Data processing device

Abstract

To provide a data processing device capable of providing information taking a user's action schedule into consideration.SOLUTION: A data processing device comprises: an input part which acquires user data; a processing part which performs identification processing using a generation model generating a predetermined inference result corresponding to the user data; and an output part which outputs a result of the identification processing to a predetermined device. Therein: the input part acquires a user's schedule, a history of predetermined service that the user utilizes, and position information as the user data from a device of the user; and the processing part identifies information related to an action schedule from the user data, and by using an output of the generation model when a text representing an instruction for obtaining a suggestion is input together with the information related to the action schedule, acquires a result of the identification processing for the user.SELECTED DRAWING: Figure 4

Description

[Technical field]

[0001] The technology of this disclosure relates to a data processing device. [Background technology]

[0002] Patent document 1 discloses a persona chatbot control method performed by at least one processor, the method including the steps of receiving a user utterance, adding the user utterance to a prompt including a description and related instruction sentence regarding the chatbot character, encoding the prompt, and inputting the encoded prompt into a language model to generate a chatbot utterance in response to the user utterance. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2022-180282 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the conventional technology has room for improvement in terms of providing information on a user's planned activities using data acquired from smart devices. [Means for solving the problem]

[0005] A data processing device of a first aspect of the technology disclosed herein comprises an input unit for acquiring user data, a processing unit for performing specific processing using a generative model that generates a predetermined inference result according to the user data, and an output unit for outputting the result of the specific processing to a predetermined device, wherein the input unit acquires the user's schedule, a history of predetermined services used by the user, and location information as the user data from the user's device, and the processing unit identifies information regarding a planned activity from the user data, and acquires the result of the specific processing for the user using the output of the generative model when text representing instructions for obtaining suggestions is input together with the information regarding the planned activity. [Brief description of the drawings]

[0006] [Figure 1] FIG. 1 is a conceptual diagram illustrating an example of a configuration of a data processing system. [Diagram 2] FIG. 2 is a conceptual diagram showing an example of main functions of a data processing device and a smart device. [Diagram 3] 1 shows an overview of a process for specifying information provision for a user's planned activities. [Figure 4] 2 shows a schematic functional configuration of a specific processing unit of the data processing device. [Diagram 5] 13 illustrates an example of an operational flow of a specific process performed by a data processing device. [Figure 6A] 1 shows an emotion map onto which multiple emotions are mapped. [Figure 6B] 1 shows an emotion map onto which multiple emotions are mapped. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0007] Hereinafter, an example of an embodiment of a data processing device, a data processing method, and a program according to the technology of the present disclosure will be described with reference to the accompanying drawings.

[0008] First, the terms used in the following description will be explained.

[0009] In the following embodiments, a signed processor (hereinafter simply referred to as a "processor") may be one arithmetic device or a combination of multiple arithmetic devices. Furthermore, a processor may be one type of arithmetic device or a combination of multiple types of arithmetic devices. Examples of arithmetic devices include a CPU (Central Processing Unit), a GPU (Graphics Processing Unit), a GPGPU (General-Purpose computing on Graphics Processing Units), an APU (Accelerated Processing Unit), or a TPU (Tensor Processing Unit).

[0010] In the following embodiments, a signed RAM (Random Access Memory) is a memory in which information is temporarily stored and is used as a working memory by the processor.

[0011] In the following embodiments, the coded storage is one or more non-volatile storage devices that store various programs, various parameters, etc. Examples of non-volatile storage devices include flash memory (SSD (Solid State Drive)), magnetic disks (e.g., hard disks), and magnetic tapes.

[0012] In the following embodiments, a communication I / F (Interface) with a code is an interface including a communication processor and an antenna. The communication I / F controls communication between multiple computers. An example of a communication standard applied to the communication I / F is a wireless communication standard including 5G (5th Generation Mobile Communication System), Wi-Fi (registered trademark), Bluetooth (registered trademark), etc.

[0013] In the following embodiments, "A and / or B" is synonymous with "at least one of A and B." In other words, "A and / or B" means that it may be only A, only B, or a combination of A and B. In addition, in this specification, the same idea as "A and / or B" is also applied when three or more things are expressed by connecting them with "and / or."

[0014] FIG. 1 shows an example of the configuration of a data processing system 10 according to the embodiment.

[0015] As shown in Fig. 1, the data processing system 10 includes a data processing device 12 and a smart device 14. An example of the data processing device 12 is a server. An example of the smart device 14 is a smartphone. In this embodiment, the data processing device 12 is an example of a "data processing device" according to the technology of the present disclosure.

