Multifactor-based decision support algorithm

Abstract

The present invention relates to a method for making decisions regarding intellectual property assets through a multi-factor decision-driven algorithm, and in some embodiments, a method for acting on that decision. The algorithm may be based on internally and / or externally defined criteria, which may be predefined or may be updated on the fly based on said internally or externally defined criteria. The algorithm may then return a decision or set of decisions to a user, who may choose to take action based on said decisions.

Description

[Technical Field]

[0001] The present invention relates generally to the use of algorithms to assist in decision making based on a variety of configurable factors, and more particularly to algorithm-based decision making in the handling of intellectual property. [Background technology]

[0002] There are countless intellectual property decisions that legal or business professionals must make. These include, but are not limited to, whether to pay an annuity on a particular asset or group of assets (e.g., a patent portfolio), which countries to file first, and which foreign authorities to file with. These decisions are often made very quickly because there is so much to decide and not enough time to thoroughly review the available data. IP professionals must make their "best assumption" decisions in seconds or minutes, without the time to thoroughly review the assets. 。

[0003] Every day, decisions are made that involve experts reviewing large amounts of data and making their best educated guesses about future actions based on that data. This data can take many forms and may include internal data sources, external data sources, or both. Either way, decision makers are always looking for ways to increase not only the data that informs their decisions, but also how to combine that data and the speed with which decisions can be made based on that data. And sometimes, decision makers only have seconds or minutes to make decisions that could result in millions of dollars or more in profit or loss. Summary of the Invention [Problem to be solved by the invention]

[0004] Currently, most decisions are made using spreadsheets where a team or individual looks at the spreadsheet and decides what to do with a particular asset based on their knowledge of the data or their intuition. Sometimes, it's just a gut feeling that an asset meets the criteria. Having too little information about an asset can lead to rash decisions, as the decision maker may miss important information that could lead to a different decision. On the other hand, too much information can lead to poor judgment, just like too little information, because too much information can obscure what is most important, cause certain data points to be missed, and many other sources of human error.

[0005] A method including an algorithm, which can be implemented by using internally defined data points, externally defined IP assets or groups of IP assets based on the data points identified, or a combination thereof make one or more decisions regarding the group and at least one predefined data point and making one or more decisions based on the one or more users. Display the settings.

[0006] This is the concept of multi-factor decision support algorithm (hereinafter referred to as "algorithm") software. This algorithm, which supports decision makers in the decision-making process, is designed to make the decision-making process more streamlined and easier by using one or more elements that assist the decision maker in the entire process of making a decision, whatever the workflow or decision.

[0007] There are many aspects of data that can be useful for algorithmic decision support. It's important to weight and understand that data and base it on other data. It's also important to have an algorithm that can sort and make decisions based on industry best practices, competition, budgets, roadmaps, or any of the myriad other data points that a decision support algorithm can weight, discard, or search for as needed, to at least provide feedback on the low-hanging fruit, but also on more complex decisions that require experts to make. [Effects of the Invention]

[0008] The multi-factor based decision support algorithm (hereinafter "Algorithm") of the present invention If we do, we will at least get feedback on the low hanging fruit, and not just on the issues that experts are working on. You can also get feedback on more complex decisions that require together with one or more elements that support the overall process for decision-making by the decision maker This can make the decision-making process more streamlined and easier. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 illustrates an example of a network of data processing systems. [Figure 2] FIG. 1 illustrates an example flowchart showing an information input process for receiving a decision by a decision support algorithm. [Figure 3] FIG. 1 illustrates an example flowchart illustrating a process for prompting decisions and further actions based on a decision support algorithm. DETAILED DESCRIPTION OF THE INVENTION

[0010] The following detailed description is merely illustrative and is not intended to be limiting of the scope of the present invention. Furthermore, it is not intended to limit the scope of the present invention or its uses, nor is it limited by any express or implied information presented in the preceding background or summary or detailed description.

[0011] One or more embodiments will now be described with reference to the drawings, in which like reference numerals refer to like elements. In the following description, numerous specific details are set forth in order to provide a more thorough understanding of one or more embodiments. It will be apparent, however, that one or more embodiments may be practiced without these specific details.

