Container system implementing a conversation assistant

Abstract

The invention relates to a system for transporting objects allowing a conversation exchange. According to the invention, such a system comprises at least one container (1, 10) configured to capture a first item of data from a user; and a smart conversation agent (18) configured to: receive the first item of data, transmitted by the container; perform a semantic analysis of the first item of data; optionally produce a second item of data resulting from the semantic analysis; optionally transmit the second item of data to the at least one container; the container (1, 10) also being configured to process the second item of data received.

Description

[0001] Container system implementing a conversational assistant

[0002] 1. Field of the invention

[0003] The invention relates to the field of conversational artificial intelligence linked to connected objects, also known under the terminology of the Internet of Things and the acronym loT (from the English “Internet of Things”).

[0004] More specifically, the invention relates to a smart container, intended for the transport of products, parts or objects.

[0005] The invention finds particular application in packaging or delivery packages, used in the field of logistics transport for the delivery of products.

[0006] 2. State of the art

[0007] In the field of logistics, it is known to label a package to identify its destination, sort it and direct it on the logistics platforms, until its recipient. Such a technique suffers from the need to multiply the number of labels according to the number of logistics providers involved in the transport. In addition, this elementary technique has many disadvantages related to the absence of the peeled label, legibility problems, and the impossibility of tracking the package between two identification points in logistics platforms. As a result, many packages are lost and some stolen.

[0008] It then became necessary to use packaging equipped with an on-board electronic unit with a screen acting as a label and capable of transmitting position information with a monitoring platform, on the logistics route followed. Finally, to reinforce the security of these packages, means of locking the packages were added, authorizing opening under specific conditions, in order to limit access to the authorized person, very often the final recipient.

[0009] However, these developments in packaging for shipping and transporting an object are not without drawbacks.

[0010] Indeed, as soon as a new type of labeling comes into operation, it is necessary to review and reformulate the processes, consider the nominal operation and the situations of processing errors in the supply chain.

[0011] Another disadvantage of such a technique is that it requires training all professionals in the supply chain in this new, often specific, use.

[0012] Another disadvantage of this technique is that when the recipient receives his package, although he has been able to track its progress, he has no information relating to its transport.

[0013] 3. Objectives of the invention

[0014] The invention therefore aims in particular to overcome the drawbacks of the state of the art cited above.

[0015] More specifically, the invention aims to provide an object transport system allowing a conversational exchange between a user and the transport packaging.

[0016] An objective of the invention is also to provide such a technique that is simple to implement by the user. Another objective of the invention is to provide such a technique that allows relevant exchanges with the user.

[0017] An objective of the invention is also to propose such a technique which allows a conversational exchange centered around the sender's profile.

[0018] Another objective of the invention is to provide such a technique which is reliable and safe for the consumer.

[0019] 4. Statement of the invention

[0020] These objectives, as well as others that will appear later, are achieved using an object transport system that allows for conversational exchange.

[0021] In the context of the invention, the system comprises at least one container configured to capture a first piece of data. The system further comprises a conversational intelligent agent configured to: a) receive the first piece of data, transmitted by the container; b) perform a semantic analysis of said first piece of data; c) produce, optionally, a second piece of data resulting from the semantic analysis; d) transmit, optionally, said second piece of data to said at least one container;

[0022] Furthermore, in this system, the container is configured to process the second received data.

[0023] Thus, in such a system, thanks to these technical characteristics it is possible to initiate a conversation in which the container sends data to an intelligent agent which, after having semantically analyzed them, can respond with the result of its analysis. According to a preferred embodiment of the invention, the first data and the second data are so-called communication data which form a conversational exchange, and in which the first data comes from a user of the container; the processing of the second data by the container consists of communicating the second data to said user, the conversational intelligent agent is further configured so as to:

[0024] - retain contextual information from the conversational exchange,

[0025] - use contextual information during said semantic analysis.

