Chip authentication method and chip with artificial intelligence identification module

Abstract

The application provides a chip authentication method applied to a chip with an artificial intelligence identification module, and has the characteristics that a first instruction sent by a host is received, a first response signal is generated based on the first instruction; a second instruction generated by the host to the first response signal is received, the second instruction is input into the artificial intelligence identification module, the artificial intelligence identification module predicts the instruction intention of the host according to a pre-trained identification model and the second instruction; if the instruction intention is that the first response signal is not identified, the first response signal output subsequently is switched to a second response signal; through the chip authentication method, the real intention of whether the host passes the chip authentication can be identified, and switching is performed in time when the host does not identify the response signal sent by the chip, so that the normal use of the chip is avoided.

Description

Technical Field

[0001] This invention relates to the field of chip communication, and more particularly to a chip authentication method. Background Technology

[0002] Generally, chips contain identification information for host authentication, such as a serial number (SN). To prevent chip reuse, the host remembers which identification information has been used. Therefore, if the host considers a chip's identification information to be used, it cannot pass the host's authentication. To address this, chips that store multiple sets of identification information and can switch between different information have emerged. Taking printing consumables as an example, these consumables contain a chip that communicates with the printer. Current consumable chips determine whether the serial number sent by the consumable chip can be recognized by the printer by judging the sequence and data characteristics of the instructions sent by the printer. The rules (i.e., the software flow) for this determination are pre-programmed into the consumable chip during production based on the known characteristics of a particular printer. Therefore, when firmware upgrades occur, if the printer changes the original instruction sequence, data content, or other characteristics that affect the consumable chip's judgment, the consumable chip cannot determine whether the serial number has been recognized. When the consumable chip cannot determine whether the serial number has been recognized, it loses the ability to switch serial numbers, rendering the consumable chip unusable on printers that store serial numbers.

[0003] Manufacturers can periodically or irregularly change how the host identifies the chip, making it impossible for the chip to recognize the host's true intention in whether the chip authentication is successful. This is a problem that compatible chip manufacturers need to solve. Summary of the Invention

[0004] To address the aforementioned issues, this invention provides a chip authentication method and a chip with an artificial intelligence recognition module. The artificial intelligence recognition module predicts the host's instruction intent based on a pre-trained recognition model and a second instruction returned by the host. When the host fails to recognize the response signal issued by the chip, it promptly switches to the chip, thus avoiding disruption to its normal operation.

[0005] In a first aspect, embodiments of the present invention provide a chip authentication method applied to a chip with an artificial intelligence recognition module, characterized in that: receiving a first instruction sent by a host, generating a first response signal based on the first instruction; receiving a second instruction generated by the host in response to the first response signal, inputting the second instruction into the artificial intelligence recognition module, the artificial intelligence recognition module predicting the host's instruction intent based on a pre-trained recognition model and the second instruction; if the instruction intent is that the first response signal is not recognized, then switching the subsequently output first response signal to a second response signal.

[0006] Secondly, embodiments of the present invention provide a chip with an artificial intelligence recognition module, comprising: an interface module, a control module, and a storage module; the storage module stores multiple sets of authentication information; the chip receives a first instruction sent by a host through the interface module and sends first authentication information to the host; the control module includes any of the aforementioned artificial intelligence recognition modules; and the chip executes any of the aforementioned authentication methods during on-machine authentication.

[0007] In this embodiment of the invention, an artificial intelligence recognition model is incorporated into the chip, which can flexibly recognize various instructions issued by the host (even if the host has undergone firmware upgrades), determine whether the authentication data sent by the chip is recognized, and intelligently switch to output other authentication data when the host does not recognize the current authentication data, so as to ensure that the chip can be used normally on the host. Attached Figure Description

[0008] Figure 1 A flowchart of a chip authentication method provided in an embodiment of the present invention;

[0009] Figure 2 This is a schematic diagram of the structure of a chip provided in an embodiment of the present invention;

[0010] Figure 3 This is a schematic diagram of the structure of another chip provided in an embodiment of the present invention;

[0011] Figure 4 This is a flowchart of another chip authentication method provided in an embodiment of the present invention. Detailed Implementation

[0012] To better understand the technical solutions in this specification, the embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0013] It should be understood that the described embodiments are merely some, not all, of the embodiments in this specification. All other embodiments obtained by those skilled in the art based on the embodiments in this specification without inventive effort are within the scope of protection of this invention.

