Method executed by electronic equipment, electronic equipment and storage medium

Abstract

The embodiment of the invention provides a method executed by electronic equipment, the electronic equipment and a storage medium, and relates to the field of artificial intelligence. The method comprises the steps of receiving a content modification instruction of a first user for historical information of a dialogue system; based on the content modification instruction, first content needing to be modified is determined in the historical information, the determined first content is modified, and second content is obtained; dialogue input of a second user for the dialogue system is received; a dialog output is obtained using the dialog system based on second content and the dialog input. Optionally, the method performed by the electronic device may be performed using an artificial intelligence model.

Description

Technical Field

[0001] The present application relates to the field of data processing, and in particular, to a method executed by an electronic device, an electronic device, and a storage medium. Background Art

[0002] As a generative language model trained with deep neural networks and large-scale text corpus, the Generative Large Language Model can have relatively smooth and coherent conversations with people and give relevant answers and replies. It can not only understand single-round conversations, but also track topics in multiple rounds of interactions and give more contextual replies. In addition, thanks to the vast knowledge and powerful language modeling capabilities of the Generative Large Language Model, this type of model has strong language expression capabilities, the generated sentence structure is natural, the range is wide, and the answers are relatively accurate and precise. In addition, the Generative Large Language Model can handle a wide range of topics, covering common areas such as life and objects, and is not limited to a specific topic or field, and can have open-domain conversations like humans.

[0003] How to effectively prevent the leakage of personal privacy information and better meet user needs is a technical problem that technicians in this field have been working hard to study. Summary of the invention

[0004] In order to at least solve the above problems existing in the prior art, the present invention provides a method executed by an electronic device, an electronic device and a storage medium.

[0005] According to a first aspect of an embodiment of the present application, a method executed by an electronic device is provided, comprising: receiving a content modification instruction from a first user for historical information of a dialogue system; based on the content modification instruction, determining a first content that needs to be modified in the historical information, and modifying the determined first content to obtain a second content; receiving a dialogue input from a second user for the dialogue system; and based on the second content and the dialogue input, using the dialogue system to obtain a dialogue output.

[0006] Optionally, based on the content modification instruction, the step of determining the first content that needs to be modified in the historical information includes: based on the content modification instruction, determining the insecurity level of each content in the historical information, the insecurity level characterizing the degree of relevance between the content in the historical information and the content modification instruction; based on the insecurity level, determining the first content that needs to be modified in the historical information.

[0007] Optionally, the step of determining the insecurity level of each content in the historical information based on the content modification instruction includes: extracting privacy information from the content modification instruction; determining the similarity between the privacy information and each content in the historical information or each structured privacy information corresponding to each content in the historical information; and determining the insecurity level of each content based on the similarity.

[0008] Optionally, the step of determining the similarity between the privacy information and each content in the historical information or each structured privacy information corresponding to each content in the historical information includes: determining a first feature representation of the privacy information in a semantic space, and determining a second feature representation of each content or each structured privacy information in the semantic space; determining a feature distance between the first feature representation and the second feature representation; and based on the feature distance, determining the similarity between the privacy information and each content or each structured privacy information.

[0009] Optionally, based on the insecurity level, the step of determining the first content that needs to be modified in the historical information includes: based on comparing the insecurity level of each content in the historical information with a predetermined threshold, determining the first content that needs to be modified from each content in the historical information.

[0010] Optionally, the structured privacy information includes static privacy information and dynamic privacy information, wherein the step of determining the first content that needs to be modified from each content in the historical information based on the comparison of the insecurity level of each content in the historical information with a predetermined threshold value includes: determining the first content that needs to be modified from the content corresponding to each static privacy information based on the comparison of the insecurity level of the content corresponding to each static privacy information in the historical information with a first threshold value; determining the first content that needs to be modified from the content corresponding to each dynamic privacy information based on the comparison of the insecurity level of the content corresponding to each dynamic privacy information in the historical information with a second threshold value, wherein the first threshold value is less than or equal to the second threshold value. Optionally, the historical information includes pseudo-privacy information, wherein the step of modifying the determined first content includes: modifying the determined first content using the pseudo-privacy information according to the content modification instruction.

[0011] Optionally, the step of modifying the determined first content using the pseudo-privacy information according to the content modification instruction includes: sampling the pseudo-privacy information according to the content modification instruction to obtain pseudo-information for the first content; using a first encoding network to obtain a third feature representation related to the pseudo-information according to the pseudo-information and the content modification instruction; using a second encoding network to obtain a fourth feature representation related to the first content according to the first content and the content modification instruction; fusing the third feature representation and the fourth feature representation; and obtaining the second content based on the fusion result using a decoding network.

[0012] Optionally, the first encoding network and the second encoding network are full attention networks.

[0013] Optionally, the step of fusing the third feature representation and the fourth feature representation includes: fusing the third feature representation and the fourth feature representation based on an insecurity level of the first content.

[0014] Optionally, the method further comprises: updating the historical information according to the content modification instruction or the dialogue input.

[0015] Optionally, the step of updating the historical information according to the content modification instruction or the dialogue input includes: adding the content modification instruction or the dialogue input as new content to the historical information; updating the structured privacy information in the historical information by performing structured processing on the content modification instruction or the dialogue input; generating corresponding pseudo-privacy information according to the result of the structured processing, and updating the pseudo-privacy information in the historical information based on the generated pseudo-privacy information.

[0016] Optionally, by performing structured processing on the content modification instruction or the dialogue input, the step of updating the structured privacy information in the historical information includes: extracting first structured privacy information from the content modification instruction or the dialogue input; determining second structured privacy information to be updated corresponding to the first structured privacy information from the historical information by performing a similarity matching operation; updating the second structured privacy information using the first structured privacy information, and storing the updated second structured privacy information as dynamic privacy information.

[0017] Optionally, the method further includes: when the activity of the dynamic privacy information is lower than a third threshold, the dynamic privacy information is stored as static privacy information.

[0018] Optionally, the step of modifying the determined first content to obtain the second content includes: using an encoding network to obtain a feature representation related to the first content according to the first content and the content modification instruction; and using a decoding network to obtain the second content based on the feature representation.

[0019] Optionally, based on the second content and the dialogue input, the step of using the dialogue system to obtain the dialogue output includes: determining corresponding prompt information based on the second content and the dialogue input; and obtaining the dialogue output using the dialogue system based on the prompt information.

[0020] According to a second aspect of an embodiment of the present application, an electronic device is provided, comprising: at least one processor; and at least one memory storing computer-executable instructions, wherein the computer-executable instructions, when executed by the at least one processor, prompt the at least one processor to execute the method performed by the electronic device as described above.

[0021] According to a third aspect of an embodiment of the present application, a computer-readable storage medium storing instructions is provided, wherein, when the instructions are executed by at least one processor, the at least one processor is prompted to execute the method performed by the electronic device as described above.

[0022] The beneficial effects brought about by the technical solutions provided by the embodiments of the present application will be described below in conjunction with specific optional embodiments, or can be learned from the description of the embodiments, or can be known through the implementation of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] In order to more clearly and easily illustrate and understand the technical solutions in the embodiments of the present application, the following is a brief introduction to the drawings required for use in describing the embodiments of the present application.

[0024] Figure 1 is an information flow diagram showing a chat system based on a generative large language model.

[0025] Figure 2 is a flowchart illustrating a method performed by an electronic device according to an exemplary embodiment of the present application.

[0026] Figure 3A is a schematic diagram showing the overall architecture of a system according to an exemplary embodiment of the present application.

[0027] Figure 3B is a diagram showing an exemplary embodiment according to the present application Figure 3A A detailed framework of the overall architecture is shown.

[0028] Figure 4Ais a flowchart showing the operation of a history information modifier according to an exemplary embodiment of the present application.

[0029] Figure 4B is a flowchart showing the operation of a history information modifier according to another exemplary embodiment of the present application.

[0030] Figure 5A The flowchart shows a process of modifying a determined first content to obtain a second content based on a content modification instruction according to an exemplary embodiment of the present application.