[0026] Thanks to these features, a user can query the container in natural language and receive help and / or clarifications in natural language. In addition, the user can converse with the container through a series of chained questions and answers.

[0027] In addition, the intelligent conversational agent has the history of questions / answers exchanged with the user and can therefore refine its analysis in order to increase the accuracy of its responses.

[0028] According to an advantageous embodiment of the invention, the first data is so-called information data, of the type, an identifier, or a physical quantity, or an operating configuration.

[0029] Thanks to these characteristics, the intelligent conversational agent receives information about the context of the container. This information can be simple, coupled or composed to provide a variable level of precision on the information collected. The first data, called information, can for example include the internal temperature alone and / or external, the internal and / or external atmospheric pressure. This can also be information related to its movement, such as acceleration, or a position by satellite location. Thus, from the movement information, the intelligent conversational agent can deduce the subjection of the container to a shock, vibrations, or a fall. In the case of a fall, the height of the fall and / or the acceleration can complete the information.

[0030] Advantageously, the identifiers are identifiers specific to the application, or identifiers of a known type, such as that of a SIM ® card (English acronym for “Subscriber Identify Module”), or even an NFC ® code (English acronym for “Near-Field Communication”).

[0031] According to a particularly advantageous embodiment of the invention, the second data item, resulting from the semantic analysis of the first data item, called information, is a command applied by said container. Thanks to these characteristics, optionally depending on the result of the semantic analysis, a second data item is produced, then sent to the container which can then use it. Thus, the intelligent conversational agent can choose between sending a text or voice message and controlling different actions, in particular controlling an LED (acronym for Light Emitting Diode also known by the acronym “LED”), a siren or a loudspeaker, a motor, a display screen, a locking / unlocking device. Thus, the intelligent conversational agent can, for example, control a flashing mode of the LEDs to attract the user’s attention.

[0032] According to a preferred embodiment of the invention, the intelligent conversational agent is configured so as to:

[0033] - record the first so-called information data in a continuous learning parameter base; and

[0034] - train a model of the conversational intelligent agent with the continuous learning parameters stored in said database. Thanks to these characteristics, the conversational intelligent agent increases its knowledge base, so as to enrich its responses and / or improve their relevance.

[0035] According to a particular aspect of the invention, the drive is automatic.

[0036] According to a particularly advantageous embodiment of the invention, the training is deep learning, in which each continuous learning parameter is associated with a positive or negative assessment weighting of said parameter.

[0037] Thus, thanks to the help of “machine learning” or “deep learning” type algorithms, the value of the learning data is qualified, improving the reliability of subsequent semantic analyses.

[0038] According to one embodiment, the container comprises a means for entering said first so-called communication data, which is a voice capture means and / or a text input means.

[0039] Thanks to these characteristics, the user can query the container directly in natural language orally and / or use an input means, such as a touch or mechanical keyboard, or a stylus associated with a handwriting recognition means, to communicate with the container. According to a particular embodiment of the invention, the input means is an independent, proximity remote control, such as a smartphone. The smartphone then communicates with a proximity wireless communication means such as a Bluetooth ®, BLE ® (English acronym for “Bluetooth Low Energy”) or Wi-Fi ® protocol.

[0040] Thanks to these characteristics, it is possible to produce containers at reduced prices and thus expand the target customer base of the product. According to an advantageous embodiment of the invention, the system comprises a means for text transcription of said voice capture, so that the intelligent conversational agent receives a first data item in text format.

[0041] Thanks to these characteristics, using speech, the user can communicate with the container whose captured voice message is converted into a text message received by the conversational intelligent agent, and directly interpretable by conversational artificial intelligence.

[0042] According to one embodiment, the container comprises a means of sound reproduction and / or a means of displaying said second so-called communication data.

[0043] Thanks to these characteristics, the user receives a response to the query that he / she submitted indirectly to the conversational intelligent agent through the container.

