Selective handling of tangible assets

Abstract

Techniques related to selectively handling tangible assets are provided. For example, one or more embodiments described herein may include a system that may include a memory that may store computer executable components. The system may also include a processor operably coupled to the memory and may execute a computer executable component stored in the memory. The computer executable components may include a treatment component that may select a treatment of a tangible asset of an organization based on characteristics of the tangible asset and a machine learning model trained based on treatment of other tangible assets of the organization. The computer executable component may include a distribution component that may distribute the tangible asset to the treatment, resulting in a distributed treatment.

Description

Background Art

[0001] One or more embodiments relate to managing tangible assets of an enterprise, and more particularly, to selecting methods for disposing of tangible assets. Summary of the invention

[0002] The following presents a summary of the invention for providing a basic understanding of one or more embodiments of the present disclosure. The summary is not intended to identify key or important elements, or to delineate any scope of a particular embodiment or any scope of the claims. Its sole purpose is to present concepts in a simplified form as a prelude to a more detailed description presented later. In one or more embodiments described herein, a system, a computer-implemented method, an apparatus, and / or a computer program product can utilize an intermediate data set to generate a test diffusion model.

[0003] According to some embodiments described herein, a system is provided. The system may include a memory storing computer executable components. The system may also include a processor operably coupled to the memory, which may execute the computer executable components stored in the memory. The computer executable components may include a disposal component, which may select a disposal of the tangible asset based on characteristics of the tangible asset of the organization and a machine learning model trained based on the disposal of other tangible assets of the organization. The computer executable components may also include an allocation component, which may allocate the tangible asset to the disposal, generating an allocated disposal.

[0004] In additional or alternative embodiments, the characteristic may include a maintenance history of the tangible asset, the maintenance history including maintenance performed on the tangible asset. In additional or alternative embodiments, the characteristic may include a material composition of the tangible asset, wherein the machine learning model is further trained based on the material composition of the other tangible assets of the organization. In additional or alternative embodiments, the disposal may also be based on government regulations associated with the disposal of the tangible asset composed of the material composition. In additional or alternative embodiments, the disposal may further be based on an estimated environmental impact of disposing of the tangible asset composed of the material composition.

[0005] In additional or alternative embodiments, the disposal may include transporting the tangible asset to a geographic location for disposal, and wherein the estimated environmental impact may include the estimated environmental impact on the geographic location. In additional or alternative embodiments, the computer executable components further include a reverse logistics component that determines reverse logistics associated with the disposal that may include transporting the tangible asset back to an original supplier of the tangible asset. In additional or alternative embodiments, the characteristics may further include a cost to the organization of the disposal of the tangible asset, wherein the machine learning model is further trained based on corresponding costs of disposing of the other tangible assets of the organization. In additional or alternative embodiments, the computer executable components may further include a disposal component that facilitates the disposal of the tangible asset in accordance with the assigned disposal.

[0006] According to one or more example embodiments, a computer-implemented method is provided. The computer-implemented method may include selecting a disposition for a tangible asset of an organization based on characteristics of the tangible asset and a machine learning model trained based on dispositions of other tangible assets of the organization. The computer-implemented method may also include assigning the tangible asset to the disposition, generating an assigned disposition.

[0007] In additional or alternative embodiments, the characteristic may include a maintenance history of the tangible asset, the maintenance history including maintenance performed on the tangible asset. In additional or alternative embodiments, the characteristic may include a material composition of the tangible asset, wherein the machine learning model is further trained based on the material composition of the other tangible assets of the organization. In additional or alternative embodiments, the disposal may also be based on government regulations associated with the disposal of the tangible asset composed of the material composition.

[0008] In additional or alternative embodiments, the disposal may be further based on an estimated environmental impact of disposing of the tangible asset comprised of the material composition. In additional or alternative embodiments, the disposal may include transporting the tangible asset to a geographic location for disposal, and wherein the estimated environmental impact may include an estimated environmental impact on the geographic location. In additional or alternative embodiments, the computer-implemented method may further include determining, by the device, reverse logistics associated with the disposal that may include transporting the tangible asset back to an original supplier of the tangible asset.

[0009] According to other example embodiments, a computer program product for selectively disposing of a tangible asset. The computer program product may include a computer-readable storage medium having program instructions embodied therewith. The program instructions may be executed by a processor to cause the processor to select a disposition of the tangible asset based on characteristics of the tangible asset of an organization and a machine learning model trained based on dispositions of other tangible assets of the organization. In various embodiments, the program instructions may also include assigning the tangible asset to the disposition, generating an assigned disposition.

