Game assisting method and electronic device using the same

Abstract

A game assisting method and an electronic device using the same are provided. The game assistance method includes the following steps. A plurality of computing hardware are sorted to obtain a designated computing hardware. The designated computing hardware performs an image recognition process on a game screen through a first artificial intelligence model to obtain a message pattern. The designated computing hardware performs an image processing procedure on the message pattern through a second artificial intelligence model to obtain a processed pattern. The processed pattern is synthesized at a specified position.

Description

[Technical Field]

[0001] This disclosure relates to a control method and an electronic device using the same, and more particularly to a game-assisting method and an electronic device using the same. [Previous Technology]

[0002] With the rapid development of information technology, various computer games are constantly being updated. Many game screens are becoming increasingly complex, causing users to miss important information or objects. For example, a user might not notice the health bar icon, leading to a rapid drop in health and the game ending. This will seriously affect the user experience. [Summary of the Invention]

[0003] This disclosure relates to a game assistance method and an electronic device using the same, which identifies the game screen to recognize message patterns, performs corresponding image processing procedures, and displays the processed pattern at a designated location. This helps the user notice the content of the message pattern.

[0004] According to one aspect of this disclosure, a game-assisting method is proposed. The game-assisting method includes the following steps: Sorting several computing devices to obtain a designated computing device; The designated computing device performs an image recognition procedure on a game screen using a first artificial intelligence model to obtain a message pattern; The designated computing device performs an image processing procedure on the message pattern using a second artificial intelligence model to obtain a processed pattern; The processed pattern is then composited at a designated location.

[0005] According to another aspect of this disclosure, an electronic device is provided. The electronic device includes a plurality of computing hardware, a control unit, a first artificial intelligence model, a second artificial intelligence model, and a synthesis unit. The control unit is used to sort the computing hardware to obtain a designated computing hardware. The designated computing hardware is used to perform an image recognition process on a game screen through the first artificial intelligence model to obtain a message pattern. The designated computing hardware is used to perform an image processing process on the message pattern through the second artificial intelligence model to obtain a processed pattern. The synthesis unit is used to synthesize the processed pattern at a designated location.

[0006] In order to better understand the above and other aspects of this disclosure, specific embodiments are described below in conjunction with the accompanying drawings:

Implementation Method

[0007] The technical terms used in this specification are based on common terminology in the field. Where this specification provides explanations or definitions for certain terms, the interpretation of those terms shall be based on the explanations or definitions provided in this specification. Each embodiment disclosed herein has one or more technical features. Where feasible, those skilled in the art may selectively implement some or all of the technical features in any embodiment, or selectively combine some or all of the technical features in these embodiments.

[0008] Please refer to Figure 1, which illustrates an example of a game assistance method performed by an electronic device 100 according to an embodiment of this disclosure. When the game screen FM is complex, the user may not notice some important information or objects. For example, the user may not notice the health bar message pattern PT. In this embodiment, the electronic device 100 can identify the message pattern PT on the game screen FM, perform corresponding processing, and then display the processed pattern PT' at a designated location LC (e.g., the vicinity of one of the cursor CR's ranges). When the user moves the cursor CR, the processed pattern PT' moves with the cursor CR, allowing the user to monitor health bar information at any time.

[0009] Please refer to Figure 2, which illustrates a block diagram of an electronic device 100 according to an embodiment of this disclosure. The electronic device 100 includes, for example, several computing hardware units 190, a control unit 140, a first artificial intelligence model 110, a second artificial intelligence model 120, a synthesis unit 130, a display unit 160, and a shared temporary memory 150. The computing hardware units 190 are, for example, a central processing unit 191, a graphics processing unit 192, a neural network processor 193, or other processors. The control unit 140 is used to analyze these computing hardware units 190 and execute a specified program. The first artificial intelligence model 110 and the second artificial intelligence model 120 are used to perform different artificial intelligence inference programs. The synthesis unit 130 is used to perform image synthesis programs. The display unit 160 is used to display various information and images. The shared temporary memory 150 is used to temporarily store various processing results.