[0044] According to an advantageous embodiment of the invention, the system comprises a means of voice transcription of the second so-called communication data produced in text format by the intelligent conversational agent. Thanks to these characteristics, the user receives a voice response.

[0045] According to one embodiment, the container is selected from a group comprising luggage and a reusable box.

[0046] Thus, in a new and unexpected way, a reusable box, a reusable logistics delivery packaging, an electronic logistics label intended to equip a single-use packaging and also luggage, such as a suitcase, allow a user to converse to obtain different types of information, in particular information facilitating the use of the container. In a particularly advantageous embodiment of the invention, the conversational intelligent agent is integrated into a computer unit embedded in said container and / or on a central computer platform configured to communicate with said container.

[0047] Thanks to these characteristics, it is possible to have either an autonomous container with localized intelligence, or a container supported by intelligence located on a remote central computing platform. It is also possible to distribute, or even combine intelligence on both sides between the central computing platform and the container, to increase the relevance and / or speed of responses, or even distribute the processing load according to the sizing of the processing power embedded in the container.

[0048] In addition, intelligence located in each container also offers the advantage, particularly in the case of luggage, of improving confidentiality and / or respect for privacy.

[0049] Furthermore, such localized intelligence makes up for the lack of connectivity with the central computing platform when long-distance communications fail. This also helps reduce the power consumption of the long-distance communication module. This also helps manage and therefore minimize the volume of 4G data consumption.

[0050] According to a particular embodiment of the invention, the intelligent conversational agent is transferred to an externalized IT platform configured to communicate with said central IT platform.

[0051] Thanks to these characteristics, the intelligent conversational agent is transferred to an outsourced platform at a specialized supplier, for example. According to a particular embodiment of the invention, the transport system further comprises a means of communication with a global computer network. According to alternative embodiments, the means of communication connects the central computer platform and / or the outsourced computer platform to the global computer network.

[0052] Thanks to these characteristics, the intelligent conversational agent is able to supplement its learning by consulting the internet. In addition, it can enrich its responses with information collected on the global computer network, such as weather information related to a temperature peak recorded by the container in a specific location.

[0053] According to an advantageous embodiment of the invention

[0054] - the container is configured to identify the user, categorize the identified user into a conversation type profile and transmit the conversation type profile to the conversational intelligent agent;

[0055] - the intelligent conversational agent being configured to adapt the semantic analysis according to the typical conversation profile.

[0056] Thanks to these characteristics, the intelligent conversational agent is able to provide targeted information based on the typical conversation profile and therefore the role of the user, who may have the role of a customer receiving a package, that of a logistician responsible for its delivery, or even the role of an agent in charge of the maintenance of the reusable package. Thus, the identified maintenance agent can, using voice command keywords, obtain diagnostic information, or even information or recommendations from the after-sales service manual, which are not available for the other roles. According to one embodiment of the invention, the identification of the user is completed by authentication.

[0057] Thus, thanks to this technique, it is possible to ensure, for example, that only the recipient customer can open the container upon delivery.

[0058] According to a particularly advantageous embodiment of the invention, the intelligent conversational agent is configured to choose a conversation language based on a position of the container, received in the first so-called information data. The position, which is a physical location quantity, is typically the last GPS position of the container. But this can also be deduced from other information, such as the current cellular telephone cell, or the known position of the current storage warehouse and / or its Wi-Fi® network to which the container can be connected.

[0059] Thus, the intelligent conversational agent communicates in the local language when it is available, or switches to an international language, typically English, or even Spanish or French.

[0060] Advantageously, the intelligent conversational agent uses the location position to determine local cultural criteria and adapt the responses produced to these criteria.