[0010] In additional or alternative embodiments, the characteristic may include a maintenance history of the tangible asset, the maintenance history including maintenance performed on the tangible asset. In additional or alternative embodiments, the characteristic may include a material composition of the tangible asset, wherein the machine learning model is further trained based on the material composition of the other tangible assets of the organization. In additional or alternative embodiments, the disposal may also be based on government regulations associated with the disposal of the tangible asset composed of the material composition.

[0011] Other embodiments may become apparent from the following detailed description when taken in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] In certain embodiments, the present invention is described with reference to the accompanying drawings. The drawings provided herein are intended to facilitate a clear understanding of the present invention and are not intended to limit the scope or functionality of the present invention in any way.

[0013] Figure 1 A block diagram is shown of an example system that can facilitate selective disposal of tangible assets according to one or more embodiments described herein.

[0014] Figure 2 A block diagram is shown of an example system that can facilitate selective disposal of tangible assets according to one or more embodiments described herein.

[0015] Figure 3 A block diagram is shown of an example system that can facilitate selective disposal of tangible assets according to one or more embodiments described herein.

[0016] Figure 4 A block diagram is shown of an example system that can utilize artificial intelligence / machine learning techniques to facilitate selective disposal of tangible assets in accordance with one or more embodiments described herein.

[0017] Figure 5 A block diagram is shown of an example system that can facilitate selective disposal of tangible assets according to one or more embodiments described herein.

[0018] Figure 6 A block diagram is shown of an example system that can facilitate selective disposal of tangible assets according to one or more embodiments described herein.

[0019] Figure 7 A flow chart is shown of an example non-limiting computer-implemented method that can facilitate selective disposition of tangible assets in accordance with one or more embodiments described herein.

[0020] Figure 8 An example computer program product (CPP) is depicted that may include executable instructions that, when executed by a processor of a system, may facilitate selective disposal of a tangible asset in accordance with one or more embodiments.

[0021] Fig. 9 A computing environment is included as an example of an environment for executing at least some of the computer code involved in performing the methods described herein. DETAILED DESCRIPTION

[0022] The following detailed description is merely illustrative and is not intended to limit the application or use of the embodiments and / or embodiments. In addition, it is not intended to be constrained by any expressed or implied information presented in the aforementioned background technology or summary of the invention section or the specific implementation section. One or more embodiments are now described with reference to the accompanying drawings, wherein the same reference numerals are always used to refer to the same elements. In the following description, for the purpose of explanation, many specific details are set forth in order to provide a more thorough understanding of one or more embodiments. However, it is apparent that in various cases, one or more embodiments can be practiced without these specific details.

[0023] Figure 1 A block diagram of an example system 100 that can facilitate selective disposal of tangible assets according to one or more embodiments described herein is shown. Embodiments of the systems, devices, or processes in various 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 media associated with one or more machines). When executed by one or more machines (e.g., computers, computing devices, virtual machines), such components may cause the machines to perform the described operations. For the sake of brevity, repeated descriptions of similar elements and processes employed in various embodiments are omitted.

[0024] As depicted, system 100 may include treatment system 102 that receives asset information 185 from asset characterization equipment 180 and treatment recommendations 195 from recommendation model 190. In one or more embodiments, as described below with respect to Figure 4As further discussed, the asset characterization equipment 180 may provide training data 192 to train the recommendation model 190 . In some embodiments, the treatment system 102 may include a memory 104 , a processor 106 , and computer-executable components 110 coupled to a bus 112 .

[0025] It should be noted that when an element is referred to herein as being "coupled" to another element, it may describe one or more different types of couplings. For example, when an element is referred to herein as being "coupled" to another element, it may describe one or more different types of couplings, including but not limited to chemical coupling, communication coupling, capacitive coupling, electrical coupling, electromagnetic coupling, inductive coupling, operational coupling, optical coupling, physical coupling, thermal coupling, and another type of coupling.

[0026] In one or more embodiments, the treatment system 102 may be operated using any suitable computing device or collection of computing devices that may be communicatively coupled to the device, non-limiting examples of computing devices may include, but are not limited to, a server computer, a computer, a mobile computer, a mainframe computer, an automatic 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 bridging device, a control system, or any other suitable computing device. The device may be any device that may communicate information with the treatment system 102 and / or any other suitable device that may employ information provided by the treatment system 102 and may enable computer executable components 110, as discussed below. As shown, the computer executable components 110 may include a treatment component 132, an allocation component 134, and any other components associated with the treatment system 102 that may be combined to provide the various functions described herein.

[0027] 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.) that may adopt one or more memory architectures. Fig. 9 The volatile memory 912 of FIG. 1 depicts a further example of the memory 104. Such an example of the memory 104 may be used to implement any of the embodiments described herein.