[0014] The terminology used in the embodiments of this invention is for the purpose of describing particular embodiments only and is not intended to be limiting of this specification. The singular forms “a,” “the,” and “the” as used in the embodiments of this invention and the appended claims are also intended to include the plural forms unless the context clearly indicates otherwise.

[0015] When a chip is installed in a host device, the host needs to authenticate the chip. The host device can be a computer, server, mobile terminal, image output device, or any other device that requires authentication of accessories connected to it. The chip is an electronic module embedded in the accessory, storing information about the accessory. Due to technological advancements, host devices have adopted more authentication measures to verify the chip's origin, such as periodically upgrading the host's firmware and using the upgraded features to authenticate the chip in the accessory. However, some authentication is unnecessary and merely a means for the host to maintain its monopolistic interests. Therefore, based on personal research, aiming to improve host performance and reduce the cost of using accessories, the inventors of this application explore how chips can adapt to various host upgrades by modifying their hardware or software in different scenarios.

[0016] Manufacturers can periodically or irregularly change how the host machine identifies the chip, which is an important problem that compatible chip manufacturers need to solve.

[0017] Figure 1 This is a flowchart illustrating a chip authentication method provided by an embodiment of the present invention. This method can be applied to a target chip; in this application, the target chip is a chip with an artificial intelligence recognition module, such as... Figure 1 As shown, the method processing steps include:

[0018] Receive a first instruction sent by the host, and generate a first response signal based on the first instruction;

[0019] The system receives a second instruction generated by the host in response to the first response signal, inputs the second instruction into the artificial intelligence recognition module, and the artificial intelligence recognition module predicts the host's instruction intent based on the pre-trained recognition model and the second instruction.

[0020] If the instruction is intended to prevent the first response signal from being recognized, then the subsequent output of the first response signal will be switched to the second response signal.

[0021] In the embodiments of this application, the host device may be a printer, mobile terminal, computer or other devices, and correspondingly, the target chip accessory may be a consumable box, earphone, battery, peripheral or other modules containing a chip.

[0022] During the chip authentication process, the host sends an authentication command to the chip, namely the first command. The chip generates a response signal based on the stored authentication information, namely the first response signal. After receiving the first response signal returned by the chip, the host performs an internal authentication algorithm. After identifying whether the chip is legitimate, it outputs a command that affects subsequent chip communication, namely the second command.

[0023] Taking the authentication process of a printer consumable chip as an example, when the printer consumable chip is installed, the printer, as the host, sends an authentication command to the consumable chip to authenticate its identity information. After receiving the authentication command from the printer, the consumable chip generates a response signal based on the authentication information such as the serial number pre-stored in the consumable chip. When the printer receives the response signal from the consumable chip, the printer generates a second command based on the response signal. This command carries the intention of whether the target consumable chip has passed authentication.

[0024] Some printers analyze the response signals sent by the consumable chip and determine that the chip is an illegal chip or a chip that has been used. They will disconnect from the consumable chip or not respond. The printer's response is regarded as a second instruction generated by the printer in response to the response signal. The instruction is intended to prevent the target consumable chip from being authenticated. Therefore, the authentication information such as the serial number sent by the consumable chip is switched. This process can also be regarded as determining whether the serial number sent by the consumable chip can be recognized by the printer by judging the sequence, data and other characteristics of the instructions sent by the specific printer to the consumable chip. The basis for the judgment and the serial number switching method are pre-burned into the consumable chip during production.

[0025] In one embodiment of this application, the consumable chip stores the sequence of instructions, data, and other features for determining whether the printer recognizes the consumable chip as legitimate. The printer determines whether the serial number sent by the consumable chip is recognized by the printer by receiving a second instruction returned by the printer.

[0026] In one embodiment of this application, after the printer is upgraded via firmware, the order and data content of the instructions used by the printer to determine whether the consumable chip is legitimate have changed. After analyzing the response signal sent by the consumable chip, the printer determines that the consumable chip is an illegal chip or a chip that has already been used. It may continue to communicate with the target chip and allow the use of the consumable chip. However, the second instruction it issues will be different from the one used by the legitimate chip, such as the order of instructions and the data content. However, when the current consumable chip cannot determine whether the serial number is recognized by the upgraded printer, it loses the function of switching serial numbers.