[0010] The control unit 140, the first artificial intelligence model 110, the second artificial intelligence model 120, and / or the synthesis unit 130 are, for example, a circuit, a circuit board, a storage device for stored code, or a chip. The chip is, for example, a programmable general-purpose or special-purpose microcontroller (MCU), microprocessor, digital signal processor (DSP), programmable controller, application-specific integrated circuit (ASIC), image signal processor (ISP), image processing unit (IPU), arithmetic logic unit (ALU), complex programmable logic device (CPLD), field programmable gate array (FPGA), or other similar components or combinations thereof.

[0011] The display unit 160 is, for example, a liquid crystal display panel, an OLED display panel, or an electronic paper display panel. The shared temporary storage 150 is, for example, any type of fixed or removable random access memory (RAM), read-only memory (ROM), flash memory, hard disk drive (HDD), solid state drive (SSD), or similar components or combinations thereof, used to store multiple modules or various applications that can be executed by the processor.

[0012] In this disclosure, the message pattern PT of the game screen FM can be identified, and after performing corresponding image processing procedures, the processed pattern PT' is displayed at a designated location LC. In this way, it can help users notice the content of the message pattern PT. The operation of the above components is explained in detail below with flowcharts.

[0013] Please refer to Figure 3, which illustrates a flowchart of a game assistance method according to an embodiment of the present disclosure. The game assistance method includes steps S110 to S140.

[0014] In step S110, the control unit 140 sorts these computing hardware 190s to obtain a designated computing hardware HW. The designated computing hardware HW is the hardware among these computing hardware 190s that is most suitable for performing artificial intelligence inference in the current context of the electronic device 100.

[0015] Please refer to Figures 4 and 5. Figure 4 illustrates a detailed flowchart of step S110 according to an embodiment of this disclosure. Step S110 includes, for example, steps S111 to S117. Figure 5 illustrates steps S111 to S117.

[0016] In step S111, the control unit 140 obtains the historical average utilization RTH of one of the computing hardware 190. In this step, the control unit 140, for example, queries which applications are running on the electronic device 100. The control unit 140 estimates, based on historical information, how much resource these applications typically consume on each computing hardware 190 to obtain the historical average utilization RTH of each computing hardware 190. The historical average utilization RTH of these computing hardware 190s may not be the same. Under different application running conditions, it is not certain which computing hardware 190 will have a higher historical average utilization RTH. Taking Figure 5 as an example, in a certain case, the historical average utilization RTH of the central processing unit 191 is estimated to be the highest, followed by the graphics processing unit 192, and then the neural network processor 193.

[0017] Next, in step S112, the control unit 140 obtains the utilization rate RTq of each computing hardware 190 performing one of the game assistance methods. The utilization rate RTq of each computing hardware 190 performing the game assistance method is obtained by prior estimation and varies depending on the computing power of each computing hardware 190. Taking Figure 5 as an example, in a certain case, the estimated utilization rate RTq of the central processing unit 191 is the highest, followed by the graphics processing unit 192 and the neural network processor 193.

[0018] Then, in step S113, the control unit 140 determines for each computing hardware 190 whether an idle rate IR is greater than 0. The idle rate IR is calculated, for example, according to the following formula (1): ….(1)

[0019] Taking Figure 5 as an example, the idle rate IR of the central processing unit 191 is 0, while the idle rate IR of the graphics processing unit 192 and the neural network processor 193 is greater than 0. The control unit 140 determines whether the idle rate IR of each processing hardware 190 is greater than 0. If the idle rate IR is greater than 0, proceed to step S114; if the idle rate IR is not greater than 0, return to step S112. In the example of Figure 5, the graphics processing unit 192 and the neural network processor 193 will proceed to step S114.

[0020] In step S114, the control unit 140 sorts the computing hardware 190 with an idle rate IR greater than 0. Taking Figure 5 as an example, the sorting result is "1. Neural network processor 193; 2. Graphics processor 192".

[0021] Next, in step S115, the control unit 140 determines that the number of computing hardware 190 with the highest idle rate IR is greater than or equal to 2. That is, the control unit 140 determines whether there is more than one with the highest idle rate IR, and needs to further decide which computing hardware 190 to use. If the number of computing hardware 190 with the highest idle rate IR is greater than or equal to 2, then proceed to step S116.