[0028] In one or more embodiments, the memory 104 may store one or more computer and machine readable, writable, and executable components and instructions that, when executed by the processor 106 (e.g., a conventional processor and a quantum processor), may perform operations defined by the executable components and instructions. For example, the memory 104 may store computer and machine readable, writable, and computer executable components 110 and instructions that, when executed by the processor 106, may perform various functions described herein in connection with the treatment system 102, including the treatment component 132, the dispensing component 134, and other components described herein with or without reference to the various figures of one or more embodiments described herein.

[0029] Processor 106 may include one or more types of processors and electronic circuits (e.g., traditional processors and quantum processors) that may implement one or more computer and machine readable, writable and executable components and instructions that may be stored on memory 104. For example, 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, etc. In some embodiments, processor 106 may include one or more central processing units, multi-core processors, microprocessors, dual microprocessors, microcontrollers, systems on a chip (SOC), array processors, vector processors, quantum processors, and another type of processor. Reference is made below to Fig. 9 910 of the processor set describes other examples of the processor 106. Such examples of the processor 106 may be employed to implement any of the embodiments described herein.

[0030] As described below, the stored data may be generated by an artificial neural network (ANN) and stored in a storage device, which may include, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, a solid-state drive (SSD) or other solid-state storage technology, a compact disk read-only memory (CD ROM), a digital video disk (DVD), a Blu-ray disk or other optical disk storage, a cassette, a magnetic tape, a magnetic disk storage or other magnetic storage device, or any other medium that may be used to store information of an embodiment and that may be accessed by a computer.

[0031] As depicted, the memory 104, the processor 106, the handling component 132, the distribution component 134, and any other components of the handling system 102 described or suggested herein can be communicatively, electrically, operatively, and optically coupled to each other via a bus 112 to perform the functions of the handling system 102 and any components coupled thereto. 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 employ a variety of bus architectures. Fig. 9 901 depicts other examples of connections similar to bus 112. Such examples of bus 112 may be used to implement any of the embodiments described herein.

[0032] In one or more embodiments described herein, the disposition system 102 can utilize a disposition component 132 to perform operations (e.g., via the processor 106) including, but not limited to, selecting a disposition for a tangible asset based on characteristics of the organization's tangible assets and a machine learning model trained based on dispositions of other tangible assets of the organization. In additional embodiments described herein, the disposition system 102 can utilize an assign component 134 to perform operations (e.g., via the processor 106) including, but not limited to, assigning a tangible asset to a disposition, generating an assigned disposition.

[0033] It should be understood that the embodiments described herein depicted in the various figures disclosed herein are for illustration only, and therefore, the architecture of these embodiments is not limited to the systems, devices, and components depicted therein. For example, in some embodiments, the treatment system 102 may also include the following parts (such as Fig. 9 ) are described in detail in connection with the various computers and computing-based elements described in the accompanying drawings. In various embodiments, components of treatment system 102 (such as treatment component 132 and dispensing 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).

[0034] Figure 2 A block diagram of an example system 200 that can facilitate selective disposal of tangible assets according to one or more embodiments described herein is shown. For the sake of brevity, repeated descriptions of similar elements employed in one or more embodiments described herein are omitted. As used herein, a diffusion model can broadly refer to a probability distribution based on a data set, and a model that can generate new samples similar to the original data set by gradually refining random guesses based on a learning-based pattern.

[0035] As shown, the system 200 includes Figure 1, wherein a reverse logistics component 232 is added to the computer executable components 110. In one or more embodiments described herein, the disposal system 102 can utilize the reverse logistics component 232 to perform operations (e.g., via the processor 106) including, but not limited to, determining reverse logistics associated with a disposal that can include transporting a tangible asset back to an original supplier of the tangible asset.

[0036] Figure 3 A block diagram of an example system 300 that can facilitate selective disposal of tangible assets according to one or more embodiments described herein is shown. For the sake of brevity, repeated descriptions of similar elements employed in one or more embodiments described herein are omitted. As used herein, a diffusion model can broadly refer to a probability distribution based on a data set, and a model that can generate new samples similar to the original data set by gradually refining random guesses based on a learning-based pattern.

[0037] As depicted, system 300 includes Figure 2 100 , wherein a disposal component 332 is added to the computer executable components 110. In one or more embodiments described herein, the disposal system 102 can utilize the disposal component 332 to perform operations (e.g., via the processor 106) including, but not limited to, facilitating the disposal of a tangible asset according to an allocated disposition, such as determined by the allocation component 134, as described above.