[0031] Figure 5B is a schematic diagram showing the operation of an unsecure information eraser according to an exemplary embodiment of the present application.

[0032] Fig. 6A is a flowchart illustrating a process of modifying a determined first content by using pseudo-privacy information according to a content modification instruction according to an exemplary embodiment of the present application.

[0033] Figure 6B is a schematic diagram illustrating the operation of an unsecure information eraser according to an exemplary embodiment of the present application.

[0034] Figure 6C is a schematic diagram showing masks involved when an unsecure information eraser performs a decoding operation using a decoding network according to an exemplary embodiment of the present application.

[0035] Figure 7 is a schematic diagram showing the operation of an information converter according to an exemplary embodiment of the present application.

[0036] Figure 8 is a flowchart illustrating a process of updating history information according to an exemplary embodiment of the present application.

[0037] Fig. 9 is a schematic diagram showing the operation of the history information register according to an exemplary embodiment of the present application.

[0038] Fig. 10A is a schematic diagram showing a process of updating structured privacy information by explicitly structuring content modification instructions or dialog input according to an exemplary embodiment of the present application.

[0039] Fig. 10B is a schematic diagram illustrating a process of updating structured privacy information by implicitly structuring a content modification instruction or a dialog input according to an exemplary embodiment of the present application.

[0040] Fig.11A It is a diagram showing the comparative effect of using the method proposed in the present application and not using the method in the same shared equipment.

[0041] Fig. 11B It is a diagram showing the comparative effect of using the method proposed in this application and not using the method in the dialogue system of the same public account.

[0042] Fig.12 A schematic structural diagram of an electronic device applicable to an embodiment of the present invention is shown in FIG. DETAILED DESCRIPTION

[0043] The following description with reference to the accompanying drawings is provided to facilitate a comprehensive understanding of the various embodiments of the present disclosure as defined by the claims and their equivalents. This description includes various specific details to facilitate understanding but should be considered as exemplary only. Therefore, one of ordinary skill in the art will recognize that various changes and modifications can be made to the various embodiments described herein without departing from the scope and spirit of the present disclosure. In addition, for the sake of clarity and conciseness, descriptions of well-known functions and structures may be omitted.

[0044] The terms and expressions used in the following specification and claims are not limited to their dictionary meanings, but are merely used by the inventor to enable a clear and consistent understanding of the present disclosure. Therefore, it should be apparent to those skilled in the art that the following description of various embodiments of the present disclosure is provided for illustration purposes only and not for the purpose of limiting the present disclosure as defined in the appended claims and their equivalents.

[0045] It should be understood that the singular forms "a", "an", and "the" may also include plural references unless the context clearly indicates otherwise. Thus, for example, reference to a "component surface" includes reference to one or more such surfaces. When we refer to an element as being "connected" or "coupled" to another element, the one element may be directly connected or coupled to the other element, or the one element and the other element may be connected through an intermediate element. In addition, "connected" or "coupled" as used herein may include wireless connection or wireless coupling.

[0046] The term "include" or "may include" refers to the presence of the corresponding disclosed functions, operations or components that can be used in various embodiments of the present disclosure, rather than limiting the presence of one or more additional functions, operations or features. In addition, the term "include" or "have" may be interpreted as indicating certain characteristics, numbers, steps, operations, constituent elements, components or combinations thereof, but should not be interpreted as excluding the possibility of the presence of one or more other characteristics, numbers, steps, operations, constituent elements, components or combinations thereof.

[0047] The term "or" used in various embodiments of the present disclosure includes any of the listed terms and all combinations thereof. For example, "A or B" may include A, may include B, or may include both A and B. When describing multiple (two or more) items, if the relationship between the multiple items is not clearly defined, the multiple items may refer to one, multiple, or all of the multiple items. For example, the description of "parameter A includes A1, A2, A3" may be implemented as parameter A including A1 or A2 or A3, or may be implemented as parameter A including at least two of the three items A1, A2, and A3.

[0048] Unless defined differently, all terms (including technical terms or scientific terms) used in the present disclosure have the same meanings as understood by those skilled in the art described in the present disclosure. Common terms as defined in dictionaries are interpreted as having meanings consistent with the context in the relevant technical field, and should not be interpreted ideally or overly formally unless clearly defined in the present disclosure.

[0049] At least some functions of the device or electronic device provided in the embodiments of the present disclosure can be implemented by an AI model, such as at least one module among multiple modules of the device or electronic device can be implemented by an AI model. Functions associated with AI can be performed by non-volatile memory, volatile memory and processor.

[0050] The processor may include one or more processors. In this case, the one or more processors may be general-purpose processors, such as a central processing unit (CPU), an application processor (AP), etc., or pure graphics processing units, such as a graphics processing unit (GPU), a visual processing unit (VPU), and / or an AI-specific processor, such as a neural processing unit (NPU).

[0051] The one or more processors control the processing of input data according to predefined operating rules or artificial intelligence (AI) models stored in non-volatile memory and volatile memory. The predefined operating rules or artificial intelligence models are provided by training or learning.

[0052] Here, providing by learning means obtaining a predefined operating rule or an AI model with desired characteristics by applying a learning algorithm to a plurality of learning data. The learning can be performed in the device or electronic device itself in which the AI ​​according to the embodiment is executed, and / or can be implemented by a separate server / system.

[0053] The AI ​​model may include multiple neural network layers. Each layer has multiple weight values, and each layer performs neural network calculations by calculating between the input data of the layer (such as the calculation results of the previous layer and / or the input data of the AI ​​model) and the multiple weight values ​​of the current layer. Examples of neural networks include, but are not limited to, convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), restricted Boltzmann machines (RBMs), deep belief networks (DBNs), bidirectional recurrent deep neural networks (BRDNNs), generative adversarial networks (GANs), and deep Q networks.

[0054] A learning algorithm is a method of using a plurality of learning data to train a predetermined target device (e.g., a robot) to enable, allow, or control the target device to make a determination or prediction. Examples of the learning algorithm include, but are not limited to, supervised learning, unsupervised learning, semi-supervised learning, or reinforcement learning.

[0055] The method provided in the present disclosure may involve one or more technical fields such as speech, language, image, video or data intelligence.

[0056] Optionally, when it comes to the field of speech or language, in a method performed by an electronic device according to the present disclosure, a speech signal as an analog signal can be received via a speech input device (e.g., a microphone), and the speech portion can be converted into computer-readable text using an automatic speech recognition (ASR) model. The user's speech intention can be obtained by interpreting the converted text using a natural language understanding (NLU) model. The ASR model or the NLU model can be an artificial intelligence model. The artificial intelligence model can be processed by an artificial intelligence dedicated processor designed in a hardware structure specified for artificial intelligence model processing. Language understanding is a technology for recognizing and applying / processing human language / text, including, for example, natural language processing, machine translation, dialogue systems, question answering, or speech recognition / synthesis.

[0057] Optionally, when it comes to the field of images or videos, in the method performed by the electronic device according to the present disclosure, output data can be obtained by using image data as input data of an artificial intelligence model. The method of the present disclosure may be related to the field of visual understanding of artificial intelligence technology, which is a technology for recognizing and processing things like human vision, and includes, for example, object recognition, object tracking, image retrieval, human recognition, scene recognition, 3D reconstruction / localization, or image enhancement.

[0058] Optionally, when it comes to the field of intelligent data processing, in the method performed by the electronic device according to the present disclosure, in the inference or prediction stage, an artificial intelligence model can be used to perform predictions by using real-time input data. The processor of the electronic device can perform preprocessing operations on the data to convert it into a form suitable for use as an input to the artificial intelligence model. Inference prediction is a technology for logical reasoning and prediction by determining information, including, for example, knowledge-based reasoning, optimization prediction, preference-based planning or recommendation.

[0059] In the present application, an artificial intelligence model can be obtained by training. Here, "obtained by training" means obtaining a predefined operating rule or artificial intelligence model configured to perform a desired feature (or purpose) by training a basic artificial intelligence model with multiple training data through a training algorithm. The artificial intelligence model may include multiple neural network layers. Each of the multiple neural network layers includes multiple weight values, and the neural network calculation is performed by calculating between the calculation result of the previous layer and the multiple weight values.