[0027] After receiving the authentication response from the consumable chip, if the printer allows the connection of the consumable chip, it may modify the amount of available recording material on the printing consumable chip during subsequent communication processes to control the remaining lifespan of the corresponding printing consumable. In the embodiments of this application, the amount of available recording material may be information such as the ink volume and toner volume of the printing consumable.

[0028] In one embodiment of this application, when the printer sends a second instruction intended to fail, the address bits written when communicating with the consumable chip are different, or there are other negative identifiers, which blacklist the consumable chip during the use of the consumable; or the amount of ink written is inconsistent with the actual amount, such as writing ink amount information multiple times, which shortens the life of the consumable; the above-mentioned false pass authentication processing method of the printer makes the user experience of compatible chips drastically reduced.

[0029] Therefore, in the embodiments of this application, after receiving the second instruction generated by the printer in response to the first response signal, the second instruction is input into the artificial intelligence recognition module of the consumable chip. The artificial intelligence recognition module predicts the printer's instruction intent based on the pre-trained recognition model and the second instruction generated by the printer. If the instruction intent is that the first response signal of the target consumable chip is not recognized, the first response signal sent subsequently is switched to the second response signal in a timely manner, so that the printer can successfully perform secondary authentication and recognize the consumable chip as legitimate.

[0030] Through the embodiments of this application, even if the printer temporarily allows the connection of the consumable chip and subsequent printing communication when sending the second instruction, the consumable chip is actually marked as illegal. Through the artificial intelligence recognition module, the true intent of the printer's second instruction can be identified, and the output authentication information can be switched in time when it is considered illegal, so as to avoid the bad user experience of being blacklisted during the printing process or having the corresponding consumable box life shortened.

[0031] Artificial Intelligence (AI) is a new branch of computer science that studies, develops, and applies theories, methods, technologies, and application systems to simulate, extend, and expand human intelligence. It aims to understand the essence of intelligence and produce intelligent machines that can react in a way similar to human intelligence. Research in this field includes robotics, speech recognition, image recognition, natural language processing, and expert systems. The concept of artificial intelligence was proposed in 1955; machine learning in 1990; and deep learning in 2010. Machine learning is just one method of implementing artificial intelligence, but currently, it is the most practical and effective. The currently popular image recognition uses deep learning algorithms such as Convolutional Neural Networks (CNNs). Artificial intelligence studies the laws governing human intelligent activity, applies computer hardware and software to simulate certain human intelligent behaviors, constructs artificial systems with a certain level of intelligence, and studies how to enable computers to perform tasks that previously required human intelligence, thus freeing humans from manual and mental labor.

[0032] The traditional programming path is: Input + Rule --> Output. The rule is derived by the human brain based on logic and rationality; this is what's commonly referred to as business logic. The artificial intelligence design path is: Existing Input + Existing Output (Training Set) --> Rule (Model). That is, the model is first trained using known inputs and a known ideal output (the training set used to train the model), resulting in a trained model. Then, the trained model is used to predict the output, i.e.: Input + Rule (Model) --> Output. This requires designing mathematical or reasoning models to allow the computer to calculate the relationship between the existing inputs and outputs, thus obtaining the rule (model).

[0033] Deep learning treats the data that the computer needs to learn as a large amount of data, puts this data into a multi-layered and complex data processing network (neural network), and then checks whether the result data obtained by this network meets the requirements. If it does, the network is kept as the target model; if it does not, the parameters of the network are adjusted one after another until the result meets the requirements.

[0034] The chip recognition model in this application is obtained through pre-training based on multiple training instructions and corresponding recognition results.

[0035] In one embodiment of this application, in order to reduce the processing difficulty of the artificial intelligence recognition module and the storage model memory of the consumable chip, after the target consumable chip receives the second instruction generated by the printer in response to the first response signal, the following steps are further included:

[0036] Determine whether the second instruction indicates that the chip is allowed to connect. If the second instruction indicates that the chip is not allowed to connect, then determine that the printer's instruction intent is that the first response signal is not recognized. This type of instruction can be directly recognized by current consumable chips. Therefore, this type of second instruction does not need to be predicted by the artificial intelligence recognition module, and the consumable chip can directly switch serial numbers.

[0037] In another embodiment, if the second instruction indicates that the chip connection is allowed, the second instruction is input to the artificial intelligence recognition module. The artificial intelligence recognition module predicts the instruction intent of the host based on the pre-trained recognition model and the second instruction. If the instruction intent is that the first response signal of the target consumable chip is not recognized, the first response signal is switched to the second response signal in a timely manner.

