable, and executable components and instructions, including, but not limited to, logic, control, input / output (I / O), arithmetic, and the like. In some embodiments, the processor 106 may include one or more central processing units, multi-core processors, microprocessors, dual microprocessors, microcontrollers, systems on chips (SOCs), array processors, vector processors, quantum processors, and other types of processors. Further examples of the processor 106 are described below with reference to the processor set 910 of FIG. 9. Such examples of the processor 106 may be utilized to implement any of the embodiments described herein.

[0031] As described below, the stored data may be generated by some type of artificial neural network (ANN) and may be stored in storage that may include, but is not limited to, RAM, ROM, EEPROM, flash memory, or other memory technology, solid-state drive (SSD) or other solid-state storage technology, compact disk read-only memory (CD ROM), digital video disk (DVD), Blu-ray disk, or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store information for the embodiments and that may be accessed by a computer.

[0032] As shown, the memory 104, the processor 106, the action component 132, the allocation component 134, and any other components of the disposal system 102 described or suggested herein may be communicatively, electrically, operatively, and optically coupled to each other, and to any components coupled thereto, via a bus 112 to perform the functions of the disposal system 102. The bus 112 may include one or more of a memory bus, a memory controller, a peripheral bus, an external bus, a local bus, a quantum bus, and another type of bus that may utilize a variety of bus architectures. Further examples of connections similar to the bus 112 are described below with reference to the computer 901 of FIG. 9. Such examples of the bus 112 may be utilized to implement any of the embodiments described herein.

[0033] In one or more embodiments described herein, the disposal system 102 may utilize the disposition component 132 to perform operations (e.g., via the processor 106) including, but not limited to, selecting a disposition for an organization's physical assets based on characteristics of the physical assets and machine learning models trained based on dispositions of the organization's other physical assets. In additional embodiments described herein, the disposal system 102 may utilize the assignment component 134 to perform operations (e.g., via the processor 106) including, but not limited to, assigning physical assets to dispositions, resulting in assigned dispositions.

[0034] It should be understood that the embodiments described herein and illustrated in the various figures disclosed herein are merely illustrative, and thus the architecture of such embodiments is not limited to the systems, devices, and components illustrated therein. For example, in some embodiments, the disposal system 102 may further include various computer and computing-based elements described herein with reference to the sections below, such as FIG. 9. In various embodiments, the components of the disposal system 102 (such as the action component 132 and the allocation component 134) may include functional elements that may be implemented via cloud technology, physical components (e.g., computer hardware), and local software (e.g., an application on a mobile phone or electronic device).

[0035] 2 illustrates a block diagram of an example system 200 that can facilitate selective disposition of physical assets according to one or more embodiments described herein. Repetitive descriptions of similar elements utilized in one or more embodiments described herein are omitted for the sake of brevity. As used herein, a diffusion model may broadly refer to a model that can generate new samples based on a probability distribution of a dataset that resembles the original dataset by gradually refining random guesses based on learned patterns.

[0036] As shown, system 200 includes the components of disposal system 102 described in FIG 1 with a reverse logistics component 232 added to computer-executable components 110. In one or more embodiments described herein, disposal system 102 may utilize reverse logistics component 232 to perform operations (e.g., via processor 106) including, but not limited to, determining reverse logistics associated with a disposition, which may include transporting a physical asset back to an original supplier of the physical asset.

[0037] 3 illustrates a block diagram of an example system 300 that can facilitate selective disposition of physical assets according to one or more embodiments described herein. Repeated descriptions of similar elements used in one or more embodiments described herein are omitted for the sake of brevity. As used herein, a diffusion model may broadly refer to a model that can generate new samples based on a probability distribution of a dataset that resembles the original dataset by gradually refining random guesses based on learned patterns.

[0038] As shown, system 300 includes the components of disposal system 102 described in FIG 2 with a disposition component 332 added to computer-executable components 110. In one or more embodiments described herein, disposal system 102 may utilize disposition component 332 to perform operations (e.g., via processor 106) including, but not limited to, facilitating the disposition of physical assets in accordance with an assigned disposition determined by allocation component 134 as described above.