[0038] Figure 4 A block diagram of an example system 400 that can utilize artificial intelligence (AI) / machine learning (ML) techniques that can facilitate the selective disposal of tangible assets according to one or more embodiments described herein is shown. For the sake of brevity, repeated descriptions of similar elements employed in one or more embodiments described herein are omitted. Various embodiments herein can employ artificial intelligence and / or machine learning systems and techniques to facilitate learning user behavior, context-based scenarios, preferences, etc., in order to facilitate taking automated actions with high confidence. Utility-based analysis can be utilized to weigh the benefits of considering taking actions against the overhead of taking incorrect actions. Probability-based or statistical-based analysis can be used in conjunction with the foregoing and / or the following.

[0039] As shown, the system 400 may include an AI / ML component coupled to the processing system 102. To illustrate the various processes that may be performed by the AI / ML component 420, the AI ​​process 410 provides examples 415A-E of different types of tangible asset-related data that may be analyzed and used to train the recommendation model 190. Note that the systems and / or associated controllers, servers, or machine learning components herein may include an artificial intelligence component that may employ an AI model and / or ML model that may learn to perform the functions described above or below (e.g., via training using historical training data and / or feedback data).

[0040] 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 functionality described herein using historical training data that includes various contextual conditions corresponding to various activities associated with acquiring, maintaining, and disposing of tangible 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 functionality using training data that includes data associated with tangible assets, where such data may be collected and / or stored (e.g., in a memory) by the AI / ML component 420. In this example, such feedback data may include the various instructions described above / below, which may be input to, for example, the system herein over time in response to observed / stored context-based information.

[0041] 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 the functions described above using feedback data, performance information, and / or past performance information herein, 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).

[0042] In an embodiment, the AI / ML component 420 may perform a utility-based analysis that weighs the cost of initiating the above-described operations against the benefits. 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 415A for information about preventive maintenance, analyzing 415B for information about corrective maintenance, analyzing 415C for information about asset maintenance contracts, analyzing 415D for information about asset depreciation history, and analyzing 415E for information about material types, environmental impacts, and legal disposal regulations.

[0043] To facilitate the above functions, the AI / ML component 420 may perform classification, correlation, inference, and / or expression associated with artificial intelligence principles. For example, the AI / ML component 420 may employ an automatic classification system and / or automatic classification. In one example, the AI / ML component 420 may employ probability and / or statistical analysis (e.g., decomposition into analytical utility and cost) to learn and / or generate inferences. The AI / ML component 420 may employ any suitable machine learning-based, statistical-based, and / or probability-based techniques. For example, the AI / ML component 420 may employ 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 nonlinear training techniques, data fusion, utility-based analysis systems, systems employing Bayesian models, and the like. In another example, the AI / ML component 420 may perform a collection of machine learning calculations. For example, the AI / ML component 420 can perform a collection of clustering machine learning calculations, a collection of logistic regression machine learning calculations, a collection of decision tree machine learning calculations, a collection of random forest machine learning calculations, a collection of regression tree machine learning calculations, a collection of least squares machine learning calculations, a collection of instance-based machine learning calculations, a collection of regression machine learning calculations, a collection of support vector regression machine learning calculations, a collection of k-means machine learning calculations, a collection of spectral clustering machine learning calculations, a collection of rule learning machine learning calculations, a collection of Bayesian machine learning calculations, a collection of deep Boltzmann machine calculations, a collection of deep belief network calculations, and / or a collection of different machine learning calculations.

[0044] Figure 5 A block diagram of an example system 500 that can facilitate selective disposal of tangible assets according to one or more embodiments described herein is shown. For the sake of brevity, repeated descriptions of similar elements employed in one or more embodiments described herein are omitted.

[0045] In one or more embodiments, asset information 560 may include, but is not limited to, preventive maintenance history, corrective maintenance history, asset depreciation history, types of materials used to make up the asset, environmental impact of disposal, active maintenance contracts, current warranties, relocation history of the asset to another location, specific locations for insertion of the asset, methods for 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 are not limited to, legal / governmental regulations associated with disposal. At 510A, one or more embodiments may receive input of an asset to be managed (e.g., disposed of, reallocated, etc.). At 510B, asset information, such as asset information 560, may be retrieved from an enterprise data system.

[0046] In some embodiments, blocks 510C-G describe a process in which asset information can be analyzed and features of assets can be extracted. At 510C, asset information can be analyzed, which can include, but is not limited to, preventive maintenance, corrective maintenance, asset maintenance contracts, asset conditions, and / or other considerations. At 510D, one or more embodiments can obtain information about other enterprise branch locations and other assets similar to the assets that will be disposable. At 510E, one or more embodiments can retrieve information about different relevant provisions for asset and material disposal, for example, provisions for the disposal of assets consisting of wood or metal and batteries, etc. At 510F, one or more embodiments can generate relevant features of a specific asset based on each type of information analyzed. One or more embodiments can obtain descriptive terms via a language model. In addition, aggregate analysis of asset disposal can be used to assist in recommendations, for example, the average number of corrective / preventive maintenance, the average cost of each type of maintenance, etc. can be analyzed for each type of asset.