[0016] The data processing device 12 includes a computer 22, a database 24, and a communication I / F 26. The computer 22 is an example of a "computer" according to the technology of the present disclosure. The computer 22 includes a processor 28, a RAM 30, and a storage 32. The processor 28, the RAM 30, and the storage 32 are connected to a bus 34. The database 24 and the communication I / F 26 are also connected to the bus 34. The communication I / F 26 is connected to a network 54. Examples of the network 54 include a wide area network (WAN) and / or a local area network (LAN).

[0017] The smart device 14 includes a computer 36, a reception device 38, an output device 40, a camera 42, and a communication I / F 44. The computer 36 includes a processor 46, a RAM 48, and a storage 50. The processor 46, the RAM 48, and the storage 50 are connected to a bus 52. The reception device 38, the output device 40, and the camera 42 are also connected to the bus 52.

[0018] The reception device 38 includes a touch panel 38A and a microphone 38B, and receives user input. The touch panel 38A detects contact with a pointer (e.g., a pen or a finger) to receive user input by the touch of the pointer. The microphone 38B detects the user's voice to receive user input by voice. The control unit 46A transmits data indicating the user input received by the touch panel 38A and the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the data indicating the user input.

[0019] The output device 40 includes a display 40A and a speaker 40B, and presents data to the person 20 by outputting the data in a form of expression that the person 20 can perceive (e.g., voice and / or text). The display 40A displays visible information such as text and images according to instructions from the processor 46. The speaker 40B outputs voice according to instructions from the processor 46. The camera 42 is a compact digital camera equipped with an optical system including a lens, an aperture, and a shutter, and an imaging element such as a Complementary Metal-Oxide-Semiconductor (CMOS) image sensor or a Charge Coupled Device (CCD) image sensor.

[0020] The communication I / F 44 is connected to a network 54. The communication I / Fs 44 and 26 are responsible for transmitting and receiving various types of information between the processor 46 and the processor 28 via the network 54.

[0021] FIG. 2 shows an example of main functions of the data processing device 12 and the smart device 14.

[0022] As shown in Fig. 2, in the data processing device 12, a specific process is performed by the processor 28. A specific process program 56 is stored in the storage 32. The specific process program 56 is an example of a "program" according to the technology of the present disclosure. The processor 28 reads the specific process program 56 from the storage 32, and executes the read specific process program 56 on the RAM 30. The specific process is realized by the processor 28 operating as a specific processing unit 290 in accordance with the specific process program 56 executed on the RAM 30.

[0023] The storage 32 stores a data generation model 58 and an emotion identification model 60. The data generation model 58 and the emotion identification model 60 are used by the identification processing unit 290.

[0024] In the smart device 14, the processor 46 performs the reception output process. The storage 50 stores a reception output program 62. The reception output program 62 is used together with the specific processing program 56 by the data processing system 10. The processor 46 reads out the reception output program 62 from the storage 50, and executes the read reception output program 62 on the RAM 48. The reception output process is realized by the processor 46 operating as the control unit 46A in accordance with the reception output program 62 executed on the RAM 48.

[0025] Next, the processing of the specific processing unit 290 when the data processing device 12 performs specific processing to provide suggestions for the user's planned activities will be described. In the specific processing of this embodiment, user data is collected from the smart device 14, information desired by the user is inferred, and the information is provided as suggestions. The suggestions include information on advertisements, services, coupons, etc. The specific processing of this embodiment realizes the provision of information to the user by so-called hyper-personalization.

[0026] In the identification process in this embodiment, as shown in FIG. 3, the user's actions from the past to the present are collected, and information on the user's planned actions is analyzed. From the smart device 14, the schedule, the history of interactions on SNS (social networking service) and other services, and the location (location information) of the current location are collected. In the analysis, for example, from the schedule information on the calendar, a planned action such as "lunch with a friend on XX date at XX time" can be identified by analysis. From the history of interactions on SNS, the preference pattern of the user himself and other users related to the user, such as "my friend likes coffee," can be identified by analysis. From the location information of the current location, a spot such as "around XX station" can be identified by analysis. The collection and analysis are performed in real time, and suggestions are generated and notified. The notified suggestions are displayed on the display 40A of the smart device 14, and details can be displayed by tapping on the touch panel 38A. In the example of Fig. 3, a suggestion such as "[Suggestion] Lunch with A is 15 minutes away. We will suggest some shops around the current location that serve delicious coffee that A likes.

[01] XXX Shop / △△ Station, 5 minutes walk...." is generated and notified to the smart device 14. The information identified about the user in this way is input to the data generation model, and suggestions that can be approached in real time based on the user's planned activities are generated.