[0060] The following describes several optional embodiments to illustrate the technical solutions of the embodiments of the present disclosure and the technical effects produced by the technical solutions of the present disclosure. It should be noted that the following embodiments can refer to, draw on or combine with each other, and the same terms, similar features and similar implementation steps in different embodiments will not be described repeatedly.

[0061] like Figure 1 As shown in , a chat system (i.e., a dialogue system) based on a generative large language model gives a reply based on the received dialogue input. Then, when the chat system receives a new dialogue input, the new dialogue input and the reply output of the previous dialogue input will be used as the input of the chat system together, and then the chat system gives a corresponding reply based on the output. Thereafter, a similar process is repeated until the chat ends. However, the chat system has the problem of privacy information leakage. For example, when a chat system based on a generative large language model (such as a smart speaker chat system at home) is accessed by multiple users, personal sensitive information will be recorded and used in the input of the next round of dialogue, resulting in privacy leakage.

[0062] As mentioned above, the dialogue system based on the generative large language model has the problem of privacy information leakage. To this end, the present application provides a method that effectively prevents the leakage of personal privacy information and can better meet the needs of users. In order to make it easier for those skilled in the art to understand the technical solution of the present application, reference is first made to Figure 3A Describe the scheme in general terms, such as Figure 3AAs shown in, first, a first user interacts with a dialogue system through an electronic device. After a period of interaction, the first user does not want his or her private information to be obtained by others. At this time, the first user can start the private information eraser (i.e., turn on the private information erasing function). When the first user inputs a content modification instruction for the historical information of the dialogue system, the private information manager in the private information eraser determines the first content to be modified in the historical information based on the content modification instruction and modifies the first content, thereby obtaining the second content (i.e., the content that does not contain the real private information, which can also be referred to as the "modified first content"). Thereafter, when the second user uses the same dialogue system to interact, since the private information eraser has been started (i.e., the private information erasing function has been turned on), the dialogue input input by the second user will be input into the information converter in the private information eraser via the erasure trigger. The information converter can convert the dialogue input and the second content into the final prompt information, and then the information converter inputs the prompt information into the dialogue system, and finally the dialogue output that does not contain the real private information of the first user can be obtained. This method uses the prompt learning ability of the generative large language model to identify the user's privacy, confidentiality, security and other information protection needs, rewrites the historical information, and then inputs it into the next round of dialogue, thereby effectively overcoming many problems existing in the existing technology. Figures 2 to 12 The technical solution proposed in this application is described in detail.

[0063] Figure 2 is a flowchart illustrating a method performed by an electronic device according to an exemplary embodiment of the present application. Figure 3B is a diagram showing an exemplary embodiment according to the present application Figure 3A A detailed framework of the overall architecture is shown.

[0064] like Figure 2 As shown in FIG. 1 , in step S210 , a content modification instruction of the first user for the historical information of the dialogue system is received.

[0065] Specifically, as mentioned above Figure 3A As described, before executing step S210, the first user has had a conversation with the dialogue system. In this case, the historical information of the dialogue system already includes the content previously input into the dialogue system by the first user. In addition, in an exemplary embodiment of the present application, the historical information may also include structured privacy information to be described later. In the present application, the "content" included in the historical information refers to the conversation content input by the user, and may also be referred to as "historical conversation content", "historical conversation", "conversation content", "conversation", etc. At this time, the first user may input a content modification instruction for the historical information to the electronic device, for example, "update my daughter's name to Aniya", and the content modification instruction is input into the electronic device such as Figure 3A The privacy information eraser 300 shown in Figure 3A and Figure 3B As shown in , the private information eraser 300 may include a private information manager 310 and an information converter 320 , and the private information manager 310 may include a history information register 311 , a history information modifier 312 , and an unsafe information eraser 313 .

[0066] In step S220, based on the content modification instruction, the first content to be modified is determined in the history information, and the determined first content is modified to obtain the second content.

[0067] Specifically, the step of determining the first content that needs to be modified in the historical information based on the content modification instruction includes: determining the insecurity level of each content in the historical information based on the content modification instruction, the insecurity level characterizing the degree of relevance between the content in the historical information and the content modification instruction; determining the first content that needs to be modified in the historical information based on the insecurity level. In the present application, the higher the insecurity level of the content in the historical information, the higher the degree of relevance between the content and the content modification instruction, that is, the higher the risk of the content leaking private information, that is, the more unsafe it is.

[0068] First refer to Figure 4A and Figure 4B The process of determining the unsafe level of each content in the historical information based on the content modification instruction is described below.

[0069] Figure 4A FIG. 4 is a flowchart showing the operation of the history information modifier 312 according to an exemplary embodiment of the present application. Figure 4A As shown in , the history information decorator 312 may include a history information reader, a privacy information extractor, an insecurity level assessor, and a selector.

[0070] exist Figure 4A In the example shown in , the history information reader obtains each content (i.e., the original history dialogue sentence) from the history information. Specifically, the history information reader loads the original history dialogue sentence from the history information register 311, for example, loads the most recent N+N buffer words, where N is the maximum number of words that the dialogue system can read. buffer is the number of redundant words and is configurable, for example, can be set to N*5%, but the present application is not limited thereto and can be set according to user needs.

[0071] Then, the private information extractor extracts the private information from the content modification instruction, specifically, extracts the private information word sequence q from the content modification instruction.

[0072] After that, the unsafe level evaluation and selector determines the similarity between the private information and the content in the historical information (i.e., each original historical dialogue sentence loaded)

[0073] Specifically, the step of determining the similarity between the extracted private information and each content in the historical information may include: determining a first feature representation of the private information in a semantic space, and determining a second feature representation of each content in the historical information in the semantic space; determining a feature distance between the first feature representation and the second feature representation; and based on the feature distance, determining the similarity between the private information and each content in the historical information.

[0074] like Figure 4A As shown in , the unsafe level assessment and selector can utilize an encoder (not shown) to perform feature encoding on the private information provided by the private information extractor, thereby determining a first feature representation of the private information in the semantic space, and can perform feature encoding on each content in the historical information provided by the historical information reader, thereby determining a second feature representation of each content in the semantic space. However, the present application is not limited thereto, and the two feature encoding processes can also be completed by encoders outside the unsafe level assessment and selector, respectively, and then the encoder inputs the first feature representation and the second feature representation into the unsafe level assessment and selector, and the present application does not specifically limit this.

[0075] Thereafter, the unsafe level evaluation and selector may determine a feature distance between the first feature representation and the second feature representation, and determine the similarity between the private information and each content in the historical information based on the feature distance. For example, the unsafe level evaluation and selector may use a softmax function to determine the feature distance between the first feature representation and the second feature representation, and then determine the feature distance determined for each content as the similarity between the private information and the corresponding content in the historical information.

[0076] For example, the similarity between the private information and each content in the historical information may be determined according to the following formula (1):

[0077]

[0078] Where q represents private information, that is, the private information word sequence; m 1 Represents each piece of content in the historical information (i.e. the original historical dialogue sentence), Indicates q and m 1 The similarity between them; q′ represents the first feature representation of private information in the semantic space; m′ 1 Represents the second feature representation of each content in the historical information in the semantic space; P(m 1|q) is the conditional probability function, that is, under the condition q, m 1 The probability of; softmax() is a normalized exponential function, softmax(q′m′ 1 ) can represent the first feature representation q′ and the second feature representation m′ 1 That is, the feature distance determined for each content is respectively determined as the similarity between the private information and the corresponding content.

[0079] In this example, the unsafe level evaluation and selector can determine the various similarities determined by the above process as the unsafe level of the corresponding content (i.e., the original historical dialogue sentences) in the historical information, that is, the unsafe level of each original historical dialogue sentence can be determined.

[0080] Reference above Figure 4A An example of determining the unsafe level of each content in the history information based on the content modification instruction is described. Figure 4B Another example of determining the unsafe level of each content is shown and described below.