[0047] At 510G, one or more embodiments generate a data set for training the recommendation model 190, associating the characteristics of the asset with the results of the asset disposal and the evaluation of the method. At 510H, one or more embodiments may generate a disposal action for each asset based on the recommendation, for example, using the disposal component 332. At 510I, one or more embodiments may automatically dispatch the action, for example, by using the disposal component 332 described above. At 510J, one or more embodiments may update the inventory list 550 based on the collected and analyzed asset data.

[0048] Figure 6 A block diagram of an example system 600 that can facilitate selective disposal of tangible assets according to one or more embodiments described herein is shown. For the sake of brevity, repeated descriptions of similar elements employed in one or more embodiments described herein are omitted.

[0049] At 610, one or more embodiments may extract information, such as documented and / or stored structured information, from asset information 560 and inventory list 550. At 620A, one or more embodiments may access historical information about past assets that may be disposed of, actions taken, and asset information. At 620B, one or more embodiments may match this historical information about actions taken with regulations to be applied to the asset to be disposed of.

[0050] At 620C, one or more embodiments may collect updated information about best practices associated with the disposal of a particular asset, for example from non-governmental organizations or other associations and cooperatives. At 620E, one or more embodiments may create a database as above. Figure 4 The AI / ML model (eg, recommendation model 190 ), for example, includes but is not limited to an artificial neural network, a random forest, and / or a distributed gradient boosting library, etc. One or more embodiments may also create a knowledge graph 690 from the information collected as described above.

[0051] Figure 7 A flow chart is shown of an example non-limiting computer-implemented method 700 that can facilitate selective disposal of tangible assets in accordance with one or more embodiments described herein. For the sake of brevity, repeated descriptions of similar elements and processes employed in various embodiments are omitted.

[0052] At 702, the computer-implemented method 700 may include selecting a disposition for a tangible asset based on characteristics of the organization's tangible assets and a machine learning model trained based on dispositions of other tangible assets of the organization. At 704, the computer-implemented method 700 may include assigning the tangible asset to a disposition, generating an assigned disposition.

[0053] The embodiments of the subject matter and operations described herein may be implemented in digital electronic circuits, or in computer software, firmware or hardware, which includes the structures disclosed in this specification and their structural equivalents, or a combination of one or more of them. The embodiments of the subject matter described herein may be implemented as one or more computer programs, i.e., one or more components of computer program instructions, which are encoded on a computer storage medium for execution by an information / data processing device or for controlling the operation of an information / data processing device. Alternatively or additionally, the program instructions may be encoded on an artificially generated propagation signal (e.g., a machine-generated electrical, optical or electromagnetic signal), which is generated to encode information / data for transmission to a suitable receiver device for execution by an information / data processing device. The computer storage medium may be a computer-readable storage device, a computer-readable storage substrate, a random or serial access memory array or device, or a combination of one or more of them, or included therein. In addition, although a computer storage medium is not a propagation signal, a computer storage medium may be a source or destination of computer program instructions encoded in an artificially generated propagation signal. The computer storage media may also be, or be included in, one or more separate physical components or media (eg, multiple CDs, disks, or other storage devices).

[0054] Figure 8 An example 800 computer program product (CPP) is depicted that may include executable instructions that, when executed by a processor of a system, may facilitate selective disposal of tangible assets in accordance with one or more embodiments. For the sake of brevity, descriptions of similar elements and / or processes employed in other embodiments are omitted.

[0055] In one or more embodiments, Figure 8 Operation 802 may identify qualitative information related to a predictive model of the operation of the modeled system, wherein the predictive model is generated by modeling the modeled system based on the data set. In one or more embodiments, Figure 8 Operation 804 may modify the predictive model based on intervention information generated based on the qualitative information, wherein the intervention information is associated with a prediction of the predictive model.

[0056] To provide context for various aspects of the disclosed subject matter, Fig. 9 The following discussion is intended to provide a general description of a suitable environment in which various aspects of the disclosed subject matter may be implemented. Fig. 9A block diagram of an example non-limiting operating environment in which one or more embodiments described herein may be facilitated is shown. For the sake of brevity, repeated descriptions of similar elements employed in other embodiments described herein are omitted.

[0057] Various aspects of the present disclosure are described by narrative text, flow charts, block diagrams of computer systems, and / or block diagrams of machine logic included in CPP embodiments. With respect to any flow chart, depending on the technology involved, the operations may be performed in an order different from the order shown in a given flow chart. For example, again depending on the technology involved, two operations shown in successive flow chart blocks may be performed in reverse order, as a single integrated step, simultaneously, or in a manner that at least partially overlaps in time.