[0039] FIG. 4 illustrates a block diagram of an example system 400 that may utilize artificial intelligence (AI) / machine learning (ML) techniques that may facilitate selective disposition of physical assets, according to one or more embodiments described herein. Repeated descriptions of similar elements used in one or more embodiments described herein are omitted for the sake of brevity. Various embodiments herein may utilize artificial intelligence and / or machine learning systems and techniques to facilitate learning of user behaviors, context-based scenarios, preferences, and the like, to facilitate taking automated actions with a high degree of confidence. Utility-based analysis may be utilized to consider the benefits of taking an action against the costs of taking an inaccurate action. Probability or statistics-based analysis may be utilized in connection with the above and / or below.

[0040] As shown, system 400 may include an AI / ML component coupled to disposal system 102. To illustrate various processes that may be performed by AI / ML component 420, AI processing 410 provides examples 415A-E of various types of physical asset related data that may be analyzed and used to train recommendation model 190. It should be noted that systems and / or associated controllers, servers, or machine learning components herein may include artificial intelligence components that may utilize AI and / or ML models that can learn (e.g., via training using historical training data and / or feedback data) to perform the functions described above or below.

[0041] In some embodiments, the AI / ML component 420 may include an AI and / or ML model (e.g., recommendation model 190) that may be trained (e.g., via supervised and / or unsupervised techniques) to perform the recommendation functions described herein using historical training data including various contextual conditions corresponding to various activities associated with the acquisition, conservation, and disposition of physical assets by an organization. In this example, such an AI and / or ML model may further learn (e.g., via supervised and / or unsupervised techniques) to perform the recommendation functions using training data including data associated with physical assets, where such data may be collected and / or stored (e.g., in memory) by the AI / ML component 420. In this example, such feedback data may include various instructions described above / below that may be input to the system herein over time, for example, in response to observed / stored context-based information.

[0042] The AI / ML component herein may initiate operations associated with a defined confidence level determined using information (e.g., feedback data). For example, based on learning to perform such functions described above using the feedback data herein, performance information, and / or past performance information, the AI / ML component 420 may initiate operations associated with determining various thresholds herein (e.g., input pattern thresholds, similarity thresholds, or other suitable thresholds).

[0043] In an embodiment, the AI / ML component 420 may perform a utility-based analysis that considers the benefits versus the costs of initiating the actions described above. In this embodiment, the AI / ML component 420 may use one or more additional contextual conditions to determine the various thresholds herein. For example, the AI ​​processing 410 activities may include, but are not limited to, analyzing information about preventive maintenance 415A, analyzing information about corrective maintenance 415B, analyzing information about asset maintenance contracts 415C, analyzing information about asset depreciation history 415D, and analyzing information about material types, environmental impacts, and legal disposal regulations 415E.

[0044] To facilitate the functions described above, the AI / ML component 420 may perform classification, correlation, inference, and / or representation associated with principles of artificial intelligence. For example, the AI / ML component 420 may utilize an automatic classification system and / or automatic classification. In one example, the AI / ML component 420 may learn and / or generate inferences using probability and / or statistics-based analysis (e.g., considering utility and cost in the analysis). The AI / ML component 420 may utilize any suitable machine learning based technique, statistics-based technique, and / or probability-based technique. For example, the AI / ML component 420 may utilize expert systems, fuzzy logic, support vector machines (SVMs), hidden Markov models (HMMs), greedy search algorithms, rule-based systems, Bayesian models (e.g., Bayesian networks), neural networks, other non-linear training techniques, data fusion, utility-based analysis systems, systems using Bayesian models, and / or the like. In another example, the AI / ML component 420 may perform a set of machine learning calculations. For example, the AI / ML component 420 may perform a set of clustering machine learning calculations, a set of logistic regression machine learning calculations, a set of decision tree machine learning calculations, a set of random forest machine learning calculations, a set of regression tree machine learning calculations, a set of least squares machine learning calculations, a set of instance-based machine learning calculations, a set of regression machine learning calculations, a set of support vector regression machine learning calculations, a set of k-means machine learning calculations, a set of spectral clustering machine learning calculations, a set of rule learning machine learning calculations, a set of Bayesian machine learning calculations, a set of deep Boltzmann machine calculations, a set of deep belief network calculations, and / or a set of different machine learning calculations.

[0045] 5 illustrates a block diagram of an example system 500 that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein. Repeated descriptions of similar elements used in one or more embodiments described herein have been omitted for purposes of brevity.