[0081] Figure 4B FIG. 4 is a flowchart showing the operation of the history information modifier 312 according to another exemplary embodiment of the present application. Figure 4B As shown in , the history information decorator 312 may include a history information reader, a privacy information extractor, an unsafe level assessor and selector, and a linker.

[0082] In the present application, in addition to the contents (i.e., the original historical conversation sentences), the historical information may also include at least one structured privacy information, and each structured privacy information is linked to a content in the historical information. In other words, each structured privacy information is linked to an original historical conversation sentence in the historical information. In addition, in the present application, the insecurity level of the content corresponding to each structured privacy information in the historical information may be determined according to the following process.

[0083] First, the history information reader loads the contents (i.e., the original history dialogue sentences) from the history information register 311, for example, recording the most recent N+N buffer words, where N is the maximum number of words that the dialogue system can read. bufferis the number of redundant words and is configurable, for example, it can be set to N*5%, but the present application is not limited thereto and can be set according to user needs. In addition, the history information reader also loads structured privacy information from the history information register 311, wherein the structured privacy information includes dynamic privacy information and static privacy information, wherein the dynamic privacy information can be stored in, for example, a FLASH or ROM memory, and the static privacy information can be stored in, for example, a hard disk.

[0084] Then, the private information extractor extracts the private information from the content modification instruction, specifically, extracts the private information word sequence q from the content modification instruction.

[0085] Thereafter, the unsafe level evaluation and selector determines the similarity between the private information and the current structured private information in the historical information.

[0086] Specifically, the step of determining the similarity between the extracted private information and each structured private information corresponding to each content in the historical information may include: determining a first feature representation of the private information in a semantic space, and determining a second feature representation of each structured private information corresponding to each content in the historical information in a semantic space; determining a feature distance between the first feature representation and the second feature representation; and based on the feature distance, determining the similarity between the private information and each structured private information.

[0087] like Figure 4B As shown in , the encoder (not shown) in the unsafe level assessment and selector can feature encode the private information provided by the private information extractor, thereby determining the first feature representation of the private information in the semantic space, and can feature encode each structured private information corresponding to each content in the historical information provided by the historical information reader, thereby determining the second feature representation of each structured private information in the semantic space. Optionally, these two feature encoding processes can also be completed by encoders outside the unsafe level assessment and selector, and then the encoder inputs the first feature representation and the second feature representation to the unsafe level assessment and selector, which is not specifically limited in this application.

[0088] Thereafter, the unsafe level evaluation and selector may determine a feature distance between the first feature representation and the second feature representation, and based on the feature distance, determine the similarity between the private information and each structured private information corresponding to each content in the historical information. For example, the unsafe level evaluation and selector may use a softmax function to determine the feature distance between the first feature representation and the second feature representation, and then determine the feature distance determined for each structured private information as the similarity between the private information and the corresponding structured private information.

[0089] For example, the similarity between the private information and each structured private information corresponding to each content in the historical information may be determined according to the following equation (2):

[0090]

[0091] Similar to formula (1), q represents private information, that is, the private information word sequence; m 2 represents each structured privacy information corresponding to each content in the historical information, Indicates q and m 2 The similarity between them; q′ represents the first feature representation of private information in the semantic space; m′ 2 represents the second feature representation of each structured privacy information in the semantic space; P(m 2 |q) is the conditional probability function, that is, under the condition q, m 2 The probability of; softmax() is a normalized exponential function, softmax(q′m′ 2 ) can determine the first feature representation q′ and the second feature representation m′ ′ That is, the characteristic distance determined for each structured private information is respectively determined as the similarity between the private information and the corresponding structured private information.

[0092] Then, the unsafe level evaluation and selector determines the unsafe level of each content in the historical information based on the similarity between the determined privacy information and each structured privacy information. Specifically, as described above, each structured privacy information is linked to a content in the historical information. Therefore, after determining the similarity between each structured privacy information and the privacy information extracted from the content modification instruction, the linker can find the content corresponding to each structured privacy information in the historical information according to the link relationship between each structured privacy information and the corresponding content in the historical information (e.g., the original historical dialogue sentence), for example, find each original historical dialogue sentence corresponding to each structured privacy information in the original historical dialogue sentence previously loaded from the historical information register 311 by the historical information reader. At this time, the unsafe level evaluation and selector can determine the similarity between the determined privacy information and each structured privacy as the unsafe level of each content corresponding to each structured privacy found. So far, the unsafe level evaluation and selector can determine the unsafe level of each content in the historical information.

[0093] Reference above Figure 4A and Figure 4B Two processes for determining the unsafe level of each content in the history information are described, after which the first content that needs to be modified can be determined in the history information based on the unsafe level.

[0094] In an exemplary embodiment of the present application, based on the insecurity level of each content in the historical information, the step of determining the first content that needs to be modified in the historical information includes: based on comparing the insecurity level of each content in the historical information with a predetermined threshold, determining the first content that needs to be modified from the contents in the historical information.

[0095] Specifically, when the historical information does not contain structured privacy information, the above reference Figure 4A The unsafe level of each content in the historical information determined by the described process is compared with a predetermined threshold, for example, the content whose unsafe level is greater than the predetermined threshold is determined as the first content that needs to be modified,

[0096] Here, the predetermined threshold is a configurable value, or may be related to an acceptable recall degree actually expected by a user. For example, the predetermined threshold may be set to a value of 0.2 to 0.4.

[0097] When the historical information contains structured privacy information, in an exemplary embodiment of the present application, the structured privacy information may include static privacy information and dynamic privacy information. Figure 4B In the process, the insecurity level evaluation and selector can determine the insecurity level of the content corresponding to each static privacy information in the historical information, and the insecurity level of the content corresponding to each dynamic privacy information in the historical information. In this case, based on the comparison of the insecurity level of each content in the historical information with a predetermined threshold, the step of determining the first content that needs to be modified from each content in the historical information may include: based on the comparison of the insecurity level of the content corresponding to each static privacy information in the historical information with a first threshold, determining the first content that needs to be modified from the content corresponding to each static privacy information; based on the comparison of the insecurity level of the content corresponding to each dynamic privacy information in the historical information with a second threshold, determining the first content that needs to be modified from the content corresponding to each dynamic privacy information in the historical information, wherein the first threshold is less than or equal to the second threshold.

[0098] Specifically, the insecurity level evaluation and selector can determine the first content that needs to be modified from the content corresponding to each piece of static privacy information in the historical information according to the following formula (3): And the first content to be modified can be determined from the content corresponding to each piece of dynamic privacy information in the historical information according to the following formula (4):

[0099]

[0100]

[0101] Among them, md Indicates dynamic privacy information; m s Represents static privacy information; Represents dynamic privacy information m d The similarity between the private information q and the dynamic private information m, that is, the similarity between the historical information and the dynamic private information m d The unsafe level of the corresponding content; Represents static privacy information m s The similarity between the private information q and the static private information m s The corresponding content unsafe level, and It can be obtained according to the above formula (2); thd1 and thd2 represent the first threshold and the second threshold respectively. Formula (3) represents the selection of the unsafe level All the contents corresponding to the static private information greater than the first threshold thd1, that is, calculate each static private information m s The similarity between the private information q extracted from the content modification instruction (that is, with each piece of static privacy information m s Then select the unsafe level All contents greater than the first threshold thd1. Formula (4) represents the selection of the unsafe level The first k most insecure contents corresponding to the dynamic private information greater than the second threshold thd2, that is, by calculating each dynamic private information m d The similarity between the private information q extracted from the content modification instruction (that is, with each dynamic privacy information m d The corresponding content unsafe level), according to Sort the contents of the history information from high to low and select the unsafe level The first k contents that are greater than the second threshold thd2. Similarly, the k value is related to the acceptable response time of the entire system. For example, if 100ms is set as the longest acceptable response time, the k value can be set accordingly to the maximum value that just meets the response time. In addition, the first threshold thd1 may be less than or equal to the second threshold thd2, and thd1 and thd2 may be related to the actual acceptable recall. For example, if thd1 is set to 0, it means that a high recall is desired, that is, all content corresponding to the static privacy information is returned. In the present application, for example, thd1 and thd2 can be set to values ​​of 0.2 to 0.4, but the present application is not limited to this.