[0012] In the exemplary embodiment shown in Figure 1, an IP professional must make a decision about a set of intellectual assets. In some embodiments, the IP professional may be an attorney, paralegal, other member of legal staff, a business manager working on a portfolio, or any other person who must make decisions about intellectual assets in some way. In some embodiments, the intellectual asset or set of intellectual assets of interest may be a patent, trademark, trade secret, or anything else that someone in the IP world would understand as an intellectual asset.

[0013] In this embodiment, an IP professional uses system 100 to expedite decision making. In this embodiment, the IP professional uses client 120 to make an algorithm request to server 130 over network 110. Server 130 executes the decision-making algorithm in response to the request from the IP professional using data stored on server 130 itself, data stored on storage 140 over network 110, or data stored both on server 130 and storage 140.

[0014] In some embodiments, the data on storage 140 and server 130 may be internal data. In other embodiments, the data on storage 140 and server 130 may be external data. In still other embodiments, there may be a mix of internal and external data.

[0015] 1, there may be multiple storages 140, or servers 130, or both. In some exemplary embodiments, internal data, external data, or a mixture of these data may be stored in one or more locations and accessed by one or more servers 130.

[0016] As an example, an IP professional may need to make a decision about a set of patents (hereinafter a "patent portfolio"), or about whether a company should pay an annuity or maintenance fee on a patent portfolio. In this embodiment, the IP professional may only have a few minutes to make a decision about each asset in the patent portfolio, and may have to make hundreds of decisions on a single portfolio.

[0017] In this embodiment, the algorithm stored on server 130 is associated with specific data stored in storage 140 and accessible to the company. Each piece of data that the IP professional determines is useful to the company is added to storage 140, and the decision-making algorithm on server 130 examines the data points and assigns a specific weighting to each data point depending on the company's anticipated business roadmap. In this embodiment, the IP professional does not have access through client 120 to what those weightings are. In some embodiments, the IP professional must input some or all of the data used by the algorithm, such as the business roadmap on which the decision is based. In yet other embodiments, the IP professional does not input any of the data used by the algorithm. In this embodiment, the IP professional requests through client 120 that the algorithm be run against a specific patent portfolio determined and input by a user within the company.

[0018] In this exemplary embodiment, the algorithm determines that a certain percentage (e.g., 20%) should be annuity-paid and a certain percentage (e.g., 30%) should be forfeited, and server 130 displays an indicator (e.g., a green thumbs-up for payment and a red thumbs-down for forfeit) on client 120 over network 110. In this example, the IP professional uses the software enough to trust the algorithm and clicks a selector to approve all returned decisions. This approval is returned over network 110, instructing the algorithm or another program on server 130 to update the portfolio with the requested action. This allows the IP professional to spend more time on the remaining 50% of the patent portfolio that the algorithm determined required user intervention and review.

[0019] In some related embodiments, if the IP professional selects only a portion of the returned decisions, the approved decisions are returned over network 110 and instruct an algorithm or another program on server 130 to update the portfolio with the requested action. In some embodiments, the additional decisions that were not approved are also updated to reflect that they were not approved. In yet other embodiments, the additional decisions that were not approved are ignored and no further updates are made by server 130.

[0020] In another exemplary embodiment, a series of decisions need to be made about a set of patents, trademarks, trade secrets, etc. (hereinafter, a "portfolio"), such as, but not limited to, patents. In this embodiment, there may be different data points required or needed for different aspects of the portfolio, so the algorithm has multiple weights and sections of data needed for different parts of the portfolio. In this embodiment, an IP expert, one or more business experts, a financial analyst, and / or other people within the company collaborate to determine the data points and the weightings for the various data points. These data points are updated with data stored on server 130, storage 140, etc.