[0061] Certain aspects of the invention are based on the idea of ​​implementing advances in artificial intelligence in the conversational field, in particular with large language models or LLM (English acronym for “Large Language Model”), of the type of “pre-trained generative transformers”, such as services like GPT3 ® and ChatGPT ® from OpenAI ®, Bert ® and Bard ® from Google ®, or even with generative artificial intelligence. 5. List of figures

[0062] Other characteristics and advantages of the invention will appear more clearly on reading the following description of 2 embodiments of the invention, given as simple illustrative and non-limiting examples, and the appended drawings among which:

[0063] [Figure 1] is a schematic representation of an exemplary embodiment of an object transport system featuring a communicating container according to the invention;

[0064] [Figure 2] is a schematic representation of a second exemplary embodiment of smart luggage according to the invention.

[0065] 6. Detailed description of the invention

[0066] 1. Example of an embodiment of the invention

[0067] Figure 1 illustrates a first embodiment of an object transport system according to the invention, comprising a container 1, an IT infrastructure 14 and an externalized IT platform 19.

[0068] The container 1 allowing a conversational exchange integrates electronic means including an on-board computer unit 2, data storage means 3, a power supply battery 4, a microphone 5, a loudspeaker 6, a digital screen 7, one or more control buttons 8, a first communication means 9, one or more actuators not shown, the assembly assembled according to techniques known per se.

[0069] It also integrates a multitude of sensors (not shown) to obtain various information related to its storage, transport, or environment. Thus, it can obtain a GPS (Global Positioning System) position allowing its movement to be traced, movement information and the receipt of a shock, its exposure to vibrations, or even recording a fall. The container 1 is also capable of recording internal and / or external information such as temperature and atmospheric pressure. This various information also includes an identifier specific to the container 1, an identifier specific to the computer unit 2, a SIM (Subscriber Identify Module) card identifier, a near-field communication code known by the acronym N FC (Near-Field Communication), or any other identifier known per se to those skilled in the art.

[0070] The IT infrastructure 14 includes a computer platform 15, a database 16 and a second communication means 17 for communicating via the first communication means 9, with the computer unit 2 of the container 1.

[0071] Furthermore, the IT infrastructure 14 is capable of communicating using a third means of communication 20 with the outsourced IT platform 19.

[0072] In this embodiment, the outsourced IT platform 19 includes a conversational artificial intelligence module, typically software, which constitutes a conversational agent 18 configured to: a) receive captured data, transmitted by the container 1 via the IT infrastructure 14; b) perform a semantic analysis of the captured data; c) produce data resulting from the semantic analysis; and d) transmit via the IT infrastructure 14, the resulting data to the container 1;

[0073] A conversation begins when the captured data is a first so-called communication data, and the resulting data is a second so-called communication data. In this case, the first communication data and the second communication data are stored by the conversational agent 18 according to methods known per se, to retain contextual information. This is then a history of the conversation composed of this contextual information which is enriched over the course of the exchanges.

[0074] To initiate a conversation, a user presses the button 8 of the container 1. The computer unit then triggers an audio capture using the microphone 5 and records the voice message emitted by the user. Then, it transmits this voice message, called the first voice message captured, to the computer infrastructure 14. The latter, equipped with voice-to-text conversion means known by the English terminology of "speech to text" and "text to speech", is responsible for transcribing the first voice message into a first corresponding text message. The computer infrastructure 14 then transmits this first text message to the conversational agent 18 on the outsourced computer platform 19.

[0075] In a conversation, the resulting data, which is a second text message, is first received by the IT infrastructure 14. This converts the second text message into a second voice message and transmits it to the container 1. Then, the container 1 broadcasts the second voice message through the loudspeaker 6, to the user, who can continue the conversation. Thus, by the sequence of these exchanges, the container 1 supports the conversation with the user.

[0076] Furthermore, being equipped with other sources of information specified above, the container 1 is also configured to transmit at least one of the various information available as a captured data message. For example, the captured data transmitted carries the information of its GPS position. Or the captured data contains a combination of information, such as the identifier, the internal temperature and the external temperature measured by the container 1.