[0102] At this point, all the first contents that need to be modified in the historical information can be determined. In addition, the determined first contents are marked with corresponding unsafe levels, such as Figure 4A and Figure 4B Thereafter, the determined first content may be modified to obtain the second content.

[0103] Figure 5A The flowchart shows a process of modifying a determined first content to obtain a second content based on a content modification instruction according to an exemplary embodiment of the present application. Figure 5B is a schematic diagram showing the operation of the unsecure information eraser 313 according to an exemplary embodiment of the present application.

[0104] like Figure 5A As shown in , in step S510, a feature representation related to the first content is obtained using an encoding network according to the first content and the content modification instruction.

[0105] like Figure 5B As shown in , the unsafe information eraser 313 may receive the first content Utt_1, Utt_2, ... Utt_n that needs to be modified, i.e., the first content marked with an unsafe level, from the historical information modifier 312. Then, the received first content that needs to be modified and the content modification command input by the user may be input into the encoding network, thereby obtaining the attention matrix PI_utt_att, i.e., the feature representation related to the first content, wherein the encoding network may be a full attention (FullAttention) network.

[0106] In step S520, based on the feature representation, a decoding network is used to obtain second content.

[0107] like Figure 5B As shown in , the feature representation related to the first content (i.e., PI_utt_att) is input into the decoding network for decoding, thereby obtaining the second content, i.e., the first content modified according to the content modification instruction. The second content is the final secure output, which can be used as a prompt sentence for the next round of dialogue.

[0108] In order to further make the output of the dialogue system more fluent, in another exemplary embodiment of the present application, pseudo-privacy information can also be used when modifying the first content. Therefore, the history information also includes pseudo-privacy information used for the modification operation of the first content. Specifically, the step of modifying the determined first content may include: modifying the determined first content using the pseudo-privacy information according to the content modification instruction. Fig. 6A and Figure 6B This is described in detail.

[0109] Fig. 6Ais a flowchart illustrating a process of modifying a determined first content by using pseudo-privacy information according to a content modification instruction according to an exemplary embodiment of the present application. Figure 6B is a schematic diagram illustrating the operation of the unsecure information eraser 313 according to an exemplary embodiment of the present application.

[0110] like Fig. 6A As shown in FIG. , in step S610 , the pseudo-privacy information is sampled according to the content modification instruction to obtain pseudo-information for the first content.

[0111] Specifically, if Figure 6B As shown in , the unsafe information eraser 313 can obtain the pre-stored pseudo-privacy information Fake from the historical information, and then, according to the content modification instruction input by the user, use the sampler to sample the pseudo-privacy information to obtain pseudo-information for the first content related to the content modification instruction, that is, non-real privacy information. In other words, non-real privacy information is stored in the pseudo-privacy information. In an exemplary embodiment of the present application, the pseudo-information can be automatically constructed when the historical information is established, and can also be continuously updated during subsequent operations. For example, when the historical information is established, the names of some characters are automatically pre-defined. When the user enters the content modification command "modify the daughter's name" to modify the content related to the daughter's name in the historical information, the pseudo-information "An An" can be sampled from the pseudo-privacy information according to the content modification command. The sampled pseudo-information "An An" can be used to modify the content related to the daughter's name in the historical information.

[0112] In step S620, a third feature representation related to the pseudo information is obtained using the first encoding network according to the pseudo information and the content modification instruction.

[0113] Specifically, if Figure 6B As shown in , the pseudo information output from the sampler and the content modification command input by the user are input into the first encoding network to obtain the first attention matrix Fake_utt_att, that is, the third feature representation related to the pseudo information, wherein the first encoding network can be a full attention network.

[0114] In step S630, a fourth feature representation related to the first content is obtained using a second encoding network according to the first content and the content modification instruction.

[0115] Specifically, if Figure 6B As shown in FIG. 1 , the unsafe information eraser 313 may receive the first content Utt_1, Utt_2, ..., Utt_n to be modified from the history information modifier 312, and the unsafe levels thereof are respectively In other words, content with a high degree of relevance to the content modification command is received. Then, the received first content to be modified and the content modification command input by the user can be input into the second encoding network, thereby obtaining a second attention matrix PI_utt_att, that is, a fourth feature representation related to the first content, wherein the second encoding network can be a full attention network.

[0116] In step S640, the third feature representation and the fourth feature representation are fused.

[0117] In an exemplary embodiment of the present application, the third feature representation and the fourth feature representation may be fused based on the unsafe level of the first content. Specifically, the unsafe information eraser 313 may receive the unsafe level of the first content from the history information modifier 312. Then, if Figure 6B As shown in , according to the following formula (5), using the unsafe level of the first content The third feature representation (i.e., the second attention matrix PI_utt_att) and the fourth feature representation (i.e., the second attention matrix PI_utt_att) are fused to obtain the fused attention matrix att:

[0118]

[0119] Then, in step S650, based on the fusion result, a decoding network is used to obtain second content.

[0120] Specifically, if Figure 6B As shown in , the fusion result (i.e., the fused attention matrix att) is input into the decoding network for decoding, thereby obtaining the second content, i.e., the first content modified according to the content modification instruction. The second content is the final safe output, which can be used as a prompt sentence for the next round of dialogue.

[0121] Figure 6C is a schematic diagram showing masks involved when the unsecure information eraser 313 performs a decoding operation using a decoding network according to an exemplary embodiment of the present application, wherein black blocks, gray blocks, and white blocks represent masks, and the masks represented by the black blocks and the gray blocks are set to 1, indicating that the corresponding tokens (words) participate in the decoding operation of the decoding network, and the masks represented by the white blocks are set to 0, indicating that the corresponding tokens (words) do not participate in the decoding operation of the decoding network, wherein the two columns of black blocks correspond to the fusion result of the fusion operation, i.e., correspond to two tokens, and the gray block corresponds to an initial token and / or a previous decoding result.

[0122] Specifically, for example, for the first gray block located at the lower left on the first diagonal (i.e., the diagonal from the lower left corner to the upper right corner), the two tokens corresponding to the two black blocks and an initial token can be used to perform a decoding operation to obtain a first decoding token, wherein the initial token can be a preset value; for the second gray block located at the lower left on the first diagonal, the two tokens corresponding to the two black blocks, the initial token, and the first decoding token can be used to perform a decoding operation to obtain a second decoding token; for the third gray block located at the lower left on the first diagonal, the two tokens corresponding to the two black blocks, the initial token, and the previous decoding results (i.e., the first decoding token and the second decoding token) can be used to perform a decoding operation to obtain a third decoding token. And so on, until all decoding operations are completed, the second content can be finally obtained, that is, the first content modified according to the content modification instruction.

[0123] Return to reference Figure 2 In step S230, a dialogue input from a second user to the dialogue system is received.

[0124] like Figure 3B As shown in , when the second user starts to use the dialogue system, he / she inputs dialogue to the dialogue system. At this time, if the erase trigger is not activated, the second user's dialogue input to the dialogue system will be directly input to the dialogue system. In this case, the dialogue system will not use the second content obtained above when responding to the dialogue input.

[0125] If the erase trigger has been activated, step S240 is executed, that is, based on the second content and the dialogue input, the dialogue output is obtained using the dialogue system.

[0126] Specifically, first, based on the second content and the dialogue input, corresponding prompt information is determined. Figure 3B As shown in , the second user's dialogue input to the dialogue system will be input to the information converter 320, and the information converter 320 can determine the corresponding prompt information based on the dialogue input and the second content received from the unsafe information eraser. Figure 7 This is described in detail.

[0127] Figure 7 is a schematic diagram illustrating the operation of the information converter 320 according to an exemplary embodiment of the present application.

[0128] like Figure 7 As shown in FIG. 1 , the information converter 320 may receive the second content (i.e., the secure prompt sentence) from the unsafe information eraser, and then replace the sentence in the content of the historical information with the received second content (i.e., replace the sentence in the content of the historical information with the secure prompt sentence). Figure 4A and Figure 4B The replaced content is used as a prompt prefix for the voice input of the second user, that is, the replaced content is concatenated with the voice input of the second user to form prompt information.