[0058] Computer program product embodiments ("CPP embodiments" or "CPP") are terms used in this disclosure to describe any collection of one or more storage media (also referred to as "media") collectively included in a collection of one or more storage devices that collectively include machine-readable code corresponding to instructions and / or data for performing the computer operations specified in a given CPP claim. A "storage device" is any tangible device that can retain and store instructions for use by a computer processor. Without limitation, a computer-readable storage medium may 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 foregoing. Some known types of storage devices that include these media include: magnetic disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), static random access memories (SRAM), compact disk read-only memories (CD-ROM), digital versatile disks (DVD), memory sticks, floppy disks, mechanical encoding devices (such as punch cards or pits / ridges formed in a major surface of a disk), or any suitable combination of the foregoing. As the term is used in this disclosure, computer-readable storage media should not be construed as storage devices in the form of transient signals themselves (such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides, light pulses passing through fiber optic cables, electrical signals transmitted through wires, and / or other transmission media). As those skilled in the art will understand, data is usually moved at some occasional points in time during normal operation of the storage device (such as during access, defragmentation, or garbage collection), but this does not make the storage device transient because the data is not transient when stored.

[0059] This paper has (and / or will further) describe systems and / or devices with respect to the interaction between one or more components. Such systems and / or components may include those components or subcomponents specified therein, one or more of the specified components and / or subcomponents, and / or additional components. Subcomponents may be implemented as components that are communicatively coupled to other components rather than being included in a parent component. One or more components and / or subcomponents may be combined into a single component that provides aggregate functionality. For the sake of brevity, a component may interact with one or more other components that are not specifically described herein but are known to those skilled in the art.

[0060] One or more embodiments described herein may employ hardware and / or software to solve problems that are highly technical, not abstract, and cannot be performed as a collection of human mental actions. For example, a person or even thousands of people cannot efficiently, accurately, and / or effectively analyze the complex nature of a portfolio of tangible assets and the different methods of managing and disposing of those assets. Also, neither the human brain nor a person with pen and paper can maintain the massive and complex records of enterprise asset management over time.

[0061] Fig. 9 A computing environment 900 is included as an example of an environment for executing at least some of the computer codes involved in performing the methods described herein, such as implementing a disposal component (e.g., disposal component 132) through a disposal component execution code 980. In addition to the 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 a processing circuit 920 and a cache 921), a communication structure 911, a volatile memory 912, a permanent storage device 913 (including an operating system 922 and the block 980, as described above), a peripheral device set 914 (including a user interface (UI), a device set 923, a storage device 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 host physical machine set 942 , a virtual machine set 943 , and a container set 944 .

[0062] Computer 901 may take the form of a desktop computer, laptop computer, tablet computer, smart phone, smart watch or other wearable computer, mainframe computer, quantum computer, or any other form of computer or mobile device now known or to be developed in the future that is capable of running 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, the execution of the computer-implemented method may be distributed among multiple computers and / or among multiple locations. On the other hand, in this presentation of computing environment 900, the detailed discussion focuses on a single computer (particularly computer 901) to keep the presentation as simple as possible. Computer 901 may be located in the cloud, even if it is not in the cloud. Fig. 9 On the other hand, except to the extent that can be indicated with certainty, computer 901 need not be in the cloud.

[0063] The processor set 910 includes one or more computer processors of any type known now or developed in the future. The processing circuit 920 can be distributed over multiple packages (e.g., multiple coordinated integrated circuit chips). The processing circuit 920 can implement multiple processor threads and / or multiple processor cores. The cache 921 is a memory located in (one or more) processor chip packages, and is generally used for data or code that should be available for fast access by threads or cores running on the processor set 910. Cache memory is generally organized into multiple levels according to relative proximity to the processing circuit. Alternatively, some or all of the caches for the processor set may be located "off-chip". In some computing environments, the processor set 910 may be designed to work with quantum bits and perform quantum computing.

[0064] Computer readable program instructions are typically loaded onto the computer 901 to cause a series of operating steps to be performed by the processor set 910 of the computer 901, thereby implementing a computer-implemented method, so that the instructions executed thereby will instantiate the method specified in the flowchart and / or narrative description of the computer-implemented method included in this document (collectively referred to as the "inventive method"). These computer readable program instructions are stored in various types of computer-readable storage media, such as cache 921 and other storage media discussed below. The program instructions and associated data are accessed by the processor set 910 to control and direct the execution of the inventive method. In the computing environment 900, at least some of the instructions for performing the inventive method may be stored in a permanent storage device 913 in a box 980.