[0022] In step S116, the control unit 140 sorts the computing hardware 190 with the highest idle rate IR according to a clock, an operating frequency and a memory available capacity.

[0023] Next, in step S117, the control unit 140 obtains the designated computing hardware HW. In the example of Figure 5, only the neural network processor 193 has the highest idle rate IR, so there is no need to execute step S116, and the neural network processor 193 is directly designated as the designated computing hardware HW.

[0024] Through the above steps S111 to S117, the control unit 140 can select the designated computing hardware HW that is most suitable for performing artificial intelligence inference from these computing hardware 190.

[0025] Next, in step S120, the specified computing hardware HW performs an image recognition process on the game screen FM through the first artificial intelligence model 110 to obtain a message pattern PT. The message pattern PT is, for example, a health pattern, an ammunition pattern, a treasure pattern, or a monster pattern.

[0026] Please refer to Figure 6, which illustrates a detailed flowchart of step S120 according to an embodiment of the present disclosure. Step S120 includes, for example, steps S121 to S126.

[0027] In step S121, the control unit 140 controls the request of the specified computing hardware HW from the shared temporary storage 150.

[0028] Next, in step S122, the control unit 140 determines whether the specified computing hardware HW has been obtained. If the specified computing hardware HW has been obtained, the process proceeds to step S123.

[0029] In step S123, the first artificial intelligence model 110 is executed by the designated computing hardware HW.

[0030] Then, in step S124, the game screen FM is input to the first artificial intelligence model 110.

[0031] Next, in step S125, the game screen is subjected to an image recognition process by the first artificial intelligence model 110.

[0032] Then, in step S126, the first artificial intelligence model 110 outputs a message pattern PT. In this step, the first artificial intelligence model 110 can further infer a specified position LC. The specified position LC is, for example, the vicinity of the cursor CR, or the upper part of the game screen, or the corner of the game screen FM, or an empty space (a position that will not obstruct game objects) inferred by the first artificial intelligence model 110.

[0033] Next, in step S130, the specified computing hardware HW performs an image processing procedure on the message pattern PT through the second artificial intelligence model 120 to obtain the processed pattern PT'. In this step, the image processing procedure includes, for example, background removal, contour analysis, or transparency processing.

[0034] Then, in step S140, the synthesis unit 130 synthesizes the processed pattern PT' at the designated position LC.

[0035] Please refer to Figures 2 and 7. Figure 7 illustrates steps S120 to S130. As shown in Figure 7, this disclosure employs a two-stage first artificial intelligence model 110 and a second artificial intelligence model 120 to perform the image recognition and image processing procedures for the message pattern PT. The two-stage approach can significantly improve processing efficiency. Furthermore, as shown in Figure 2, the control unit 140 temporarily stores the processing results of the search procedure for the specified computing hardware HW, the image recognition procedure of the first artificial intelligence model 110, the image processing procedure of the second artificial intelligence unit 120, and the image compositing procedure of the compositing unit 130 in the shared temporary storage 150. The first artificial intelligence model 110, the second artificial intelligence unit 120, and the compositing unit 130 can obtain the processing results of the previous action from the shared temporary storage 150, enabling the control unit 140, the first artificial intelligence model 110, the second artificial intelligence unit 120, and the compositing unit 130 to process in parallel, greatly improving processing efficiency.

[0036] In the embodiment of Figure 1 above, only one message pattern PT is identified, and a processed pattern PT' is presented at a designated location LC. Please refer to Figure 8, which illustrates an example of a game assistance method performed by an electronic device 100 (1) according to another embodiment of this disclosure. In the embodiment of Figure 8, both the message pattern PT and the message pattern PT (1) can be identified. After image processing, the processed pattern PT' and the processed pattern PT (1)' are presented at designated locations LC and LC (1) respectively. The designated locations LC and LC (1) are, for example, the vicinity of the cursor CR.

[0037] In the embodiment of Figure 1 above, the designated position LC is the vicinity of the cursor CR. Please refer to Figure 9, which illustrates an example of a game assistance method performed by an electronic device 100 (2) according to another embodiment of this disclosure. In the embodiment of Figure 9, after identifying the message pattern PT (2), the processed pattern PT (2)' is presented at the designated position (2). The designated position LC (2) is an empty space (a position that will not obstruct game objects) inferred by the first artificial intelligence model 110.