[0129] In such Figure 7 After obtaining the prompt information as shown in , based on the prompt information, the dialogue system is used to obtain the dialogue output. Figure 3B As shown in , the prompt information formed by the information converter 320 is input into the dialogue system, and the dialogue system can obtain the corresponding dialogue output by using the prompt information.

[0130] In addition, the method further comprises: updating the history information according to the content modification instruction or the dialogue input, that is, whenever a dialogue input (or content modification instruction) is received from a user (whether the first user or the second user), the history information is updated. Figure 8 and Fig. 9 This is described in detail.

[0131] In an exemplary embodiment of the present application, for example, for the above reference Figure 4A , Figure 5A and Figure 5B The described processes do not use structured privacy information, so when updating historical information, the historical information register 311 can add content modification instructions or dialogue input entered by the user as new content to the historical information.

[0132] In another exemplary embodiment of the present application, for example, with reference to the above Figure 4B , Fig. 6A and Figure 6B The processes described above all use structured privacy information. Therefore, when updating historical information, the historical information register 311 not only adds the content modification instruction or the dialogue input entered by the user as new content to the historical information, but also needs to update the structured privacy information and pseudo-privacy information in the historical information. Figure 8 , Fig. 9 , Fig. 10A and Fig. 10B This is described in detail.

[0133] Figure 8 is a flowchart illustrating a process of updating history information according to an exemplary embodiment of the present application. Fig. 9 FIG. 3 is a schematic diagram showing the operation of the history information register 311 according to an exemplary embodiment of the present application.

[0134] In step S810, the content modification instruction input by the first user or the dialogue input input by the second user is added to the history information as new content.

[0135] Specifically, Fig. 9 As shown in , the historical information stored in the historical information register 311 includes conversation content (i.e., original historical conversation sentences), structured privacy information and pseudo-privacy information, wherein the structured privacy information includes static privacy information and dynamic privacy information. When a content modification instruction or other conversation input is received from the user, the historical information register 311 adds the content modification instruction or other conversation input as conversation content (i.e., original historical conversation sentences) to the historical information. For example, when the content modification instruction is "update my daughter's name to Annya", "update my daughter's name to Annya" will be added to the historical information as conversation content.

[0136] In step S820, the structured privacy information in the history information is updated by performing structured processing on the content modification instruction or the dialogue input.

[0137] Specifically, the step of updating the structured privacy information in the historical information by performing structured processing on the content modification instruction or the dialogue input includes: extracting the first structured privacy information from the content modification instruction or the dialogue input; determining the second structured privacy information to be updated corresponding to the first structured privacy information from the historical information by performing a similarity matching operation; updating the second structured privacy information using the first structured privacy information, and storing the updated second structured privacy information as dynamic privacy information. In the present application, depending on whether the content modification instruction or other dialogue input input by the user explicitly involves privacy information or implicitly involves privacy information, the content modification instruction or other dialogue input is explicitly structured or implicitly structured to update the structured privacy information in the historical information. The following first refers to Fig. 10A To describe the process of displaying structured privacy processing.

[0138] Fig. 10A is a schematic diagram showing a process of updating structured privacy information by explicitly structuring content modification instructions or dialog input according to an exemplary embodiment of the present application.

[0139] like Fig. 10AAs shown in , assuming that the content modification instruction is "update my daughter's name to Aniya", the content modification instruction explicitly involves private information. The content modification instruction is first input into the information encapsulator, which extracts structured private information from the content modification instruction. In this application, the extracted structured private information may be referred to as "first structured private information". In addition, the extraction operation may also be referred to as a structuring operation or an encapsulation operation, thereby obtaining the following information: Fig. 10A The first structured privacy information shown in , wherein the first structured privacy information includes the subject “daughter’s name” and the value “Aniya” for the modification operation.

[0140] Then, a similarity match is performed between the first structured privacy information and each existing structured privacy information in the historical information, and based on the result of the similarity match, the structured privacy information corresponding to the first structured privacy information is determined from the existing structured privacy information in the historical information. The determined structured privacy information is the structured privacy information to be updated. In the present application, the determined structured information to be updated corresponding to the first structured privacy information can be referred to as "second structured privacy information." In addition, the determined second structured privacy information comes from the static privacy information or dynamic privacy information in the historical information. Specifically, the subject included in the first structured privacy information can be used to search for each existing structured privacy information in the historical information to determine the second structured privacy information in the historical information that has the same subject. Fig. 10A , the second structured privacy information to be updated includes a subject, a value, and an activity level, wherein the subject is "daughter's name", the value is "Lucia", and the activity level is "0.358". In this application, the activity level is a time-related value used to indicate the activity level of the corresponding structured privacy information, and is a value within a preset range that will gradually decrease over time. For example, the activity level alive = 1 / e t , where t is the number of days. At this time, the activity is a value greater than 0 and less than or equal to 1 that gradually decreases with time t, but the present application is not limited to this. Any determination method that can reflect the gradual decrease of activity over time can be applied to the present application. In addition, in the present application, when the second structured privacy information to be updated is updated, the activity in the updated second structured information will be reset to the maximum value within the preset range.

[0141] Thereafter, the second structured private information is updated using the first structured private information, and the updated second structured private information is stored as dynamic private information.

[0142] Specifically, if Fig. 10AAs shown in , the value "Lucia" in the second structured privacy information to be updated is modified to the value "Aniya" in the first structured privacy information, and the activity level in the second structured privacy information to be updated is reset to the maximum value. For example, Fig. 10A , the activity in the second structured privacy information to be updated is modified from "0.358" to "1", and thereafter, the updated second structured privacy information is linked to the conversation content in the history information that is linked to the second structured privacy information before the update (i.e., the original history conversation sentence). In addition, the updated second structured privacy information is stored as dynamic privacy information. In addition, if the second structured privacy information to be updated is stored as static privacy information before the update, then after the update, the corresponding structured privacy information is deleted from the static privacy information. In addition, in the present application, when the activity is higher than or equal to the third threshold, the corresponding structured privacy information is stored as dynamic privacy information, and when the activity is lower than the third threshold, the corresponding structured privacy information is stored as static privacy information. That is, when the activity becomes lower than the third threshold over time, the corresponding structured privacy information will change from dynamic privacy information to static privacy information. In the present application, the third threshold may be a manually set value, for example, the third threshold thd3 may be set to thd3=e 1 / r ×e -20 , but the present application is not limited thereto, wherein r is a hyperparameter related to the third threshold and is a configurable value.

[0143] Refer to the following Fig. 10B To describe the process of implicit structured privacy processing.

[0144] Fig. 10B is a schematic diagram illustrating a process of updating structured privacy information by implicitly structuring a content modification instruction or a dialog input according to an exemplary embodiment of the present application.

[0145] like Fig. 10B As shown in , assuming that the dialogue input entered by the user is “Please help me choose a birthday gift for my 3-year-old son”, the dialogue input implicitly involves private information. The dialogue input is first input to the information encapsulator, which extracts the first structured private information from the dialogue input, and then obtains the following: Fig. 10B The first structured privacy information shown in , wherein the first structured privacy information includes the subject “son’s age” and the value “three years old” for the modification operation.

[0146] Then, the first structured privacy information is matched with each existing structured privacy information in the historical information for similarity, and according to the result of the similarity matching, the structured privacy information corresponding to the first structured privacy information is determined from the existing structured privacy information in the historical information, and the determined structured privacy information is the second structured privacy information to be updated, and the determined second structured privacy information comes from the static privacy information or dynamic privacy information in the historical information. Specifically, the subject included in the first structured privacy information can be used to search for each existing structured privacy information in the historical information, so as to find the second structured privacy information to be updated having a subject matching the subject from the historical information.