[0038] Of course, in the embodiments of this application, in order to simplify the identification steps, the target consumable chip can be directly input into the artificial intelligence identification module for prediction by receiving the second instruction generated by the printer in response to the first response signal, omitting the preliminary judgment process.

[0039] In the embodiments of this application, the consumable chip receives a first instruction sent by the printer, generates a first response signal based on the first instruction, the first response signal is generated by the first authentication information, the second response signal is generated by the second authentication information, and the consumable chip stores at least two sets of authentication information.

[0040] The second authentication information is different from the first authentication information. Both the first and second authentication information are data used by the printer to read and determine whether the source or identity of the consumable chip is recognized by the printer.

[0041] In one embodiment, the chip is initially set to output first authentication data. When the artificial intelligence recognition model detects that the host does not recognize the first authentication data, the chip switches to outputting second authentication data instead of the previously output first authentication data.

[0042] In the embodiments of this application, a deep learning recognition model is established when designing the consumable chip. This recognition model can be a Convolutional Neural Network (CNN) model. In one embodiment, the recognition model can be pre-trained using a large number of training instructions in a classification manner, enabling the model to have classification capabilities. The training instructions are various instructions collected in advance from the authentication process of consumable chips by different printers currently on the market. The AI ​​recognition module operates at low and high levels. The recognition model of the AI ​​recognition module includes multiple convolutional layers and multiple fully connected layers. The convolutional layers are the low-level layers of the recognition model, used to learn instruction type, instruction quantity, and instruction length information. The fully connected layers are the high-level layers of the recognition model, used to learn instruction content, instruction interval information, instruction signal amplitude, and signal frequency.

[0043] Although the recognition model is trained based on different known instructions from various printers to identify unknown instructions, this artificial intelligence recognition model is trained through deep learning. Therefore, it can be assumed that the recognition results of the artificial intelligence recognition model for known and unknown instructions are consistent with the true intention of the printer.

[0044] In one embodiment, to ensure the accuracy of the trained recognition model, the more training instructions collected, the better. The number of instructions collected during the printer's authentication process of the consumable chip can reach tens of thousands, for example, 10,000, 300,000, or even more. The more training instructions collected, the more accurate the trained recognition model will be, and the more accurately it will recognize subsequent printer instructions (through which the printer identifies the chip's authentication information). Besides collecting the printer's authentication process instructions for the consumable chip, it is also necessary to collect the printer's true intention in issuing these instructions: that is, whether the collected instructions indicate the printer's true intention to recognize or not recognize the consumable chip.

[0045] When pre-training a recognition model, the following steps can be taken:

[0046] X1. Substitute the current training instruction into the artificial intelligence recognition model so that the artificial intelligence recognition model predicts the recognition result corresponding to the instruction;

[0047] X2. Determine whether the recognition result of the training instruction matches the instruction of the host. Figure 1 If they are consistent, proceed to step X3; if they are consistent, proceed to step X4.

[0048] X3. Modify the model parameters of the artificial intelligence recognition model so that the recognition result of the instruction predicted by the artificial intelligence recognition model is consistent with the instruction intent of the host; execute step X4;

[0049] X4. Continue to select the next training instruction as the current training data, and train using the steps (X1-X3).

[0050] Using each training instruction, repeat the above steps (X1-X4) to train the artificial intelligence recognition model until the model's predictions for all collected instructions match the printer's actual intention. Figure 1 This process determines the model parameters of the recognition model, ultimately establishing the artificial intelligence recognition model. During production, the determined artificial intelligence model is then written (programmed) into the chip.

[0051] When a printer adopts a completely different instruction format after a later firmware upgrade, the chip's control module classifies the received printer instructions and extracts relevant features based on the determined model parameters. These features are then input into the recognition model to predict whether the authentication data will be recognized by the printer.

[0052] In the embodiments of this application, when analyzing the instruction content of the second instruction, the write address of the instruction and / or the recording material consumption of the instruction are included. The recording material consumption of this application is used to calculate the availability of the consumable chip. When the remaining amount of recordable material is 0, the chip is unusable. When there is an error in the write address bit of the instruction, it will also affect the calculation of the remaining amount of recording material. The analysis of the instruction content of the second instruction may also include analyzing whether there are other negative identifiers, which are used to identify the target consumable chip as illegal, so as to avoid blacklisting the consumable chip during the use of the consumable.