[0046] In one or more embodiments, asset information 560 may include, but is not limited to, preventive maintenance history, corrective maintenance history, asset depreciation history, type of material used to construct the asset, environmental impact of disposal, active maintenance contracts, current warranties, history of relocation of the asset to another location, drop-in specific locations used for the asset, manner of disposing of the asset through reverse logistics (e.g., reverse logistics component 232), opportunities to donate the asset, opportunities to evaluate / terminate active preventive / corrective maintenance contracts, etc. In one or more embodiments, regulations 570 may include, but is not limited to, legal / government regulations associated with the disposition. At 510A, one or more embodiments may receive an input of an asset to be managed (e.g., to be disposed of, reassigned, etc.). At 510B, asset information, such as asset information 560, may be retrieved from an enterprise data system.

[0047] In some implementations, blocks 510C-G describe a process in which asset information may be analyzed and asset features may be extracted. At 510C, asset information may be analyzed, which may include, but is not limited to, preventive maintenance, corrective maintenance, asset maintenance contracts, asset status, and / or other considerations. At 510D, one or more embodiments may obtain information about other business branch locations and other assets similar to those that may be disposed of. At 510E, one or more embodiments may retrieve information about different relevant regulations for asset and material disposal, such as regulations related to the disposal of assets composed of wood or metal that have batteries, etc. At 510F, one or more embodiments may generate relevant features for a particular asset based on each type of information analyzed. One or more embodiments may obtain descriptive terms via a language model. Additionally, aggregate analysis of asset disposals may be used to assist with recommendations. For example, for each type of asset, the average number of corrective / preventive maintenance may be analyzed, for each type of maintenance, the average cost may be analyzed, etc.

[0048] At 510G, one or more embodiments may generate a dataset for training recommendation model 190 along with an evaluation of the techniques to correlate asset characteristics with asset disposition outcomes. At 510H, one or more embodiments may generate disposition actions for each asset based on the recommendations, e.g., utilizing disposition component 332. At 510I, one or more embodiments may automatically dispatch the actions, e.g., by utilizing disposition component 332 described above. At 510J, one or more embodiments may update inventory 550 based on the collected and analyzed asset data.

[0049] 6 illustrates a block diagram of an example system 600 that can facilitate selective disposition of physical assets in accordance with one or more embodiments described herein. Repeated descriptions of similar elements used in one or more embodiments described herein have been omitted for purposes of brevity.

[0050] At 610, one or more embodiments may extract information from asset information 560 and inventory list 550, for example, as documents and / or stored structured information. At 620A, one or more embodiments may access historical information about past assets that have been available for disposal, actions taken, and asset information. At 620B, one or more embodiments may match this historical information about actions taken with regulations that apply to the asset being disposed of.

[0051] At 620C, one or more embodiments may collect updated information about best practices associated with the disposition of a particular asset, for example, from non-governmental organizations or other associations and associations. At 620E, one or more embodiments may create an AI / ML model (e.g., recommendation model 190) as described in FIG. 4 above, including, for example, but not limited to, an artificial neural network, a random forest, and / or a distributed gradient boosting library. One or more embodiments may also create a knowledge graph 690 from the information collected as described above in the description.

[0052] 7 illustrates a flow diagram of an exemplary non-limiting computer-implemented method 700 that may facilitate selective disposition of physical assets in accordance with one or more embodiments described herein. Repetitive descriptions of similar elements and processes utilized in each embodiment are omitted for the sake of brevity.

[0053] At 702, the computer-implemented method 700 may include selecting a disposition for a physical asset of an organization based on characteristics of the physical asset and a machine learning model trained based on dispositions of other physical assets of the organization. At 704, the computer-implemented method 700 may include assigning the physical asset to a disposition, resulting in an assigned disposition.

[0054] The subject matter and operations described herein may be implemented in digital electronic circuitry, including the structures disclosed herein and their structural equivalents, or in computer software, firmware, or hardware, or any combination or combinations thereof. The subject matter described herein may be implemented as one or more components of one or more computer programs, i.e., computer program instructions encoded on a computer storage medium for execution by or to control the operation of an information / data processing apparatus. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagated signal, e.g., a machine-generated electrical, optical, or electromagnetic signal, generated to encode information / data for transmission to a suitable receiver apparatus for execution by the information / data processing apparatus. The computer storage medium may be or may be included in a computer readable storage device, a computer readable storage substrate, a random or serial access memory array or device, or any combination or combinations thereof. Furthermore, while a computer storage medium is not a propagated signal, a computer storage medium may be a source or destination of computer program instructions encoded in an artificially generated propagated signal. A computer storage medium may also be, or be contained in, one or more separate physical components or media (eg, multiple CDs, disks, or other storage devices).