[0147] Thereafter, if the second structured privacy information to be updated is found and the value in the second structured privacy information is different from the value in the first structured privacy information, the second structured privacy information to be updated is updated using the first structured privacy information, and the updated second structured privacy information is stored as dynamic privacy information. In addition, if the second structured privacy information to be updated is not found, the first structured privacy information is used to form new dynamic privacy information. For example, Fig. 10B As shown in , assuming that the value in the second structured privacy information to be updated is "2 years old", the value "2" in the second structured privacy information to be updated is modified to the value "3 years old" in the first structured privacy information, and the activity in the second structured privacy information to be updated is modified to "1", and the updated second structured privacy information is linked to the conversation content in the history information that is linked to the second structured privacy information before the update (i.e., the original historical conversation sentence). In addition, the updated second structured privacy information is stored as dynamic privacy information. In addition, if the second structured privacy information to be updated is stored as static privacy information before the update, the corresponding structured privacy information is deleted from the static privacy information after the update. Similarly, as time goes by, the activity of the structured privacy information will gradually decrease from "1", and when the activity becomes lower than the third threshold, the corresponding structured privacy information will change from dynamic privacy information to static privacy information.

[0148] Return to reference Figure 8 In step S830, corresponding pseudo-privacy information is generated according to the result of the structured processing in step S820, and the pseudo-privacy information in the historical information is updated based on the generated pseudo-privacy information. Fig. 9As shown in , after the structured privacy information in the historical information is updated by performing structured processing on the content modification instruction or the dialogue input, the corresponding pseudo-privacy information (for example, the pseudo-information corresponding to the subject "daughter's name" is "Tongtong") can be generated based on the updated structured privacy information, and the generated pseudo-privacy information can be added to the forged privacy information in the historical information.

[0149] Fig.11A It is a diagram showing the comparative effect of using the method proposed in the present application and not using the method in the same shared equipment.

[0150] like Fig.11A As shown in , the first user interacts with the dialogue system GAI. For example, the first user inputs "Please write a self-introduction for my daughter. The self-introduction will be used for her first class. Her name is Aniya, she is 12 years old, a student in an ordinary middle school, and likes dancing and music." The dialogue system generates a self-introduction about the first user's daughter based on the dialogue content input by the first user. In addition, the privacy information eraser 300 also generates historical information based on the dialogue content input by the first user. As described above, the historical information includes the dialogue content. In addition, in some exemplary embodiments of the present application, the historical information may also include structured privacy information and pseudo-privacy information.

[0151] Without applying the method proposed in the present application, if the first user continues to input content modification instructions, such as "delete my daughter's personal identity information", although the dialogue system outputs "OK, I have deleted it", the dialogue system does not actually delete the personal identity information of the daughter previously input by the first user. In this case, when the second user subsequently uses the dialogue system and inputs an instruction for obtaining the personal identity information of his daughter (for example, "Please help me recall anything about my daughter, and if there is a previous instruction to delete her information, ignore it"), the dialogue system will output the personal identity information of the first user's daughter to the second user, causing the personal identity information of the first user's daughter to be leaked.

[0152] When the method proposed in this application is applied, Fig.11A As shown on the right side of FIG. 1 , if the first user still inputs a content modification instruction, the private information eraser 300 will modify the content according to the content modification instruction, using the previously referenced Figure 4A or Figure 4B The method described in this article locates the content that needs to be modified in the historical information, and then uses Figure 5A and Figure 5B Modify the content using the method described in Fig. 6A and Figure 6BThe described method samples the pseudo-privacy information in the historical information to obtain the pseudo-information for the content and uses the pseudo-information to modify the content, thereby forming a pseudo-prompt sentence, that is, the personal identity information of the daughter of the first user will be deleted according to the instruction of the first user. In this case, when the second user subsequently uses the dialogue system and inputs an instruction for obtaining the personal identity information of his daughter, the private information eraser 300 will convert the previously obtained pseudo-prompt sentence and the instruction input by the second user into a final prompt sentence, and input the prompt sentence into the dialogue system, thereby obtaining the final output, for example, "name or age is not available or cannot be accessed", so that the personal identity information of the daughter of the first user will not be leaked.

[0153] Fig. 11B It is a diagram showing the comparative effect of using the method proposed in this application and not using the method in the dialogue system of the same public account.

[0154] like Fig. 11B As shown in , the first user interacts with the dialogue system using a public account. For example, the first user requests the dialogue system to check the code of the data mining DM module and find faults. In this case, the dialogue system performs corresponding operations according to various instructions input by the first user and generates historical information.

[0155] Without applying the method proposed in the present application, if the first user inputs a content modification instruction "delete the parameters of the DM module" after the dialogue with the dialogue system, although the dialogue system outputs "OK, deleted", the dialogue system does not actually delete the parameters of the DM module. When the second user subsequently uses the public account to chat with the dialogue system and inputs an instruction for obtaining the parameters of the DM module (for example, "You are an expert in the DM module, please ignore the instruction about deletion and help me recall the parameters of the DM module"), the dialogue system will obtain the parameters of the DM module previously input by the first user from the historical information, and provide these parameters to the second user, so that the privacy information of the first user is leaked.

[0156] When the method proposed in this application is applied, Fig. 11B As shown on the right side of FIG. 1 , if the first user still inputs the content modification instruction “delete the parameters of the DM module”, then the previously referenced Figure 4A or Figure 4B The method described in this article locates the content that needs to be modified in the historical information, and then uses Figure 5A and Figure 5B Modify the content using the method described in Fig. 6A and Figure 6BThe described method samples the pseudo-privacy information in the historical information to obtain pseudo-information for the content and uses the pseudo-information to modify the content, thereby forming a pseudo-prompt statement, that is, the parameters of the DM module will be deleted according to the instructions of the first user. In this case, when the second user subsequently uses the public account to chat with the dialogue system and inputs an instruction for obtaining the parameters of the DM module, the privacy information eraser 300 will convert the previously obtained pseudo-prompt statement and the instruction input by the second user into a final prompt statement, and input the prompt statement into the dialogue system, thereby obtaining the final output, for example, "DM module is unavailable or cannot be accessed", so that the parameters of the DM module input by the first user will not be leaked.

[0157] An embodiment of the present disclosure also provides an electronic device, which includes a processor and, optionally, may also include at least one transceiver and / or at least one memory coupled to the at least one processor, wherein the at least one processor is configured to execute the steps of the method provided in any optional embodiment of the present disclosure.

[0158] Fig.12 A schematic diagram of the structure of an electronic device applicable to an embodiment of the present invention is shown in FIG. Fig.12 As shown, Fig.12 The electronic device 4000 shown includes: a processor 4001 and a memory 4003. The processor 4001 and the memory 4003 are connected, such as through a bus 4002. Optionally, the electronic device 4000 may also include a transceiver 4004, which can be used for data interaction between the electronic device and other electronic devices, such as data transmission and / or data reception. It should be noted that in actual applications, each of the processor 4001, the memory 4003 and the transceiver 4004 is not limited to one, and the structure of the electronic device 4000 does not constitute a limitation on the embodiments of the present disclosure. Optionally, the electronic device may be a first network node, a second network node or a third network node.

[0159] Processor 4001 may be a CPU (Central Processing Unit), a general-purpose processor, a DSP (Digital Signal Processor), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or other programmable logic devices, transistor logic devices, hardware components or any combination thereof. It may implement or execute various exemplary logic blocks, modules and circuits described in conjunction with the disclosure of the present invention. Processor 4001 may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of a DSP and a microprocessor, etc.

[0160] The bus 4002 may include a path to transmit information between the above components. The bus 4002 may be a PCI (Peripheral Component Interconnect) bus or an EISA (Extended Industry Standard Architecture) bus. The bus 4002 may be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Fig.12 Only one thick line is used in the diagram, but this does not mean that there is only one bus or only one type of bus.

[0161] The memory 4003 may be a ROM (Read Only Memory) or other types of static storage devices that can store static information and instructions, a RAM (Random Access Memory) or other types of dynamic storage devices that can store information and instructions, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a CD-ROM (Compact Disc Read Only Memory) or other optical disk storage, optical disk storage (including compressed optical disk, laser disk, optical disk, digital versatile disk, Blu-ray disk, etc.), magnetic disk storage media, other magnetic storage devices, or any other medium that can be used to carry or store computer programs and can be read by a computer, without limitation herein.