[0027] As shown in FIG. 4, the specific processing unit 290 includes an input unit 292, a processing unit 294, and an output unit 296.

[0028] The input unit 292 acquires user data collected from the smart device 14. Specifically, the input unit 292 acquires user data such as the user's schedule, the user's SNS history, and location information based on the current location and movement history, etc., collected in real time from the smart device 14. The user data may be acquired according to a predetermined cycle or the user's use frequency of the smart device 14. The user's behavior pattern may be detected and learned to determine the frequency of collection of user data. For example, if the user uses the SNS of the smart device 14 frequently during the daytime from 12:00 to 13:00, the user data may be collected around that time period. In addition, when data that needs to be collected as user data is updated, the smart device 14 may notify the data processing device 12, and the data may be acquired when the notification is received.

[0029] Although the SNS history is an example of the history of a specific service used by the user in the present disclosure, the history of the service used by the user is not limited to SNS, and various services and applications can be used. For example, preference patterns can be collected and analyzed from the history of various applications that the user has arbitrarily installed on the smart device 14, such as a weight management application, a mail-order application, a video viewing application, an information search application, and a learning management application. In addition, the usage history of sites used by browsing can also be collected and analyzed.

[0030] Next, the processing unit 294 analyzes the user data as a pre-processing, and identifies information related to the action plan. The information related to the action plan is the action plan such as a schedule plan or a behavior pattern, the user's preference pattern related to the action plan, and the spot of the current location. The pre-processing for identifying information related to the action plan may be executed each time the user data is acquired and stored in advance. The action plan is not limited to a calendar plan, but also includes a behavior pattern. The behavior pattern may be identified by analyzing the usage history and frequency of use of a service that is used periodically. For example, a behavior pattern such as "the user browses diet products five or more times a week" can be identified. This allows the target of interest related to the action plan to be identified. As described above, the action plan is identified from the schedule plan and behavior pattern of the user in the user data. The preference pattern is identified from the history or behavior pattern of the SNS. The spot of the current location is identified from the spots included in the location information and map information.

[0031] The processing unit 294 performs a specific process using the data generation model 58. Specifically, the processing unit 294 determines whether a predetermined trigger condition is satisfied. More specifically, the trigger condition is acquisition of user data. Also, the trigger condition may be a case where specific information of the user data is updated. For example, the trigger condition may be set to a case where schedule information is updated, a case where there is an exchange with a specific other user on SNS, a case where the user's current location in location information approaches the vicinity of a predetermined spot, etc. Also, the trigger condition may be set to a case where the schedule is scheduled within a predetermined time. Also, the user may be allowed to set the notification frequency of the suggestions, and the set notification frequency may be set as the trigger condition.

[0032] A text representing an instruction for obtaining a suggestion by a specific process is generated as a prompt together with information about the planned action, and inputted to the data generation model 58, and a processing result is obtained based on the output of the data generation model 58. The following instruction sentences can be generated as the instruction sentence of the prompt. For example, instruction sentences such as "I have an appointment with a friend who likes coffee from 18:00. The user is at a certain spot. Please suggest the best spots in the vicinity", "The user has searched for a dress. The user is at a certain spot. Please suggest nearby spots where a dress can be purchased", "The user cooks at least three times a week. Please suggest services and recipes related to cooking ingredients", and "The user is on a diet at least three days a week. Please suggest nearby spots where diet products can be purchased" are generated. The processing unit 294 inputs the generated prompt to the data generation model 58 and obtains a suggestion for the user's planned action.

[0033] The processing unit 294 may estimate the user's emotion using the emotion identification model 60 and perform identification processing using the user's emotion and the data generation model 58.

[0034] The output unit 296 transmits the result of the specific processing to the smart device 14. In the smart device 14, the control unit 46A causes the output device 40 to output the result of the specific processing. The microphone 38B acquires a voice indicating a user input for the result of the specific processing. The control unit 46A transmits the voice data indicating the user input acquired by the microphone 38B to the data processing device 12. In the data processing device 12, the specific processing unit 290 acquires the voice data.

[0035] The data generation model 58 is a so-called generative AI (Artificial Intelligence). An example of the data generation model 58 is ChatGPT (Internet search<URL: https: / / openai.com / blog / chatgpt> ) and other generation AIs. The data generation model 58 is obtained by making a neural network perform deep learning. A prompt including an instruction is input to the data generation model 58, and inference data such as voice data indicating a voice, text data indicating a text, and image data indicating an image is input. The data generation model 58 performs inference on the input inference data according to the instruction indicated by the prompt, and outputs the inference result in a data format such as voice data and text data. Here, inference refers to, for example, analysis, classification, prediction, and / or summarization.