[0065] The communication fabric 911 is a signal conduction path that allows the various components of the computer 901 to communicate with each other. Typically, the fabric is made of switches and conductive paths, such as those that make up a bus, a bridge, physical input / output ports, etc. Other types of signal communication paths may be used, such as fiber optic communication paths and / or wireless communication paths.

[0066] 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, but this is not required unless explicitly indicated. In computer 901, volatile memory 912 is located in a single package and is internal to computer 901, but alternatively or additionally, volatile memory can be distributed over multiple packages and / or located externally relative to computer 901.

[0067] Permanent storage device 913 is any form of non-volatile storage device for computers known now or to be developed in the future. The non-volatility of the storage device means that the stored data is maintained regardless of whether the computer 901 and / or the permanent storage device 913 is powered directly. Permanent storage device 913 can be a read-only memory (ROM), but usually at least a portion of the permanent storage device allows writing data, deleting data, and rewriting data. Some common forms of permanent storage devices include disks and solid-state storage devices. Operating system 922 can take several forms, such as various known proprietary operating systems or open source portable operating system interface type operating systems using a kernel. The code included in frame 980 generally includes at least some of the computer codes involved in executing the method of the present invention.

[0068] The peripheral device set 914 includes a peripheral device set of the computer 901. The data communication connection between the peripheral device and other components of the computer 901 can be implemented in various ways, such as a Bluetooth connection, a near field communication (NFC) connection, a connection by a cable (such as a universal serial bus (USB) type cable), a plug-in type connection (e.g., a secure digital (SD) card), a connection through a local area network, and even a connection through a wide area network such as the Internet. In various embodiments, the UI device set 923 may include components such as a display screen, a speaker, a microphone, a wearable device (such as goggles and smart watches), a keyboard, a mouse, a printer, a touchpad, a game controller, and a tactile device. The storage device 924 is an external storage device (such as an external hard drive) or a pluggable storage device (such as an SD card). The storage device 924 can be permanent and / or volatile. In some embodiments, the storage device 924 can take the form of a quantum computing storage device for storing data in the form of quantum bits. In embodiments where it is necessary for computer 901 to have a large amount of storage (e.g., where computer 901 locally stores and manages a large database), the 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 collection 925 consists of sensors that can be used in IoT applications. For example, one sensor may be a thermometer, while another sensor may be a motion detector.

[0069] The network module 915 is a collection of computer software, hardware, and firmware that allows the computer 901 to communicate with other computers via the WAN 902. The network module 915 may include hardware such as a modem or a Wi-Fi signal transceiver, software for packetizing and / or depacketizing data for communication network transmission, and / or web browser software for transmitting data via the Internet. In some embodiments, the network control function and the network forwarding function of the network module 915 are executed on the same physical hardware device. In other embodiments (e.g., embodiments utilizing software defined networks (SDN)), the control function and forwarding function of the network module 915 are executed on physically separated devices, so that the control function manages several different network hardware devices. Computer-readable program instructions for executing the method of the present invention can typically be downloaded to the computer 901 from an external computer or an external storage device via a network adapter card or a network interface included in the network module 915.

[0070] WAN 902 is any wide area network (e.g., the Internet) capable of transmitting computer data over non-local distances by any technology now known or to be developed in the future for transmitting computer data. In some embodiments, the WAN may be replaced and / or supplemented by a local area network (LAN) designed to transmit data between devices located in a local area (such as a Wi-Fi network). A WAN and / or LAN typically includes computer hardware such as copper transmission cables, optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and edge servers.

[0071] End-user device (EUD) 903 is any computer system used and controlled by an end-user (e.g., a customer of an enterprise operating computer 901), and may take any of the forms discussed above in connection with computer 901. EUD 903 typically receives helpful and useful data from the operation of computer 901. For example, in the hypothetical case where computer 901 is designed to provide recommendations to an end-user, the recommendations would typically be delivered to EUD 903 from network module 915 of computer 901 via WAN 902. 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, or the like.

[0072] 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(s) that collects and stores helpful and useful data 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, then that historical data may be provided to computer 901 from remote database 930 of remote server 904.

[0073] The public cloud 905 is any computer system that is available to multiple entities and provides on-demand availability of computer system resources and / or other computer capabilities, especially data storage (cloud storage) and computing capabilities, without the need for direct active management by users. Cloud computing generally uses the sharing of resources to achieve consistency and economies of scale. The direct and active management of the computing resources of the public cloud 905 is performed by the computer hardware and / or software of the cloud orchestration module 941. The computing resources provided by the public cloud 905 are generally implemented by virtual computing environments running on various computers that constitute the host physical machine set 942, which is the totality of physical computers in and / or available for the public cloud 905. The virtual computing environment (VCE) is generally in the form of a virtual machine from a virtual machine set 943 and / or a container from a container set 944. It should be understood that these VCEs can be stored as images and can be transmitted between various physical machine hosts as images or after the instantiation of the VCE. The cloud orchestration module 941 manages the transmission and storage of images, deploys new instantiations of VCEs, and manages active instantiations of VCE deployments. Gateway 940 is a collection of computer software, hardware, and firmware that allows public cloud 905 to communicate over WAN 902 .