[0055] 8 illustrates an exemplary computer program product (CPP) 800 that may include executable instructions that, when executed by a processor of a system, may facilitate selective disposition of physical assets, according to one or more embodiments. For purposes of brevity, descriptions of similar elements and / or processes utilized in other embodiments are omitted.

[0056] In one or more embodiments, operation 802 of Figure 8 may identify qualitative information for a predictive model of operation of the modeling system, where the predictive model is generated by modeling the modeling system based on the dataset. In one or more embodiments, operation 804 of Figure 8 may modify the predictive model based on intervention information generated based on the qualitative information, where the intervention information is associated with a prediction of the predictive model.

[0057] To provide a context for various aspects of the disclosed subject matter, Figure 9 and the following discussion are intended to provide a general description of a suitable environment in which various aspects of the disclosed subject matter may be implemented. Figure 9 illustrates a block diagram of an example of a non-limiting operating environment in which one or more embodiments described herein may be facilitated. Repeated descriptions of similar elements utilized in other embodiments described herein are omitted for the sake of brevity.

[0058] Various aspects of the disclosure are described by narrative text, flow charts, block diagrams of computer systems, and / or block diagrams of machine logic included in CPP embodiments. For any flow chart, depending on the technology involved, operations may be performed in a different order than shown in a given flow chart. For example, two operations shown in successive flow chart blocks may be performed in reverse order, as a single integrated step, simultaneously, or at least partially overlapping in time, depending also on the technology involved.

[0059] A computer program product embodiment ("CPP embodiment" or "CPP") is a term used in this disclosure to describe any set of one or more storage media (also referred to as "media") collectively contained in one or more storage devices that collectively contain machine-readable code corresponding to instructions and / or data for performing computer operations specified in a given CPP claim. A "storage device" is any tangible device that can hold and store instructions for use by a computer processor. Without limitation, a computer-readable storage medium can be an electronic storage medium, a magnetic storage medium, an optical storage medium, an electromagnetic storage medium, a semiconductor storage medium, a mechanical storage medium, or any suitable combination of the above. Some known types of storage devices that include these media include: diskettes, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static random access memory (SRAM), compact disk read-only memory (CD-ROM), digital versatile disk (DVD), memory stick, floppy disk, mechanically encoded devices (such as punch cards or pits / lands formed on the major surface of a disk), or any suitable combination of the foregoing. A computer-readable storage medium, as the term is used in this disclosure, is not to be construed as storage in the form of a temporary signal per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through a waveguide, light pulses passing through fiber optic cables, electrical signals transmitted through wires, and / or other transmission media. As will be appreciated by those skilled in the art, data is typically moved at some infrequent time during normal operation of the storage device, such as during access, defragmentation, or garbage collection, but the above does not render the storage device temporary since the data is not temporary while it is stored.

[0060] Systems and / or devices have been described (and / or will be further described) herein with respect to interactions between one or more components. Such systems and / or components may include those components or subcomponents designated therein, one or more of the designated components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components communicatively coupled to other components rather than being included within a parent component. One or more components and / or subcomponents may be combined into a single component that provides an aggregate function. A component may interact with one or more other components not specifically described herein for brevity but known to those skilled in the art.

[0061] One or more embodiments described herein may utilize hardware and / or software to solve problems that are not highly technical, abstract, and cannot be performed by a set of human mental activities. For example, a human, or even thousands of humans, cannot efficiently, accurately, and / or effectively analyze the complex characteristics of a combination of physical assets, along with different approaches to managing and disposing of such assets. And neither the human mind nor a human with pen and paper can maintain extensive and complex records of an enterprise's asset management over time.

[0062] 9 includes a computing environment 900 as an example of an environment for the execution of at least some of the computer code associated with performing the methods described herein, such as the implementation of a treatment component (e.g., treatment component 132) by treatment component execution code 980. In addition to block 980, the computing environment 900 includes, for example, a computer 901, a wide area network (WAN) 902, an end user device (EUD) 903, a remote server 904, a public cloud 905, and a private cloud 906. In this embodiment, the computer 901 includes a processor set 910 (including processing circuitry 920 and cache 921), a communication fabric 911, a volatile memory 912, a persistent storage 913 (including an operating system 922 and block 980, as shown above), a peripheral device set 914 (including a user interface (UI), a device set 923, storage 924, and an Internet of Things (IoT) sensor set 925), and a network module 915. The remote server 904 includes a remote database 930. The public cloud 905 includes a gateway 940, a cloud orchestration module 941, a set of host physical machines 942, a set of virtual machines 943, and a set of containers 944.