[0036] Next, an example of the flow of specific processing will be described with reference to Fig. 5 regarding the operation of the data processing system 10. Note that the flow of specific processing shown in Fig. 5 is an example of a data processing method according to the technique of the present disclosure.

[0037] 5 is a schematic diagram showing an example of an operation flow of the identification processing unit 290 performing identification processing for a user to be analyzed. The operation flow shown in FIG. 5 is automatically and repeatedly executed, for example, every time a certain time elapses or every time a notification is received from the smart device 14.

[0038] In step S300, the input unit 292 acquires user data from the smart device 14.

[0039] In step S302, the processing unit 294 analyzes the user data as preprocessing, and identifies information related to the action schedule.

[0040] In step S304, the processing unit 294 determines whether or not a predetermined trigger condition is satisfied. If the trigger condition is satisfied, the process proceeds to step S306. On the other hand, if the trigger condition is not satisfied, the identification process ends.

[0041] In step S306, the processing unit 294 generates text representing an instruction for obtaining a suggestion by a specific process as a prompt.

[0042] In step S308, the processing unit 294 inputs the generated prompt to the data generation model 58, and based on the output of the data generation model 58, obtains a suggestion for the user's action schedule as a result of the specific process.

[0043] In step S310, the output unit 296 outputs the suggestions obtained as a result of the identification process to the smart device 14. The suggestions are provided in a notification format as shown in FIG.

[0044] As described above, the data processing device 12 makes it possible to provide information taking into account the user's planned activities.

[0045] The emotion identification model 60 may determine the emotion of the user according to a specific mapping. Specifically, the emotion identification model 60 may determine the emotion of the user according to an emotion map (see FIG. 7A), which is a specific mapping.

[0046] FIG. 7A is a diagram showing an emotion map 400 on which multiple emotions are mapped. In the emotion map 400, emotions are arranged in concentric circles radiating from the center. The closer to the center of the concentric circles, the more primitive emotions are arranged. The more outside the concentric circles, the more emotions that represent states and actions that arise from a state of mind are arranged. Emotions are a concept that includes emotions and mental states. On the left side of the concentric circles, emotions that are generally generated from reactions that occur in the brain are arranged. On the right side of the concentric circles, emotions that are generally induced by situational judgment are arranged. On the upper and lower sides of the concentric circles, emotions that are generally generated from reactions that occur in the brain and are induced by situational judgment are arranged. In addition, on the upper side of the concentric circles, emotions of "pleasure" are arranged, and on the lower side, emotions of "discomfort" are arranged. In this way, in the emotion map 400, multiple emotions are mapped based on the structure in which emotions are generated, and emotions that tend to occur simultaneously are mapped close to each other.

[0047] These emotions are distributed in the 3 o'clock direction of emotion map 400 and usually fluctuate between relief and anxiety. In the right half of emotion map 400, situational awareness dominates over internal sensations, resulting in a sense of calm.

[0048] The inside of emotion map 400 represents what is going on inside one's mind, and the outside of emotion map 400 represents behavior, so the further out you go on emotion map 400, the more visible the emotions become (the more they are expressed in behavior).

[0049] Here, human emotions are based on various balances such as posture and blood sugar level, and when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. Emotions can also be created for robots, cars, motorcycles, etc., based on various balances such as posture and battery level, so that when these balances are far from the ideal, it indicates an unpleasant state, and when they are close to the ideal, it indicates a pleasant state. The emotion map may be generated, for example, based on the emotion map of Dr. Mitsuyoshi (Research on speech emotion recognition and emotion brain physiological signal analysis system, Tokushima University, doctoral dissertation: https: / / ci.nii.ac.jp / naid / 500000375379). On the left half of the emotion map, emotions belonging to an area called "reaction" where sensation is dominant are lined up. On the right half of the emotion map, emotions belonging to an area called "situation" where situation recognition is dominant are lined up.

[0050] The emotion map defines two emotions that promote learning. The first is the negative emotion around the middle of "repentance" or "remorse" on the situation side. In other words, this is when the robot experiences negative emotions such as "I never want to feel this way again" or "I don't want to be scolded again." The other is the positive emotion around "desire" on the response side. In other words, this is when the robot has positive feelings such as "I want more" or "I want to know more."