[0074] Some further explanation of virtualized computing environments (VCEs) will now be provided. A VCE can be stored as an "image". A new active instance of a VCE can be instantiated from an image. Two common types of VCEs are virtual machines and containers. Containers are VCEs that use operating system-level virtualization. This refers to an operating system feature where the kernel allows the existence of multiple isolated user space instances called containers. From the perspective of the programs running in them, these isolated user space instances typically behave like real computers. Computer programs running on a normal operating system can utilize all of the resources of that computer, such as connected devices, files and folders, network shares, CPU power, and quantifiable hardware capabilities. However, programs running within a container can only use the contents of the container and the devices assigned to the container, a feature known as containerization.

[0075] Private cloud 906 is similar to public cloud 905, except that the computing resources are only available to a single enterprise. Although private cloud 906 is depicted as communicating with WAN 902, in other embodiments, the private cloud may be completely disconnected from the Internet and accessible only through a local / private network. A hybrid cloud is a combination of multiple clouds of different types (e.g., private, community, or public cloud types), typically implemented by different vendors. Each of the multiple clouds remains a separate and discrete entity, but the larger hybrid cloud architecture is tied together by standardized or proprietary technologies that enable orchestration, management, and / or data / application portability between the multiple constituent clouds. In this embodiment, both public cloud 905 and private cloud 906 are part of a larger hybrid cloud.

Claims

1. A system comprising: a memory storing computer executable components; as well as a processor operably coupled to the memory, the processor executing the computer-executable components stored in the memory, wherein the computer-executable components include: a disposition component that selects a disposition of a tangible asset of an organization based on characteristics of the tangible asset and a machine learning model trained based on dispositions of other tangible assets of the organization, and An allocation component allocates the tangible asset to the disposition, generating an allocated disposition.

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

3. The system of claim 1, wherein the characteristic comprises a material composition of the tangible asset, wherein the machine learning model is also trained based on the material composition of the other tangible assets of the organization.

4. The system of claim 3, wherein the disposal is further based on government regulations associated with disposal of the tangible asset comprised of the material component.

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

6. The system of claim 5, wherein the disposing comprises transporting the tangible asset to a geographic location for disposal, and wherein the estimated environmental impact comprises an estimated environmental impact on the geographic location.

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 including transporting the tangible asset back to an original supplier of the tangible asset.

8. A system according to claim 1, wherein the characteristics include the cost of the disposal of the tangible asset to the organization, and wherein the machine learning model is also trained based on the corresponding cost of disposing of the other tangible 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 tangible asset in accordance with the assigned disposition.

10. A computer-implemented method comprising: selecting, by a device operably coupled to the processor, a disposition of the tangible asset based on characteristics of the tangible asset of the organization and a machine learning model trained based on dispositions of other tangible assets of the organization, and The tangible asset is allocated, by the device, to the disposition, resulting in an allocated disposition.

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

12. The computer-implemented method of claim 10, wherein the characteristics include material composition of the tangible asset, wherein the machine learning model is also trained based on material composition of the other tangible assets of the organization.

13. The computer-implemented method of claim 12, wherein the disposing is further based on governmental regulations associated with disposing of the tangible asset comprised of the material component.

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

15. The computer-implemented method of claim 14, wherein the disposing comprises transporting the tangible asset to a geographic location for disposal, and wherein the estimated environmental impact comprises an estimated environmental impact on the geographic location.

16. The computer-implemented method of claim 10, wherein the computer-implemented method further comprises determining, by the device, reverse logistics associated with a disposition including transporting the tangible asset back to an original supplier of the tangible asset.

17. A computer program product for selecting a disposition for a tangible asset of an organization, the computer program product comprising a computer readable storage medium having program instructions embodied therewith, the program instructions executable by a processor to cause the processor to: selecting a disposition of the tangible asset based on characteristics of the tangible asset and a machine learning model trained based on dispositions of other tangible assets of the organization, and The tangible asset is allocated to the disposition, resulting in an allocated disposition.

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

19. The computer program product of claim 17, wherein the characteristic comprises a material composition of the tangible asset, wherein the machine learning model is also trained based on the material composition of the other tangible assets of the organization.

20. The computer program product of claim 19, wherein the disposing is further based on governmental regulations associated with disposing of the tangible asset comprised of the material composition.

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