[0063] Computer 901 may take the form of a desktop computer, a laptop computer, a tablet computer, a smartphone, a smartwatch or other wearable computer, a mainframe computer, a quantum computer, or any other form of computer or mobile device now known or later developed that is capable of executing programs, accessing a network, or querying a database, such as remote database 930. As is well understood in the field of computer technology, and depending on the technology, execution of a computer-implemented method may be distributed among multiple computers and / or among multiple locations. However, in this description of computing environment 900, for purposes of brevity as possible, the detailed discussion focuses on a single computer, specifically computer 901. Although computer 901 is not shown in the cloud in FIG. 9, it may be located in the cloud. However, computer 901 is not required to be in the cloud except to any extent that may be categorically indicated.

[0064] The processor set 910 includes one or more computer processors of any type now known or developed in the future. The processing circuitry 920 may be distributed across multiple packages, e.g., multiple tailored integrated circuit chips. The processing circuitry 920 may implement multiple processor threads and / or multiple processor cores. The cache 921 is memory located within the processor chip package and is typically used for data or code that should be available for fast access by threads or cores executing on the processor set 910. Cache memory is typically organized into multiple levels depending on relative proximity to the processing circuitry. Alternatively, some or all of the cache for a processor set may be located "off-chip." In some computing environments, the processor set 910 may be designed to operate with qubits and perform quantum computing.

[0065] Computer readable program instructions are typically loaded onto the computer 901 to cause the processor set 910 of the computer 901 to execute a sequence of operational steps, thereby realizing a computer-implemented method, such that the instructions so executed instantiate the method specified in the flowcharts and / or descriptions of the computer-implemented methods contained herein (collectively, the "methods of the invention"). These computer readable program instructions are stored in various types of computer readable storage media, such as cache 921 and other storage media described below. The program instructions and associated data are accessed by the processor set 910 to control and direct the execution of the methods of the invention. In the computing environment 900, at least some of the instructions for executing the methods of the invention may be stored in block 980 in persistent storage 913.

[0066] Communications fabric 911 is the signal conducting pathway that allows various components of computer 901 to communicate with one another. Typically, this fabric is made of switches and conductive pathways, such as those that make up buses, bridges, physical input / output ports, etc. Other types of signal communication pathways may be used, such as fiber optic and / or wireless communication pathways.

[0067] Volatile memory 912 is any type of volatile memory now known or developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memory is characterized by random access, although this is not required unless expressly indicated. In computer 901, volatile memory 912 is located in a single package and is internal to computer 901, although alternatively or additionally, volatile memory may be distributed across multiple packages and / or located external to computer 901.

[0068] The persistent storage 913 is any form of non-volatile storage for a computer, now known or later developed. The non-volatility of this storage means that the stored data is maintained whether or not power is supplied to the computer 901 and / or directly to the persistent storage 913. The persistent storage 913 may be a read-only memory (ROM), but typically at least a portion of the persistent storage allows data to be written, data to be deleted, and data to be rewritten. Some common forms of persistent storage include magnetic disks and solid-state storage devices. The operating system 922 can be in a number of forms, such as various known proprietary operating systems or an open source Portable Operating System Interface type operating system that utilizes a kernel. The code contained in block 980 typically includes at least some of the computer code associated with performing the inventive method.

[0069] The peripheral device set 914 includes a set of peripheral devices of the computer 901. Data communication connections between the peripheral devices and other components of the computer 901 may be implemented in various manners, such as Bluetooth connections, Near Field Communication (NFC) connections, connections made by cable (such as a Universal Serial Bus (USB) type cable), insertion type connections (e.g., Secure Digital (SD) cards), connections made through a local area communication network, and even connections made through a wide area network such as the Internet. In various embodiments, the UI device set 923 may include components such as display screens, speakers, microphones, wearable devices (such as goggles and smart watches), keyboards, mice, printers, touch pads, game controllers, and haptic devices. The storage 924 is external storage, such as an external hard drive or insertable storage such as an SD card. The storage 924 may be persistent and / or volatile. In some embodiments, the storage 924 may take the form of a quantum computing storage device for storing data in the form of qubits. In embodiments where computer 901 needs to have a large amount of storage (e.g., computer 901 stores and manages a large database locally), this storage may be provided by a peripheral storage device designed to store very large amounts of data, such as a storage area network (SAN) shared by multiple geographically distributed computers. IoT sensor set 925 consists of sensors that may be used in Internet of Things applications. For example, one sensor may be a thermometer and another sensor may be a motion detector.