[0053] This application also provides a method for using artificial intelligence to identify whether authentication data has been identified, including...

[0054] The preprocessing step involves preprocessing the second instruction sent from the printer to obtain instruction features, which are used to indicate the operating level of the artificial intelligence recognition module.

[0055] The recognition step involves predicting the intent of the instruction based on its features and a pre-trained recognition model, according to the operational hierarchy of the AI ​​recognition module; and

[0056] The switching step determines whether to switch the authentication information to be output later, based on the instruction intent. If the instruction intent is that the printer does not recognize the previously output first authentication information, then the output will be switched to the second authentication information.

[0057] In another embodiment, the chip's control module may include multiple sub-recognition models, each corresponding to a learning and training model for different instruction features. After the chip is installed on the host computer, it performs preliminary classification and feature filtering on the received instructions, then inputs them into different sub-recognition models. Finally, the outputs of the different sub-recognition models are weighted to obtain a more accurate recognition result, thereby revealing the host computer's true intent.

[0058] Therefore, by incorporating an artificial intelligence recognition model into the chip through this application, it is possible to flexibly recognize various instructions issued by the host (even if the host has undergone firmware upgrades), determine whether the authentication data sent out by the chip has been recognized, and intelligently switch to output other authentication data when the host does not recognize the current authentication data, so as to ensure that the chip can continue to be used on the host.

[0059] The present invention also provides a chip with an artificial intelligence recognition module, including an interface module, a control module, and a storage module. The storage module stores multiple sets of authentication information. The chip receives a first instruction sent by a host through the interface module and sends first authentication information to the host. The control module includes the artificial intelligence recognition module. The chip executes the authentication method of any one of the foregoing.

[0060] This application provides a chip with an artificial intelligence recognition module, such as... Figure 2 As shown, it includes an interface module, a control module, and a storage module. The storage module stores multiple sets of authentication information (such as serial numbers). The chip receives communication commands sent by the host through the interface module and sends the first authentication information to the host. The control module includes an artificial intelligence recognition module, which can predict the recognition result based on a pre-trained recognition model and the received communication commands. When the recognition result is that the first authentication information is not recognized, the second authentication information is output from the storage module through the interface module.

[0061] Because the pre-trained recognition module has a high accuracy rate in recognizing commands sent by the host, it can correctly determine whether the host recognizes the authentication data based on the recognition results of the recognition module. Therefore, when the authentication data is not recognized, the output authentication data can be switched in time to ensure that the chip can be recognized by the host, so that the accessories used with the chip can continue to be used on the host.

[0062] In the embodiments of this application, the chip can also possess autonomous learning capabilities, enabling organic iteration of the chip, algorithm, and data from data collection, annotation, and training to algorithm upgrades. This promotes continuous self-evolution of algorithms for application scenarios and even allows for distributed self-training on the terminal chip. In other words, the more hosts installed on the chip, the more subsequent training the chip's model receives, resulting in more accurate judgments.

[0063] The chip proposed in this invention has the ability to learn autonomously. It can actively memorize and analyze the characteristics and communication sequence of the printer's instructions. Based on the changed characteristics of the printer's instructions, it can autonomously adjust the serial number switching method to adapt to the usage environment after firmware upgrade. This allows the chip to no longer be limited to the original switching rules, but to flexibly follow the changes in the printer and intelligently identify whether the serial number needs to be switched.

[0064] The chip provided by this invention specifically includes a consumable chip for printer authentication.

[0065] Through the above research, the inventors of this application discovered that they can make corresponding changes to the printers and chips they purchase, thereby improving the printer's performance and reducing their own operating costs, without having to constantly purchase original manufacturer chips and printing consumables.

[0066] This application also provides a chip including an interface module, a memory, a main processor, and a coprocessor. The main processor is used to process signals received from the interface module; the coprocessor is used to execute a preset program segment that cannot be executed on the main processor. The preset program segment is received from the host or stored in the memory.

[0067] This application also provides a method for responding to escalation, including:

[0068] Receive signals from the host computer. The received signals include regular program segments and program segments written using a specific instruction set.

[0069] Determine whether the received signal is a preset program segment, where the preset program segment is a program segment written using a specific instruction set;

[0070] When a pre-defined program segment is received, the program segment is forwarded to the coprocessor for processing;

[0071] The main processor feeds back and sends out the execution results of the coprocessor.

