Kancelaria Rzeczników Patentowych

The subject of the application is an acoustic panel module for sound attenuation, comprising a self-supporting front panel (10) wherein the module has a shape defined by the plane of the front panel (10),characterized in that front panel (1) is made of a biomass-based material having a density of from 200 kg/m³ and a thickness of from 2 cm to 40 cm, and the shape of the surface of the front panel (1) is provided with a structure enabling the absorption of the energy of incident sound waves and the reflection of incident sound waves whose energy is not absorbed by the structure of the surface (1) in a direction substantially different from the direction perpendicular to the plane of the panel module, wherein openings (7) are arranged on the surface of the front panel (1) to support the absorption of sound wave energy.

The subject of the application is a computer-implemented method for generating a digital signature for a message (M) in a private-key and public-key cryptographic architecture, enabling recovery of the private key for the message (M) based on the message (M), using two pairs of cryptographic keys, wherein the first key pair comprises a first private key (x_A) and a first public key (y_A), and the second key pair comprises a second private key (x_B) and a second public key (y_B), a signature for the message (M) is generated in steps in which: a first auxiliary value (d) is generated such that d←yBxA, a second auxiliary value (k) is generated such that k ← hash(d ∥ M), a third auxiliary value (r) is generated such that r ← g^k, a hash function value (e) is generated for the message (M) and the third auxiliary value (r) such that e ← hash(M ∥ r), a signature (s) is generated for the message (M) s ← k - x_A · e mod q, the message (M) is linked with the signature (s) and the hash function value (e). The subject of the application also relates to a computer-implemented method for recovering the private key (x_A) from a message (M).

A user (41) behaviour analysis system in the form of user (41) interactions with an end device (12), comprising an input module (43) that receives signals from the end device's (12) sensors (42) during user (41) interactions with end device (12), a memory module (14) that records sensor (42) signals during user (41) interactions with end device (12), a comparison module (44) that uses a pre-trained machine learning model (30), and an output module (46) that provides the response from the comparison module (44). The system is characterised in that the comparison module (44) uses a pre-trained machine learning model (30) that responds to a set of signals from the sensors (42) of the device (12) during the interaction of the user (41) with the end device, representing at least two interactions (I, G) of the user (41) with the end device (12), to generate a response in the form of a similarity metrics (P) between interactions (I, G); furthermore, the comparison module (44) contains a pre-trained machine learning model (30) which is a machine learning model trained on interactions originating from a closed group of end device users (12) other than the current end user (41).

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