[0070] The network module 915 is a collection of computer software, hardware, and firmware that enables the computer 901 to communicate with other computers over the WAN 902. The network module 915 may include hardware such as a modem or Wi-Fi® signal transceiver, software for packetizing and / or depacketizing data for communication network transmission, and / or web browser software for communicating data over the Internet. In some embodiments, the network control and network forwarding functions of the network module 915 are performed on the same physical hardware device. In other embodiments (e.g., those that utilize Software-Defined Networking (SDN)), the control and forwarding functions of the network module 915 are performed on physically separate devices such that the control function manages several different network hardware devices. Computer readable program instructions for carrying out the methods of the present invention may be downloaded to the computer 901 from an external computer or external storage device, typically via a network adapter card or network interface included in the network module 915.

[0071] WAN 902 is any wide area network capable of communicating computer data over non-local distances (e.g., the Internet) by any technology now known or later developed for communicating computer data. In some embodiments, the WAN may be replaced and / or supplemented by a local area network (LAN) designed to communicate data between devices located in a local area, such as a Wi-Fi network. WANs and / or LANs typically include computer hardware such as copper transmission cables, optical transmission fiber, wireless transmission routers, firewalls, switches, gateway computers, and edge servers.

[0072] An end user device (EUD) 903 is any computer system used and controlled by an end user (e.g., a customer of the enterprise that operates computer 901) and may be of any of the forms described above in connection with computer 901. EUD 903 typically receives useful and actionable data from the operation of computer 901. For example, in a hypothetical case in which computer 901 is designed to provide recommendations to the end user, the recommendations would typically be communicated over WAN 902 from a network module 915 of computer 901 to EUD 903. In this manner, EUD 903 may display or otherwise present the recommendations to the end user. In some embodiments, EUD 903 may be a client device, such as a thin client, a heavy client, a mainframe computer, a desktop computer, and the like.

[0073] Remote server 904 is any computer system that provides at least some data and / or functionality to computer 901. Remote server 904 may be controlled and used by the same entity that operates computer 901. Remote server 904 represents a machine that collects and stores useful data available for use by other computers, such as computer 901. For example, in the hypothetical case where computer 901 is designed and programmed to provide recommendations based on historical data, this historical data may be provided to computer 901 from a remote database 930 of remote server 904.

[0074] A public cloud 905 is any computer system available for use by multiple entities that provides on-demand availability of computer system resources and / or other computer capabilities, particularly data storage (cloud storage) and computing power, without direct active management by users. Cloud computing typically utilizes sharing of resources to achieve consistency and economies of scale. The direct and active management of computing resources of the public cloud 905 is performed by computer hardware and / or software of a cloud orchestration module 941. The computing resources provided by the public cloud 905 are typically implemented by virtual computing environments running on various computers that make up the computers of the host physical machine set 942, which is a collection of physical computers in and / or available to the public cloud 905. The virtual computing environments (VCEs) typically take the form of virtual machines from the virtual machine set 943 and / or containers from the container set 944. It is understood that these VCEs can be stored as images and can be transferred between various physical machine hosts as images or after instantiation of the VCEs. Cloud orchestration module 941 manages image transfer and storage, deploys new instantiations of VCE, and manages active instantiation of VCE deployments. Gateway 940 is a collection of computer software, hardware, and firmware that enables public cloud 905 to communicate over WAN 902.

[0075] Some further description of Virtualized Computing Environments (VCEs) is provided here. A VCE may be stored as an "image." A new active instance of a VCE may be instantiated from an image. Two common types of VCEs are virtual machines and containers. A container is a VCE that uses operating system level virtualization. This refers to a feature of an operating system where the kernel allows the existence of multiple isolated user space instances, called containers. These isolated user space instances typically behave as real computers from the perspective of the programs running within them. A computer program running on a typical operating system may utilize all of the resources of that computer, such as attached devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, a program running within a container may only use the contents of the container and the devices assigned to the container. This is a feature known as containerization.