[0072] This application provides a solution using one or more chips to address various host upgrade methods in different scenarios. This allows inventors to improve host performance and reduce component usage costs.

[0073] During the certification of consumable chips, the printer sends a code segment (program segment) containing a specific set of instructions to the chip. The chip needs to execute this code segment and then return the execution result to the printer. Original consumable chips include a custom CPU that supports direct execution of these code segments. However, currently compatible consumable chips use general-purpose CPUs that cannot execute these code segments. Therefore, the execution results of known code segments must be stored in the chip beforehand during production. When a printer undergoes a firmware upgrade, if the upgraded printer sends a code segment containing new instructions, the compatible consumable chips, lacking a custom CPU to execute this code segment, will be unable to respond properly unless these compatible chips are recalled for rework. This can lead to printer malfunctions and user complaints.

[0074] Another scenario involves a compatible chip storing an encrypted program segment. When the compatible chip is installed in the printer, the printer sends a decryption key to the compatible chip, which then decrypts the key, runs the program segment, and sends the result back to the host. Similarly, because the compatible consumable chip lacks a CPU to execute this program segment, it will be unable to do so and thus cannot respond to the host's requests.

[0075] Therefore, under the current circumstances, the host can verify whether the chip supports certain features or functions by sending a new program segment or sending an instruction to request the chip to execute a certain encrypted program segment and return the execution result. This is one way for the host to improve the verification function, but there is no good solution yet.

[0076] In existing consumable chips, the SoC integrates a coprocessor (co-CPU) that supports processing OEM instruction sets. This co-CPU handles special program segments, such as code segments that encrypt data. Thus, when the SoC receives a code segment containing OEM instruction sets, or after decrypting its own stored program segments, the coprocessor processes the data. The execution result from the coprocessor is then forwarded by the SoC to the printing device.

[0077] This application also provides a chip that can be authenticated by a host, such as... Figure 3 As shown, it includes an interface module, a memory, a main processor, and a coprocessor. The main processor is used to process signals received from the interface module; the coprocessor is used to execute preset program segments that cannot be executed on the main processor. These preset program segments are received from the host or stored in the memory.

[0078] In one embodiment, after receiving a signal from the interface module, the main processor determines whether the received signal is a preset program segment. If so, it forwards the program segment to the coprocessor for processing. The main processor is also used to send the execution results of the coprocessor to the outside through the interface module.

[0079] The main processor (CPU) is a general-purpose processor that can execute regular instructions, such as read and write instructions. The coprocessor, on the other hand, is a dedicated processor used to execute authentication instructions with specific instruction sets, such as encryption instructions or program segments containing special encodings. These program segments cannot be executed on the main processor, but the coprocessor supports the instruction set of the program segment and can therefore execute it. The host can determine the legitimate origin of the chip by checking whether it supports executing the program segment or whether the execution result returned by the chip is correct. If the chip is legitimate, it is authenticated.

[0080] This chip can be a consumable chip used in printing and imaging, a chip in mobile terminal accessories (such as batteries, charging cables, and earphones), or a chip in wearable devices. Because the chip includes a coprocessor that executes a special instruction set, it possesses features not found in conventional general-purpose processors. Therefore, printing equipment, mobile terminals, or main controllers can authenticate devices that include this chip. The chip may also include memory for storing information related to the chip, accessories, or authentication.

[0081] Generally, processors with different architectures support different instruction sets, resulting in different executable machine code. Typically, programmers write easily readable and understandable computer programs using high-level languages ​​(such as C) or low-level languages ​​(such as assembly language) based on the required software functionality. These programs are then input into a compiler, which compiles (resulting in object files) and links (linking library files), ultimately producing executable code (machine code that the processor can directly process). For example, the machine code compiled from a processor of architecture A (supporting instruction set AA) cannot be executed on a processor of architecture B (supporting instruction set BB, which differs from instruction set AA).

[0082] For example, the main processor of the chip is an ARM core CPU, while the coprocessor of the chip is a CPU that supports the x86 instruction set. When the main processor receives a program segment written in the x86 instruction set from the interface module, it hands the program segment over to the coprocessor for processing. After the coprocessor executes the program segment, it feeds back the execution result to the main processor. Thus, the chip can execute program segments written in the x86 instruction set to meet the host's authentication or communication needs for the chip.