[0077] When the captured data is therefore not the user's voice message but so-called information data, the resulting data is a command transmitted to the container 1 which applies it to at least one of the actuators. For example, the resulting command commands the unlocking of the container 1, authorizing its opening by the user. However, the user's voice message can be interpreted by the conversational agent 18 as a command to control one of the actuators. However, depending on the configuration of the container 1 and / or the conversational agent 18, the user cannot necessarily act on all the actuators equipping the container 1.

[0078] In the context of the object transport system, using a fleet of containers 1 , it is all the data sent by the fleet of containers to the conversational agent 18 which contributes to the enrichment of the model.

[0079] Furthermore, contextualizing container 1 allows the conversation to be oriented in order to provide the user with the most appropriate information possible in relation to container 1 and in particular its possibilities.

[0080] In addition, contextual information is subjected to the learning of the model of the conversational agent 18, so as to instill in it a corpus of initial conditions. Typically, this contextual information relates to the container 1, its description, its instructions for use, possible uses, frequently asked questions submitted by users, the commercial offer for transporting objects using the container 1, or information on the company managing the fleet of containers 1 forming the object transport system. For this, the conversational agent 18 is subjected to a specialization of its model in which it is provided with a series of keywords and the associated semantics to form the corpus of initial conditions.

[0081] As an example, learning the events associated with a shipment can be done using the following illustrative pseudo-code:

[0082] “Each stored event is composed of type, value where date is the date of the event, type is the type of the event and value is defined per type as follows:

[0083] - 'sender': name, address

[0084] - 'recipient'mame, address

[0085] - 'startshipment': date, latitude, longitude

[0086] - 'endShipment': date, latitude, longitude

[0087] - 'sensors':date, 'externalTemperature', measure (in °C), 'internalTemperature', measure (in °C), 'externalHumidity', measure (in%), 'internalHumidity', measure (in%), 'pressure', measure (in hPa)

[0088] - 'geolocation': date, latitude, longitude

[0089] - 'warehouse': enterDate, exitDate, latitude, longitude, name

[0090] - 'flight': fromDate, fromLatitude, fromLongitude, fromAirport, toDate, toLatitude, toLongitude, toAirport

[0091] - 'alert': date, latitude, longitude, subtype (in 'externalTemperature', 'internalTemperature', 'externalHumidity', 'internalHumidity', 'freeFall'), measure

[0092] -'events': You will return every events containing the keyword 'store' as a list without displaying the keyword itself. When 'event' is followed by a type, return every events of the defined type as a list instead of all 'store' keywords and return the closest city instead of raw location coordinates. »

[0093] L’apprentissage de la sémantique associée peut être réalisée de la façon suivante :

[0094] “A duration between two events event A and event B is defined as the difference between the date of the event B and the date of the event A. The duration of an event is defined as the difference between the exitDate and the enterDate of the event or the toDate and the fromDate of the event”

[0095] Container 1 is, for example, a package or shipping packaging in accordance with the invention.

[0096] 2. Other examples of embodiments of the invention

[0097] In Figure 2, the container 1 is represented as a piece of luggage, here a suitcase 10 forms an object transport system according to a second embodiment of the invention.

[0098] The container 1 also comprises electronic means including the on-board computer unit 2, the data storage means 3, the power supply battery 4, the microphone 5, the loudspeaker 6, the button 8, assembled according to techniques known per se. The on-board computer unit 2 of the suitcase 10 is configured to capture, using the microphone 5, requests formulated by the user of the suitcase 10 and to emit responses via the loudspeaker 6. Thus, the microphone 5 and the loudspeaker 6 form an interface with the user of the suitcase 10.

[0099] Furthermore, the on-board computer unit 2 is configured to execute the conversational intelligent agent, and thus autonomously maintain a conversation with the user of the suitcase 10.