[0076] A private cloud 906 is similar to a public cloud 905, except that the computing resources are only available for use by a single enterprise. Although the private cloud 906 is shown in communication with the WAN 902, in other embodiments, the private cloud may be completely disconnected from the Internet and only accessible through a local / private network. A hybrid cloud is a composition of multiple clouds of various types (e.g., private, community, or public cloud types), often implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is bound together by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple constituent clouds. In this embodiment, both the public cloud 905 and the private cloud 906 are part of a larger hybrid cloud.

Claims

1. memory for storing computer-executable components; and a processor operatively coupled to the memory for executing the computer-executable components stored in the memory; wherein the computer executable components include: a disposition component that selects a disposition for a physical asset of the organization based on characteristics of the physical asset and a machine learning model trained based on dispositions of other physical assets of the organization; and an allocation component that allocates the physical assets to the actions, resulting in an assigned action; Including, system.

2. The system of claim 1 , wherein the characteristics include a maintenance history of the physical asset, including maintenance performed on the physical asset.

3. 2. The system of claim 1, wherein the characteristics include a material composition of the physical asset, and wherein the machine learning model is further trained based on material compositions of the other physical assets of the organization.

4. The system of claim 3 , wherein the disposition is further based on a government regulation associated with disposing of the physical asset comprised of the material composition.

5. The system of claim 3 , wherein the disposition is further based on an estimated environmental impact of disposing of the physical asset comprised of the material composition.

6. The system of claim 5 , wherein the disposition includes transporting the physical asset to a geographic location for disposal, and the estimated environmental impacts include estimated environmental impacts for the geographic location.

7. 7. The system of claim 1, wherein the computer executable components further comprise a reverse logistics component that determines reverse logistics associated with a disposition that includes transporting the physical asset back to an original supplier of the physical asset.

8. 7. The system of claim 1 , wherein the characteristics include a cost of the disposition of the physical asset to the organization, and wherein the machine learning model is further trained based on respective costs of disposition of the other physical assets of the organization.

9. The system of claim 1 , wherein the computer-executable components further comprise a disposition component that facilitates disposition of the physical asset in accordance with the assigned disposition.

10. selecting, by a device operably coupled to the processor, a disposition for a physical asset of the organization based on characteristics of the physical asset and a machine learning model trained based on dispositions of other physical assets of the organization; and allocating, by the device, the physical assets to the actions, resulting in an assigned action. A computer-implemented method comprising:

11. The computer-implemented method of claim 10 , wherein the characteristics include a maintenance history of the physical asset, including maintenance performed on the physical asset.

12. The computer-implemented method of claim 10 or 11, wherein the characteristics include a material composition of the physical asset, and the machine learning model is further trained based on a material composition of the other physical assets of the organization.

13. The computer-implemented method of claim 12 , wherein the disposition is further based on a government regulation associated with the disposition of the physical asset comprised of the material composition.

14. The computer-implemented method of claim 12 , wherein the disposition is further based on an estimated environmental impact of disposing of the physical asset comprised of the material composition.

15. 15. The computer-implemented method of claim 14, wherein the disposition includes transporting the physical asset to a geographic location for disposal, and the estimated environmental impacts include estimated environmental impacts for the geographic location.

16. 12. The computer-implemented method of claim 10 or 11, further comprising determining, by the device, reverse logistics associated with a disposition that includes transporting the physical asset back to an original supplier of the physical asset.

17. 1. A computer program for selecting a disposition for a physical asset of an organization, the computer program comprising program instructions that cause a processor to: selecting the disposition for the physical asset based on a machine learning model trained based on characteristics of the physical asset and dispositions of other physical assets of the organization; and assigning said physical assets to said actions, resulting in assigned actions; Executable by the processor to execute Computer program.

18. The computer program product of claim 17 , wherein the characteristics include a maintenance history of the physical asset, including maintenance performed on the physical asset.

19. 19. The computer program product of claim 17 or 18, wherein the characteristics include a material composition of the physical asset, and the machine learning model is further trained based on a material composition of the other physical assets of the organization.

20. 20. The computer program product of claim 19, wherein the disposition is further based on a government regulation associated with the disposal of the physical asset comprised of the material composition.

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