[0083] For example, the chip may include a main processor that is an ARM-based CPU, and a coprocessor that is a CPU that supports the RISC-V instruction set. In other embodiments, the main processor may be a CPU that supports conventional instruction sets (e.g., x86, ARM, MIPS, and POWER PC), while the coprocessor may be a less common CPU used by a few companies on a small scale, such as Motorola's M-core or DEC's Alpha processor.

[0084] In one embodiment, the host sends a program segment to the chip, indicating that 1 million floating-point operations need to be performed. This program segment can only be executed on a processor that supports a preset instruction set. If the main processor cannot execute the program segment, it will hand it over to the coprocessor for execution. After the coprocessor executes the program segment and performs 1 million floating-point operations, it feeds back the execution result (operation result) to the main processor, and then the main processor responds to the host through the interface module.

[0085] In another embodiment, the chip stores multiple encrypted program segments, but the chip does not possess the decryption key. When the chip is installed on the host, the host sends the decryption key for a specific program segment to the chip and requests the chip to return the execution result of that program segment to the host. Therefore, the chip can use the received decryption key to decrypt the corresponding encrypted program segment, thereby obtaining the plaintext version of the program segment. This program segment instructs the chip to calculate the CRC value of the first 100 bytes of the chip. Since this program segment can only be executed on a processor that supports a preset instruction set, the main processor cannot execute it. Therefore, the main processor delegates the execution to a coprocessor. The coprocessor then executes the program segment, calculates the CRC value of the first 100 bytes of the chip, and returns the result (calculation result) to the main processor. Finally, the main processor responds to the host through the interface module.

[0086] Once the host device has certified the chip, it can take expected actions towards the accessories, provided that the accessories using the chip are from legitimate sources. For example, if the accessory is a fast charging cable that supports 67W power, then only after the phone, tablet, or laptop has certified the chip on the charging cable will it be allowed to fast charge its battery at a high power of 67W. This prevents damage to the battery of the phone, tablet, or laptop from uncertified charging cables due to product quality issues or unstable charging power.

[0087] In other embodiments, some original products use CPUs that support special instruction sets, while compatible products do not include CPUs that support similar special instruction sets. In order to be suitable for these original products, a sub-CPU (or coprocessor) that can process program segments written with special instruction sets can be integrated on the basis of a general-purpose processor to meet the performance requirements.

[0088] This solution also provides a method for processing program segments, which are executed on a chip including a main processor and a coprocessor, such as... Figure 4 As shown, the method includes:

[0089] Receive signals from the host computer. The received signals include regular program segments and program segments written using a specific instruction set.

[0090] Determine whether the received signal is a preset program segment, where the preset program segment is a program segment written using a specific instruction set;

[0091] When a pre-defined program segment is received, the program segment is forwarded to the coprocessor for processing;

[0092] The main processor feeds back and sends out the execution results of the coprocessor.

[0093] In other embodiments, the chip stores multiple encrypted program segments. After the host sends a decryption key to the chip, the chip decrypts the program segments, the coprocessor executes the program segments, and the main processor then sends the execution result back to the host. In this way, the host does not send different instructions to the chip, but instead sends a decryption key, allowing the chip to execute the decrypted program segments and then send the execution result.

[0094] In this application, by adding an additional coprocessor to the chip that can process instruction sets different from those of the main processor, the host's authentication and communication requirements for the chip can be met, thus preventing the chip from becoming unusable.

[0095] The present invention also provides an accessory having any of the aforementioned chips installed on it. The accessory may be a data cable, charger, battery, printing consumables (e.g., ink cartridge, toner cartridge, powder cartridge), replaceable printing components (e.g., printhead, photosensitive drum), etc.

[0096] The aforementioned chip's storage module / device is used to store information about the chip. When the chip is a consumable chip, it can store writable or read-only information such as the chip's manufacturing date, manufacturer, color of the recording material (e.g., ink, toner), recording material capacity, remaining or consumed quantity of recording material, number of printable pages, and number of printed pages. The storage module / device can use common non-volatile memory, such as EPROM, EEPROM, FLASH, ferroelectric memory, phase-change memory, etc., or it can use a volatile memory plus a power supply, such as SRAM + battery or capacitor, or DRAM + battery or capacitor.

[0097] The chip may also include a microcontroller (MCU), a microcontroller, an FPGA, an ASIC, etc., for controlling the communication between the chip and the host, reading information from the storage module / device and storing information in the storage module / device. Specifically, it may be the main processor, control module, or control circuit in the above embodiments.

[0098] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of this application.