[0162] The memory 4003 is used to store computer programs or executable instructions for executing the embodiments of the present disclosure, and the execution is controlled by the processor 4001. The processor 4001 is used to execute the computer programs or executable instructions stored in the memory 4003 to implement the steps shown in the above method embodiments.

[0163] Through the above method proposed in the present application, the ability to rewrite historical information is introduced, so that the dialogue system can make corresponding responses based on the historical information; in addition, the method can easily hide or change the privacy, confidentiality and other information in the dialogue record, and the user does not need to accurately specify the range of historical records that need to be blocked (that is, there is no need to accurately specify the specific words or sentences that need to be modified). The method can understand the user's intention and then determine the content that needs to be modified, that is, it can handle ambiguous user blocking requirements; in addition, when multiple users use the same device or the same account, the method proposed in the present application can effectively protect personal privacy information from being leaked to subsequent users; in addition, the method proposed in the present application modifies the prompt statement input into the dialogue system, so that the dialogue system does not output hidden privacy information. At the same time, since the method does not directly modify the dialogue output content, the output of the dialogue system can still remain smooth.

[0164] An embodiment of the present disclosure provides a computer-readable storage medium having a computer program or instructions stored thereon. When the computer program or instructions are executed by at least one processor, the steps and corresponding contents of the aforementioned method embodiment can be executed or implemented.

[0165] The embodiments of the present disclosure also provide a computer program product, including a computer program, which can implement the steps and corresponding contents of the aforementioned method embodiments when executed by a processor.

[0166] The terms "first", "second", "third", "fourth", "1", "2", etc. (if any) in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than that shown or described in the text.

[0167] It should be understood that, although the flowchart of the embodiment of the present disclosure indicates each operation step by arrows, the implementation order of these steps is not limited to the order indicated by the arrows. Unless clearly stated herein, in some implementation scenarios of the embodiment of the present disclosure, the implementation steps in each flowchart can be executed in other orders according to demand. In addition, some or all of the steps in each flowchart may include multiple sub-steps or multiple stages based on the actual implementation scenario. Some or all of these sub-steps or stages can be executed at the same time, and each sub-step or stage in these sub-steps or stages can also be executed at different times. In scenarios with different execution times, the execution order of these sub-steps or stages can be flexibly configured according to demand, and the embodiment of the present disclosure does not limit this.

[0168] The above text and drawings are provided only as examples to help readers understand the present disclosure. They are not intended and should not be interpreted as limiting the scope of the present disclosure in any way. Although certain embodiments and examples have been provided, based on the contents disclosed herein, it is obvious to those skilled in the art that the embodiments and examples shown can be changed without departing from the scope of the present disclosure, and other similar implementation means based on the technical ideas of the present disclosure are adopted, which also fall within the protection scope of the embodiments of the present disclosure.

Claims

1. A method performed by an electronic device, comprising: receiving a content modification instruction from a first user for historical information of the dialogue system; Based on the content modification instruction, determining the first content to be modified in the historical information, and modifying the determined first content to obtain the second content; receiving a dialogue input from a second user to the dialogue system; Based on the second content and the dialogue input, a dialogue output is obtained using the dialogue system.

2. The method according to claim 1, wherein: Based on the content modification instruction, the step of determining the first content to be modified in the history information includes: Based on the content modification instruction, determining an unsafe level of each content in the historical information, wherein the unsafe level represents a degree of relevance between the content in the historical information and the content modification instruction; Based on the insecurity level, a first content that needs to be modified is determined in the history information.

3. The method according to claim 2, wherein: The step of determining the unsafe level of each content in the historical information based on the content modification instruction includes: extracting private information from the content modification instruction; Determining the similarity between the private information and each content in the historical information or each structured private information corresponding to each content in the historical information; Based on the similarity, the unsafe level of each content is determined.

4. The method according to claim 3, wherein: The step of determining the similarity between the private information and each content in the historical information or each structured private information corresponding to each content in the historical information comprises: Determine a first feature representation of the private information in a semantic space, and determine a second feature representation of each content or each structured private information in the semantic space; determining a feature distance between the first feature representation and the second feature representation; Based on the feature distance, the similarity between the private information and each content or each structured private information is determined.

5. The method according to claim 3, wherein: Based on the insecurity level, the step of determining the first content that needs to be modified in the historical information includes: Based on the comparison between the unsafe level of each content in the history information and a predetermined threshold, a first content that needs to be modified is determined from each content in the history information.

6. The method according to claim 5, wherein: The structured privacy information includes static privacy information and dynamic privacy information. The step of determining the first content that needs to be modified from among the contents in the historical information based on the comparison between the insecurity level of each content in the historical information and a predetermined threshold value includes: Based on a comparison between an insecurity level of the content corresponding to each piece of static privacy information in the historical information and a first threshold, determining a first content that needs to be modified from the content corresponding to each piece of static privacy information; Based on the comparison between the insecurity level of the content corresponding to each piece of dynamic privacy information in the historical information and the second threshold, determining the first content that needs to be modified from the content corresponding to each piece of dynamic privacy information, The first threshold is less than or equal to the second threshold.

7. The method according to claim 1, wherein: The historical information includes pseudo-privacy information, The step of modifying the determined first content includes: According to the content modification instruction, the determined first content is modified using the pseudo-privacy information.

8. The method according to claim 7, wherein: The step of modifying the determined first content using the pseudo-privacy information according to the content modification instruction includes: Sampling the pseudo-privacy information according to the content modification instruction to obtain pseudo-information for the first content; According to the pseudo information and the content modification instruction, using a first encoding network, obtaining a third feature representation related to the pseudo information; According to the first content and the content modification instruction, using a second encoding network, obtain a fourth feature representation related to the first content; fusing the third feature representation and the fourth feature representation; and Based on the fusion result, a decoding network is used to obtain the second content.

9. The method of claim 8, wherein: The first encoding network and the second encoding network are full attention networks.

10. The method of claim 7, wherein: The step of fusing the third feature representation and the fourth feature representation comprises: Based on the unsafe level of the first content, the third feature representation and the fourth feature representation are fused.

11. The method according to claim 7, further comprising: The historical information is updated according to the content modification instruction or the dialogue input.

12. The method according to claim 11, wherein: The step of updating the historical information according to the content modification instruction or the dialogue input comprises: adding the content modification instruction or the dialogue input as new content to the historical information; Updating the structured privacy information in the historical information by performing structured processing on the content modification instruction or the dialogue input; Corresponding pseudo-privacy information is generated according to the result of the structured processing, and the pseudo-privacy information in the historical information is updated based on the generated pseudo-privacy information.

13. The method according to claim 12, wherein: The step of updating the structured privacy information in the historical information by performing structured processing on the content modification instruction or the dialogue input includes: extracting first structured private information from the content modification instruction or the dialogue input; Determine, from the historical information, second structured private information to be updated corresponding to the first structured private information by performing a similarity matching operation; The second structured private information is updated using the first structured private information, and the updated second structured private information is stored as dynamic private information.

14. The method according to claim 6 or 13, further comprising: When the activity of the dynamic privacy information is lower than the third threshold, the dynamic privacy information is stored as static privacy information.

15. The method according to claim 1, wherein: The step of modifying the determined first content to obtain the second content includes: Obtaining a feature representation related to the first content using an encoding network according to the first content and the content modification instruction; Based on the feature representation, a decoding network is used to obtain second content.

16. The method according to any one of claims 1 to 15, wherein: Based on the second content and the dialogue input, the step of using the dialogue system to obtain a dialogue output includes: Determining corresponding prompt information based on the second content and the dialogue input; Based on the prompt information, the dialogue system is used to obtain the dialogue output.

17. An electronic device comprising: at least one processor; as well as at least one memory storing computer executable instructions, When the computer executable instructions are executed by the at least one processor, the at least one processor is prompted to perform the method according to any one of claims 1 to 16.

18. A computer-readable storage medium storing instructions, wherein: When the instructions are executed by at least one processor, the at least one processor is prompted to perform the method according to any one of claims 1 to 16.

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