[0100] Furthermore, the on-board computer unit 2, thanks to the communication means 9, is able to receive a specialized corpus chosen according to the intended use of the suitcase 10. For example, this corpus is specialized according to a particular trip, with information on the scheduled transit locations such as stations, airports, transport infrastructure of a city with information on the map of the metro stations, etc. Thus, the user can, thanks to a conversation with the suitcase 10, obtain information to find his way through the maze of corridors of these transit locations. In a variant of the second embodiment, the on-board computer unit 2 communicates via the communication means 9 with the conversational intelligent agent 18 remoted on the IT infrastructure 14, or on the externalized IT platform 19, not shown.

[0101] 3. Other optional features and advantages of the invention

[0102] In variants of the embodiments of the invention detailed above, it may also be provided: to record, at the level of the conversational agent 18, the captured data in a database for storing continuous learning parameters, when the captured data is at least one of the various pieces of information, so as to train a model of said conversational agent 18, according to a technique known in the English terminology of “machine learning”. Thus, the conversational agent 18 is able to train its model on the one hand and it is configured to train it automatically on the other hand;to associate the captured data with a weighting of positive or negative assessment, taken into account by the conversational agent model 18. Thus, the training of the model is a deep learning technique, known under the English terminology of “deep learning”. This increases the statistical analysis performance of the model and consequently the relevance of conversational exchanges with the user; to integrate into the container means of identification and / or authentication of the user to determine his role and consequently the conversation profile to adopt. Identification can be obtained by any known method, such as a badge, a means of voice recognition, etc.;to integrate the voice-to-text conversion means into the container 1, or to transfer these means to the outsourced platform, or to a specialized supplier. to integrate the conversational agent 18 into the IT infrastructure 14, in particular optimizing response times; to embed the conversational agent 18 in the IT unit 2 of the container 1 serving as shipping packaging, and thus give it autonomy to converse with the user; for the conversational agent 18 embedded in the IT unit 2, to receive possibly weighted continuous learning parameters, emitted by the IT infrastructure 14, the learning parameters being based on feedback from a fleet of containers 1 in operation; to specialize the intelligent conversational agent so that it adapts its semantic analysis according to the current location of the container;to transmit an instruction, in other words a voice message to the user, following the analysis of captured data, for example to issue a message requiring more precautions in handling the container 1 when a fall is detected; to use a digital touch screen as an input and / or output interface between the container 1 and the user; to integrate the first means of communication 9 with the electronic means of the suitcase 10; to create dedicated fleets per customer of a logistics carrier, with a partitioning of the contextualization and therefore of the initial corpus used by the conversational agent 18 according to the specificities of the customer. The technique described above for producing an object transport system allowing a conversational exchange can be used with different types of receptacles, for example to constitute a container of the goods transport container type.;

[0103] It should be understood that the elements shown in the figures may be implemented in various forms of hardware, software, or combinations thereof. Preferably, these elements are implemented in a combination of hardware and software on one or more suitably programmed general-purpose devices, which may include a processor, memory, and input / output interfaces.

[0104] This description illustrates the principles of the present disclosure. It will therefore be appreciated that those skilled in the art will be able to devise various arrangements which, although not explicitly described or shown herein, embody the principles of the disclosure and are included within its scope.

[0105] All examples and conditional language cited herein are intended for educational purposes to assist the reader in understanding the principles of the disclosure and the concepts contributed by the inventor to advance the art, and should be construed as not being limited to these specifically cited examples and conditions.

[0106] Furthermore, all statements of principles, aspects, and embodiments of the disclosure, as well as specific examples thereof, are intended to encompass their structural and functional equivalents. Furthermore, it is intended that such equivalents include both presently known equivalents and equivalents developed in the future, i.e., any developed element that performs the same function, regardless of its structure. Thus, for example, those skilled in the art will understand that the block diagrams presented herein represent conceptual views of illustrative circuits implementing the principles of the disclosure. Likewise, it will be appreciated that all flowcharts, flow diagrams, and the like represent various processes that may be substantially represented on a computer-readable medium and thereby executed by a computer or processor, whether or not such computer or processor is explicitly shown.