Claims

1. A chip authentication method, applied to a chip with an artificial intelligence recognition module, characterized in that, The system receives a first instruction sent by the host and generates a first response signal based on the first instruction; it receives a second instruction generated by the host in response to the first response signal and inputs the second instruction into the artificial intelligence recognition module, which predicts the host's instruction intent based on a pre-trained artificial intelligence recognition model and the second instruction. If the instruction is intended to prevent the first response signal from being recognized, then the first response signal output subsequently will be switched to the second response signal. The artificial intelligence recognition model is obtained through pre-training based on multiple training instructions and corresponding recognition results. The multiple training instructions are various instructions that appear during the authentication process of chip authentication information by various different hosts currently on the market, which are collected in advance. The corresponding recognition results include authentication information being recognized and authentication information not being recognized. The AI ​​recognition module operates at two levels: a low-level layer and a high-level layer. The low-level and high-level layers store different recognition models. Each recognition model is obtained by learning different instruction features. Specifically, the low-level recognition model is obtained by learning at least one or more of the following: instruction type, instruction quantity, and instruction length. The high-level recognition model is obtained by learning at least one or more of the following: instruction content, instruction interval, instruction signal amplitude, and signal frequency. Using these multiple training instructions, the low-level and / or high-level AI recognition models are pre-trained using a classification method, enabling the AI ​​recognition model to have classification capabilities.

2. The chip authentication method according to claim 1, characterized in that, After receiving the second instruction generated by the host in response to the first response signal, the method further includes: Determine whether the second instruction indicates that the chip is allowed to connect. If the second instruction indicates that the chip is not allowed to connect, determine that the instruction intent is that the first response signal is not recognized. If the second instruction indicates that the chip is allowed to connect, input the second instruction into the artificial intelligence recognition module. The artificial intelligence recognition module predicts the instruction intent of the host based on the pre-trained recognition model and the second instruction.

3. The chip authentication method according to claim 1 or 2, characterized in that, The first response signal is generated by the first authentication information, and the second response signal is generated by the second authentication information; The second authentication information is different from the first authentication information. Both the first authentication information and the second authentication information are data used by the host to read and determine whether the source or identity of the chip is recognized by the host.

4. The chip authentication method according to claim 1, characterized in that, The artificial intelligence recognition model is pre-trained based on multiple training instructions and corresponding recognition results, specifically including: X1. Substitute the current training instruction into the artificial intelligence recognition model so that the artificial intelligence recognition model predicts the recognition result corresponding to the instruction; X2. Determine whether the recognition result of the training instruction is consistent with the instruction intent of the host. If they are inconsistent, proceed to step X3; if they are consistent, proceed to step X4. X3. Modify the model parameters of the artificial intelligence recognition model so that the recognition result of the instruction predicted by the artificial intelligence recognition model is consistent with the instruction intent of the host; execute step X4; X4. Continue to select the next training instruction as the current training data, and perform training using steps X1-X3.

5. The chip authentication method according to claim 1, wherein the instruction content includes at least one or a combination of the following: the instruction write address, the instruction recording material consumption, or whether it contains a negative identifier, wherein the recording material consumption is used to calculate the availability of the chip.

6. The chip authentication method according to claim 1 or 5, characterized in that, Also includes: The preprocessing step involves preprocessing the second instruction sent from the host to obtain instruction features, which are used to indicate the operating level of the artificial intelligence recognition module. The identification step involves predicting the instruction intent corresponding to the instruction features based on the operation level of the artificial intelligence identification module, the instruction features, and the pre-trained identification model. and The switching step involves determining whether to switch the first authentication information based on the instruction intent. If the instruction intent is that the host does not recognize the first authentication information, then the second authentication information is switched to be output.

7. The chip authentication method according to claim 1, characterized in that, The artificial intelligence recognition model includes multiple sub-recognition models, each corresponding to a learning and training model for different instruction features. The authentication method includes inputting the received instruction features into the corresponding multiple sub-recognition models, weighting the recognition results of the different sub-recognition models, and thereby predicting the host instruction intent corresponding to the instruction features.

8. A chip with an artificial intelligence recognition module, characterized in that, The chip includes an interface module, a control module, and a storage module. The storage module stores multiple sets of authentication information. The chip receives a first instruction sent by the host through the interface module and sends the first authentication information to the host. The control module includes the artificial intelligence recognition module. When the chip performs on-machine authentication, it executes the authentication method described in any one of claims 1-7.

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