[0038] According to the above embodiments, the message pattern PT of the game screen FM can be identified, and after performing the corresponding image processing procedure, the processed pattern PT' is displayed at a designated location LC. In this way, it can help the user notice the content of the message pattern PT.

[0039] The foregoing disclosure provides different features for implementing some embodiments or examples of this disclosure. Specific examples of components and configurations described above (e.g., mentioned values ​​or names) are used to simplify / illustrate some embodiments of this disclosure. Of course, these components and configurations are merely examples and are not intended to be limiting. Furthermore, reference numerals and / or letters may be repeated in various instances of some embodiments of this disclosure. This repetition is for simplicity and clarity and does not in itself indicate a relationship between the various embodiments and / or configurations discussed.

[0040] In summary, although this disclosure has been presented above with reference to embodiments, it is not intended to limit this disclosure. Those skilled in the art to which this disclosure pertains can make various modifications and refinements without departing from the spirit and scope of this disclosure. Therefore, the scope of protection of this disclosure shall be determined by the appended claims. [Simplified Explanation of the Diagram]

[0041] Figure 1 illustrates an example of a game assistance method using an electronic device according to an embodiment of the present disclosure. Figure 2 illustrates a block diagram of an electronic device according to an embodiment of the present disclosure. Figure 3 illustrates a flowchart of a game assistance method according to an embodiment of the present disclosure. Figure 4 illustrates a detailed flowchart of step S110 according to an embodiment of the present disclosure. Figure 5 illustrates steps S111 to S117. Figure 6 illustrates a detailed flowchart of step S120 according to an embodiment of the present disclosure. Figure 7 illustrates steps S120 to S130. Figure 8 illustrates an example of a game assistance method using an electronic device according to another embodiment of the present disclosure. Figure 9 illustrates an example of a game assistance method using an electronic device according to another embodiment of the present disclosure.

Claims

1. A game assistance method, comprising: The plurality of computing hardware are sorted to obtain a specified computing hardware; the specified computing hardware performs an image recognition process on a game screen through a first artificial intelligence model to obtain a message pattern; the specified computing hardware performs an image processing process on the message pattern through a second artificial intelligence model to obtain a processed pattern; and the processed pattern is combined at a specified position, wherein the specified position is a proximity range of a cursor.

2. The game assistance method as described in claim 1, wherein the computing hardware is sorted according to a usage rate.

3. The game assistance method as described in claim 2, wherein the computing hardware is further sorted according to a clock speed, an operating frequency, and an available memory capacity.

4. The game assistance method as described in claim 1, wherein the computing hardware includes a central processing unit, a graphics processing unit, and a neural network processor.

5. The game assistance method as described in claim 1, wherein the message pattern is a health pattern, an ammunition pattern, a treasure pattern, or a monster pattern.

6. The game assistance method as described in claim 1, wherein the first artificial intelligence model further infers the specified location.

7. The game assistance method as described in claim 1, wherein the image processing procedure is background removal, contour analysis, or transparency processing.

8. An electronic device comprising: A complex number of arithmetic hardware units; A control unit is used to sort these computing hardware devices to obtain a specified computing hardware device; A first artificial intelligence model, the designated computing hardware being used to perform an image recognition process on a game screen through the first artificial intelligence model to obtain a message pattern; a second artificial intelligence model, the designated computing hardware being used to perform an image processing process on the message pattern through the second artificial intelligence model to obtain a processed pattern; and a synthesis unit being used to synthesize the processed pattern at a designated location, wherein the designated location is a proximity range of a cursor.

9. The electronic device as claimed in claim 8, wherein the control unit sorts the computing hardware according to a usage rate.

10. The electronic device as claimed in claim 9, wherein the control unit further sorts the computing hardware according to a clock frequency, an operating frequency, and an available memory capacity.

11. The electronic device as claimed in claim 8, wherein the message pattern is a health pattern, an ammunition pattern, a treasure pattern, or a monster pattern.

12. The electronic device as claimed in claim 8, wherein the first artificial intelligence model further infers the designated location.

13. The electronic device as claimed in claim 8, wherein the image processing procedure is background removal, contour analysis, or transparency processing.

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