[0107] The functions of the various elements illustrated in the figures may be performed by the use of dedicated hardware as well as hardware capable of executing software in association with appropriate software. When performed by a processor, the functions may be performed by a single dedicated processor, by a single shared processor, or by a plurality of individual processors, some of which may be shared. In addition, the explicit use of the term "processor" or "controller" should not be construed as referring exclusively to hardware capable of executing software, and may implicitly include, without limitation, a digital signal processing (DSP) processor, read-only memory (ROM) for storing software, random access memory (RAM), and a nonvolatile storage medium.Additionally, some elements may be independent or grouped together, for example in a microcontroller (MCU), or a system-on-chip (SoC).

[0108] Other hardware, conventional and / or custom, may also be included. Similarly, any switches shown in the figures are conceptual only. Their function may be performed by the operation of program logic, by dedicated logic, by the interaction of a program control and dedicated logic, or even manually, the particular technique being selectable by the performer as more specifically understood in the context. In the claims herein, anything expressed as a means of performing a specified function is intended to encompass any manner of performing that function, including, for example, a) a combination of circuit elements that performs that function or b) software in any form, including, therefore, firmware, microcode, or the like, combined with suitable circuitry for executing that software to perform the function.The disclosure as defined by such claims is that the functionalities provided by the various means recited are combined and brought together in the manner provided by the claims. It is therefore considered that all means which can provide these functionalities are equivalent to those presented here.

Claims

CLAIMS 1. Object transport system enabling conversational exchange, comprising: - a fleet formed of at least one object transport container, of the reusable luggage or parcel type, the at least one container being configured so as to capture a first data component of a set of data sent by the fleet of containers; and - a conversational intelligent agent configured to: a) receive the first data item, transmitted by said container; b) perform a semantic analysis of said first data item; c) produce, optionally, a second data item resulting from the semantic analysis; d) transmit, optionally, said second data item to said at least one container; said container being further configured to process the second data item received.

2. Object transport system according to claim 1, wherein the first data and the second data are so-called communication data which form a conversational exchange, and wherein said first data comes from a user of said container; said processing of the second data by the container consists of communicating said second data to said user, the conversational intelligent agent is further configured so as to: - retain contextual information from the conversational exchange, - use contextual information during said semantic analysis.

3. Object transport system according to claim 1 wherein the first data is so-called information data, of the type, an identifier, or a physical quantity, or an operating configuration.

4. Object transport system according to claim 3 wherein the second data, resulting from the semantic analysis of the first data, called information, is a command applied by said container.

5. Object transport system according to one of claims 3 or 4, in which the conversational intelligent agent, which is configured so as to: - record the first so-called information data in a continuous learning parameter base; and - train a model of the conversational intelligent agent with the continuous learning parameters stored in said database.

6. Object transport system according to one of claims 3 to 5, in which the intelligent conversational agent is configured to choose a conversation language based on a position of the container, received in the first so-called information data.

7. Object transport system according to one of the preceding claims, in which the container comprises a means for entering said first so-called communication data, which is a voice capture means and / or a text input means.

8. Object transport system according to claim 7, comprising means for text transcription of said voice capture, so that the intelligent conversational agent receives a first data in text format.

9. System for transporting objects according to one of the preceding claims, in which the container comprises a means of sound reproduction and / or a means of displaying said second so-called communication data.

10. Object transport system according to claim 9, comprising a means of voice transcription of the second so-called communication data produced in text format by the intelligent conversational agent.

11. Object transport system according to one of the preceding claims, in which the conversational intelligent agent is integrated into a computer unit on board said container and / or on a central computer platform configured to communicate with said container.

12. Object transport system according to claim 12, in which the conversational intelligent agent is transferred to an externalized computing platform configured to communicate with said central computing platform.

13. Object transport system according to one of the preceding claims, in which: - the container is configured to identify the user, categorize the identified user into a conversation profile type and transmit the conversation profile type to the conversational intelligent agent; - the intelligent conversational agent being configured to adapt the semantic analysis according to the typical conversation profile.

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