[0021] In this embodiment, the portfolio may be automatically determined by, but not limited to, date, budget, business unit, manufacturing location, internal hierarchy, or people participating in the meeting where the decision is made, such as subject matter experts (SMEs) who handle specific aspects of the portfolio. In this embodiment, the algorithm runs and displays a percentage of the decisions made by the algorithm, allowing the decision maker to view and choose to process some, all, or none. For example, the algorithm may return a list of items for which the decision maker can click one or all and decide whether to perform an action, such as paying a pension or filing in a particular country. Once a decision is made, the remaining items on the list may be automatically returned to the portfolio for discussion, or the decision maker may select one or more items based on the algorithm's findings and weightings and leave them for the next round of decisions.

[0022] In some embodiments, the factors are market research factors included in the basic algorithm. For example, these market research factors may include external data stored in storage 140 or another external storage device about what competitors are doing in a particular technology. These may include abandoning more assets related to one technology than assets related to several other technologies before paying maintenance fees on a particular patent or group of patents within that technology, or shifting the focus of the filing to a particular technology, country, etc., or to corporate decisions or strategies in a particular technology field. These external storage points may be accessed on the fly by server 130 and used on the fly to make the algorithmically weighted decision.

[0023] In other embodiments, external data points stored on a device such as storage 140 may be unknown to the IP professional and the algorithm until the IP professional accesses the algorithm using client 120. 。

[0024] child In embodiments, artificial intelligence (AI), machine learning (ML), or other processes determine whether previously used, never used, or a combination thereof are the most appropriate data points for making the decision requested through client 120. In such embodiments, some data points may be predetermined and other data points may be determined on the fly without prior configuration or determination. These additional decision data points may be determined based on, but are not limited to, scientific papers, white papers, competitor data, competitor business plans, the user's business plan, forecasting tools, budgets, cognitive computing, etc.

[0025] In an exemplary embodiment, the algorithm is utilized through internal and / or external data. A learning algorithm determines the factors it uses to make a decision based on possible trends and other data. For example, an algorithm may be configured to determine the optimal foreign filing strategy to block a competitor. Based on these initial factors, the algorithm may then review available public data, determine that a competitor is attempting to enter a particular foreign market, and return a decision to file in that market before the competitor. This decision may be returned by an IP professional requesting a decision on a set of assets, or by the algorithm being configured to alert, regardless of whether a user has requested its use. In this example, the algorithm may run continuously or be configured to scan available data points at specific intervals and return a user decision, regardless of whether an IP professional or another user has directly requested it.

[0026] In some embodiments, factors are weighted according to their predicted impact on the portfolio. In one example, an algorithm, in response to a competitor's business decision, determines that abandoning one of the assets submitted to it would save $20,000 over the asset's remaining life and would not affect sales. In this example, the algorithm determines that the asset should be abandoned and returns that decision to the IP professional. In another example, the algorithm determines that abandoning the asset would make the company vulnerable to competitors and could result in a loss of $500,000 in sales for the year, and determines that the company would need to pay maintenance fees to maintain the asset in order to maintain market power. In yet another related example, the algorithm determines that filing a trademark in a particular country would prevent generic drugs from copying the particular product associated with the trademark, allowing the company to continue selling in that particular country, and returns a decision to file as soon as possible in that country to reduce competition risk.

[0027] Some embodiments may include all or some of the above embodiments. Other embodiments may include, but are not limited to, other types of decision makers, other types of data such as budgets, forecasts, market, or competitive data, other types of forms or decision trees, or other types of visual cues for decision makers (such as color coding, sorting, automatically shifting all or certain portions of the portfolio to other new or previously created portfolios, or automated algorithmic actions in response to algorithmic findings).

[0028] The present invention may be a system, method, and / or computer program product in its integration at any possible level of technical detail. , may include a computer-readable storage medium (or media) containing computer-readable program instructions for causing a processor to carry out embodiments of the present invention.

[0029] A computer-readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution apparatus. A computer-readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or a suitable combination thereof. A non-exhaustive list of more specific examples of computer-readable storage media includes the following: portable computer 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), portable computers, and the like. Compact Disc Read-Only Memory (CD-ROM), Digital Versatile Disk (DVD), Memory Stick, Floppy Disk, punch card or groove recorded life The term "computer-readable storage medium" as used herein should not be construed as a transitory signal itself, such as a radio wave or other freely propagating electromagnetic wave, an electromagnetic wave propagating through a waveguide or other transmission medium (e.g., a light pulse passing through a fiber optic cable), or an electrical signal transmitted through a wire.