[0051] The emotion identification model 60 inputs user input to a pre-trained neural network, obtains emotion values ​​indicating each emotion shown in the emotion map 400, and determines the emotion of the user. This neural network is pre-trained based on multiple learning data that are combinations of user input and emotion values ​​indicating each emotion shown in the emotion map 400. In addition, this neural network is trained so that emotions that are located close to each other have similar values, as in the emotion map 900 shown in FIG. 7B. FIG. 7B shows an example in which multiple emotions, "relief," "calm," and "encouraging," have similar emotion values.

[0052] In the above embodiment, an example was given in which a specific process is performed by one computer 22, but the technology disclosed herein is not limited to this, and distributed processing of the specific process may be performed by multiple computers, including computer 22.

[0053] In the above embodiment, an example in which the specific processing program 56 is stored in the storage 32 has been described, but the technology of the present disclosure is not limited to this. For example, the specific processing program 56 may be stored in a portable computer-readable non-transitory storage medium such as a Universal Serial Bus (USB) memory. The specific processing program 56 stored in the non-transitory storage medium is installed in the computer 22 of the data processing device 12. The processor 28 executes the specific processing in accordance with the specific processing program 56.

[0054] In addition, the specific processing program 56 may be stored in a storage device such as a server connected to the data processing device 12 via the network 54, and the specific processing program 56 may be downloaded and installed on the computer 22 upon request from the data processing device 12.

[0055] It is not necessary to store all of the specific processing program 56 in a storage device such as a server connected to the data processing device 12 via the network 54, or to store all of the specific processing program 56 in the storage 32; only a portion of the specific processing program 56 may be stored.

[0056] As the hardware resource for executing the specific process, various processors as shown below can be used. An example of the processor is a CPU, which is a general-purpose processor that functions as a hardware resource for executing the specific process by executing software, i.e., a program. Another example of the processor is a dedicated electric circuit, which is a processor having a circuit configuration designed exclusively for executing the specific process, such as a Field-Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), or an Application Specific Integrated Circuit (ASIC). Each processor has a built-in or connected memory, and each processor executes the specific process by using the memory.

[0057] The hardware resource that executes the specific process may be one of these various processors, or may be a combination of two or more processors of the same or different types (for example, a combination of multiple FPGAs, or a combination of a CPU and an FPGA). Also, the hardware resource that executes the specific process may be a single processor.

[0058] As an example of a configuration using one processor, first, one processor is configured by combining one or more CPUs and software, and this processor functions as a hardware resource that executes a specific process. Second, there is a configuration using a processor that realizes the functions of the entire system including multiple hardware resources that execute a specific process on a single IC chip, as typified by SoC (System-on-a-chip). In this way, a specific process is realized using one or more of the above-mentioned various processors as hardware resources.

[0059] Furthermore, more specifically, the hardware structure of these various processors can be an electric circuit that combines circuit elements such as semiconductor elements. The specific processes described above are merely examples. It goes without saying that unnecessary steps may be deleted, new steps may be added, or the order of processes may be changed without departing from the spirit of the invention.

[0060] The above description and illustrations are detailed descriptions of the parts related to the technology of the present disclosure, and are merely an example of the technology of the present disclosure. For example, the above description of the configuration, function, action, and effect is an example of the configuration, function, action, and effect of the parts related to the technology of the present disclosure. Therefore, it goes without saying that unnecessary parts may be deleted, new elements may be added, or replacements may be made to the above description and illustrations, within the scope of the gist of the technology of the present disclosure. In addition, in order to avoid confusion and to facilitate understanding of the parts related to the technology of the present disclosure, the above description and illustrations omit explanations of technical common sense that do not require explanation in order to enable the implementation of the technology of the present disclosure.

[0061] All publications, patent applications, and standards mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent application, and standard was specifically and individually indicated to be incorporated by reference. [Explanation of symbols]

[0062] 10 Data Processing System 12 Data Processing Device 14 Smart Devices

Claims

1. an input unit for acquiring user data; A processing unit that performs a specific process using a generative model that generates a predetermined inference result according to user data; an output unit that outputs a result of the specific processing to a predetermined device, The input unit acquires, as the user data, a schedule of the user, a history of a predetermined service used by the user, and location information from the user's device; The processing unit identifies information regarding an action plan from the user data, obtaining a result of the specific process for the user by using an output of the generative model when a text representing an instruction for obtaining a suggestion is input together with the information about the action schedule; Data processing device.

2. Identifying the action plan, the user's preference pattern related to the action plan, and the spot of the current location as information related to the action plan; The action plan is identified as a behavior pattern of the schedule and the service history in the user data; The preference pattern is identified from a history of the service in the user data; The data processing device according to claim 1 , wherein the spot of the current position is identified from the position information in the user data and spots included in map information.

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