[0030] The computer-readable program instructions described herein may be downloaded from a computer-readable storage medium to each computing / processing device. or downloaded to an external computer or external storage device via the Internet, a local area network, a wide area network, and / or a wireless network. A network may consist of copper transmission cables, optical fiber transmissions, wireless transmissions, routers, firewalls, switches, gateway computers, and / or Configure the network adapter for each computing / processing device. The computer card or network interface receives computer-readable program instructions from the network and transmits them to the computer within each computing / processing device.

[0031] Computer readable program instructions for carrying out the operations of the present invention may include assembler instructions, instruction set architecture (ISA) instructions, machine instructions, machine dependent instructions, microprocessor instructions, etc. Crowd code, firmware instructions, state setting data, configuration data for integrated circuits, or source or object code written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Smalltalk, C++, C#, procedural programming languages ​​(such as the "C" programming language or similar programming languages) , or other types of programming languages ​​such as Angular or ReactJS. The computer-readable program instructions may be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer, partially on a remote computer or server, or entirely on a remote computer or server. do.

[0032] rear In the user scenario, the remote computer is either on a local area network (LAN) or The user's computer can be accessed over any type of network, including a network or wide area network (WAN). The electronic circuitry may be connected to a computer, or may be connected to an external computer over the internet, for example, using an internet service provider (). In some embodiments, electronic circuitry, including programmable logic circuits, field programmable gate arrays (FPGAs), or programmable logic arrays (PLAs), may be computer readable. The state information of the program instructions may be used to personalize the electronic circuitry to execute the instructions.

[0033] Embodiments of the present invention are described herein with reference to flowchart illustrations and / or block diagrams of apparatuses (systems) and computer program products. It will be readily understood that each block of the flowcharts and / or block diagrams, and combinations of blocks in the flowcharts and / or block diagrams, can be implemented by computer-readable program instructions. It will also be understood that the diagrams are not intended to limit the invention, but rather are intended merely as some possible exemplary embodiments of the invention.

[0034] These computer-readable program instructions are provided to a processor of a general-purpose computer, special-purpose computer, or other programmable data processing apparatus and executed by the processor of the computer or other programmable data processing apparatus to generate means for performing the functions / acts indicated in the blocks or blocks of the flowcharts and / or block diagrams. These computer-readable program instructions are particularly useful for controlling the computer, programmable data processing apparatus, and / or other devices. The flowcharts and / or block diagrams may also be stored on a computer-readable storage medium that can be used to perform the functions of the flowcharts and / or block diagrams. Or, it includes a product including instructions that implement the functions / operations shown in the blocks.

[0035] The computer-readable program instructions are loaded into a computer, other programmable data processing apparatus, or other device and cause a series of operational steps to be performed on the computer, other programmable apparatus, or other device, thereby creating a computer-implemented process, and the instructions executed on the computer, other programmable apparatus, or other device may be represented as blocks or portions of the flowcharts and / or block diagrams. performs the functions / operations indicated by the blocks.

[0036] The flowcharts and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of the present invention. In this regard, each block in the flowcharts or block diagrams may represent a module, segment, or portion of instructions that includes one or more executable instructions for implementing the specified logical function(s). In some alternative implementations, the functions noted in the blocks may occur in a different order than noted in the figures, or other functions not shown may be added to the implementation. do.

[0037] example For example, two blocks that appear consecutively may actually execute at approximately the same time. Alternatively, the blocks may be executed in reverse order depending on the functionality involved. It should also be noted that each block of the block diagrams and / or flowcharts, and combinations of steps in the block diagrams and / or flowcharts, can be implemented by a special purpose hardware-based system that performs the specified functions or operations, or a system that executes a combination of special purpose hardware and computer instructions.

[0038] The description of various embodiments of the present invention has been presented for purposes of explanation, but is not intended to be limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein has been selected to best explain the principles of the embodiments, practical applications or technical improvements to technology found in the market, or to facilitate the understanding of the embodiments described herein by those skilled in the art.

Claims

1. A method comprising an algorithm, Based on internally defined data points, externally defined data points, or a combination Make one or more decisions regarding an intellectual property asset or group of intellectual property assets; making one or more decisions based on at least one predefined data point; A method for displaying one or more decisions to one or more users.

2. the predefined one or more data points for the one or more decisions are updated after one or more decisions are requested by a user; 10. The method of claim 1, wherein the predefined one or more data points are updated through a user, additional data, or a decision previously requested by the user, and wherein these processes are performed using artificial intelligence, machine learning, or a combination thereof.

3. 2. The method of claim 1, further comprising the step of a user performing an action based on detections by said algorithm to update said intellectual property or group of intellectual property.

4. 2. The method of claim 1, wherein said algorithm determines whether an annuity or renewal fee is due for said intellectual asset or group of intellectual assets.

5. 10. The method of claim 1, wherein the algorithm weights each data point or each plurality of data points based on one or more of the company's projected business roadmap, costs, budget, manufacturing footprint, market factors, comments made in internal meetings, or a combination thereof.

6. 10. The method of claim 1, wherein the algorithm weights each data point or each plurality of data points based on their expected effect on the portfolio.

7. 2. The method of claim 1, wherein said algorithm determines a rate at which decisions are made for said intellectual asset or group of intellectual assets.

8. a processor; 1. A system comprising a memory in communication with a processor and containing program instructions that, when executed by the processor, are configured to cause the processor to perform a method, The method comprises: Based on internally defined data points, externally defined data points, or a combination Make one or more decisions regarding an intellectual property asset or group of intellectual property assets; making one or more decisions based on at least one predefined data point; A system for displaying one or more decisions to one or more users.

9. the predefined one or more data points for the one or more decisions are updated after one or more decisions are requested by a user; 10. The system of claim 8, wherein the predefined one or more data points are updated through a user, additional data, or decisions previously requested by the user, and wherein these processes are performed using artificial intelligence, machine learning, or a combination thereof.

10. 10. The system of claim 8, further comprising a process for a user to perform an action based on detections by said algorithm to update said intellectual property or group of intellectual property.

11. 9. The system of claim 8, wherein said algorithm determines whether an annuity or renewal fee is due for said intellectual property or group of intellectual property.

12. 10. The system of claim 8, wherein the algorithm weights each data point or each plurality of data points based on one or more of the company's projected business roadmap, costs, budget, manufacturing footprint, market factors, comments made in internal meetings, or a combination thereof.

13. 9. The system of claim 8, wherein the algorithm weights each data point or each plurality of data points based on their expected effect on the portfolio.

14. 9. The system of claim 8, wherein said algorithm determines a rate at which decisions are made for said intellectual property or group of intellectual property.

15. One or more computer-readable media containing program instructions, The above program instructions are: Based on internally defined data points, externally defined data points, or a combination Make one or more decisions regarding an intellectual property asset or group of intellectual property assets; making one or more decisions based on at least one predefined data point; an algorithm for displaying one or more decisions to one or more users; Computer program products.

16. the predefined one or more data points for the one or more decisions are updated after one or more decisions are requested by a user; 16. The computer program product of claim 15, wherein the predefined one or more data points are updated through a user, additional data, or decisions previously requested by the user, and wherein these processes are performed using artificial intelligence, machine learning, or a combination thereof.

17. 16. The computer program product of claim 15, wherein the program instructions recorded on the computer-readable medium include a process for a user to perform an action based on detection by the algorithm to update the intellectual property or group of intellectual property.

18. 16. The computer program product of claim 15, wherein said algorithm determines whether an annuity or renewal fee is due for said intellectual property or group of intellectual property.

19. 16. The computer program product of claim 15, wherein the algorithm weights each data point or each plurality of data points based on one or more of the company's projected business roadmap, costs, budget, manufacturing footprint, market factors, comments made in internal meetings, or a combination thereof.

20. 16. The computer program product of claim 15, wherein the algorithm weights each data point or each plurality of data points based on their expected effect on the portfolio.

Images