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EP-4736401-A1 - COMPUTER-IMPLEMENTED METHOD FOR PROVIDING A STARTING SEQUENCE, IN PARTICULAR A PARTIALLY TEXT-BASED STARTING SEQUENCE

EP4736401A1EP 4736401 A1EP4736401 A1EP 4736401A1EP-4736401-A1

Abstract

The invention relates to a computer-implemented method (10), using an algorithm (12), in particular machine learning, having the following steps: - inputting (14) at least one input sequence (22) into the algorithm (12), said input sequence describing a product (32), system and/or method which is novel, in particular at least for the algorithm (12), preferably patentable; - processing (16) the input sequence (22) by collecting meta data (76) on the product (32), system and/or method using the algorithm (12); - generating (18) a starting sequence (30) by means of the algorithm (12), said starting sequence being based on at least some of the collected meta data (34) and in particular being at least partially text-based; and - providing (20) the starting sequence (30).

Inventors

  • DAUB, Louis

Assignees

  • Impalst GmbH

Dates

Publication Date
20260506
Application Date
20240701

Claims (20)

  1. 1 . Computer-implemented method (10), with an algorithm (12), in particular machine learning, with the following steps: Input (14) of at least one input sequence (22) into the algorithm (12), which describes a product (32), system and/or method that is novel, preferably patentable, at least for the algorithm (12); - processing (16) the input sequence (22) by collecting metadata (76) about the product (32), system and/or method using the algorithm (12); - generation (18) of an output sequence (30) based on at least part of the collected metadata (34), in particular at least partially text-based, by means of the algorithm (12); and - Providing (20) the output sequence (30).
  2. 2. Computer-implemented method (10) according to claim 1, characterized in that the input sequence (22) describes at least one sub-element (36) and/or method step and/or a combination of at least two sub-elements (36) of the product (32), system and/or method.
  3. 3. Computer-implemented method (10) according to claim 1 or 2, characterized in that the algorithm (12) has at least one technology recognition model (38) with which the product (32), system and/or method, in particular the at least one sub-element (36) and/or the method step, is detected.
  4. 4. Computer-implemented method (10) according to claim 2 and 3, characterized in that the at least one subelement (36) is evaluated on the basis of structural features within the input sequence (22) with the technology recognition model (38).
  5. 5. Computer-implemented method (10) according to one of the preceding claims, in particular according to claim 2, characterized in that the product (32), method and/or system, in particular the at least one subelement (36), is described in abstract form in the input sequence (22).
  6. 6. Computer-implemented method (10) according to one of the preceding claims, characterized in that at least one concrete input sequence (24) is entered, which describes the product (32), system and/or method in concrete form.
  7. 7. Computer-implemented method (10) according to claim 2, characterized in that at least one graphic input sequence (26) is entered which graphically represents the product (32), the system and/or the method and/or the at least one sub-element (36) of the product (32), system and/or method, in particular in the form of a technical drawing.
  8. 8. Computer-implemented method (10) according to claim 7, characterized in that the input sequence (22) is linked to the graphical input sequence (26).
  9. 9. Computer-implemented method (10) according to one of the preceding claims, characterized in that at least one further input sequence (28) is entered, which describes a, in particular obvious, further product, system and/or method.
  10. 10. Computer-implemented method (10) according to one of the preceding claims, characterized in that, in particular with the algorithm, at least one, in particular private, property database (44) is accessed which stores metadata (46), in particular information on, preferably technical, properties and/or advantages, of at least one element.
  11. 11. Computer-implemented method (10) according to one of the preceding claims, characterized in that, in particular with the algorithm, at least one, in particular private, sequence database (48) is accessed with a plurality of, in particular text-based, sequences (50), which each describe a further product, system and/or method, in particular with metadata (52) for the further product, system and/or method.
  12. 12. Computer-implemented method (10) according to claim 11, characterized in that the sequences (50) in the sequence database (48) are divided into classes (54) and the input sequence (22) is assigned to one of the classes (54) on the basis of metadata of the input sequence (22).
  13. 13. Computer-implemented method (10) according to claim 12, characterized in that the metadata (52) of the product (32), system and/or method are at least partially taken from the assigned class (54) of the sequence database (48).
  14. 14. Computer-implemented method (10) according to claim 12, characterized in that trend properties (56) are stored in the sequence database (48) for the respective classes (54), which are transferred to the technology recognition model (38) for processing (16) of the input sequence (22) with the algorithm (12).
  15. 15. Computer-implemented method (10) according to claim 3, characterized in that at least one implementation (58) of the product (32), system and/or method and/or of the at least one sub-element (36) is derived with the technology recognition model (38) on the basis of the abstract form of the product (32), system and/or method with the metadata (76).
  16. 16. Computer-implemented method (10) according to claim 2 and 3, characterized in that the technology recognition model (38) links the metadata (76) with the subelements (36) of the product (32), system and/or method and the implementation (58) is derived by means of property relationships and/or advantage relationships of the metadata (76).
  17. 17. Computer-implemented method (10) according to claim 16 and in particular claim 11, characterized in that a relationship matrix (116) is generated with the technology recognition model (38) on the basis of the property relationship and/or advantage relationships of the metadata (76) of the sub-elements (36), which creates a link between at least the sub-elements (36) of the product (32), system and/or method, in particular the sub-elements of the sequences (50) of the sequence database (48).
  18. 18. Computer-implemented method (10) according to claim 17, characterized in that the links (118) of the relationship matrix (116) are weighted at least based on the metadata (76) of the input sequence (22).
  19. 19. Computer-implemented method (10) according to claim 18, characterized in that based on the weighting (106) of the links (118), at least the implementation (58) of the product (32), system and/or method, in particular of the at least one sub-element (36), is integrated in the generation of the output sequence (30).
  20. 20. Computer-implemented method (10) according to one of the preceding claims, characterized in that, in particular with the algorithm (12), at least one further, in particular public, sequence database (62), preferably a patent database, is accessed which has a plurality of sequences (64).

Description

Computer-implemented method for providing an output sequence, in particular at least partially text-based State of the art The invention relates to a computer-implemented method with an algorithm according to claim 1, a computer-implemented method for learning an algorithm according to claim 22, a computer-implemented method for continuously learning an algorithm according to claim 23, a system according to claim 24, a computer program with program code according to claim 25 and a computer-readable storage medium according to claim 26. The automated, especially AI-supported, generation of patent applications brings with it a multitude of challenges, for example with regard to quality and/or data security. The object of the invention is in particular to provide a computer-implemented method with advantageous properties with regard to algorithm-based processing of, in particular, technically novel, preferably patentable, input sequences. The object is achieved according to the invention by the features of patent claim 1, while advantageous embodiments and further developments of the invention can be taken from the subclaims. Advantages of the invention A computer-implemented method is proposed, using an algorithm, in particular machine learning, with the following steps: Input of at least one input sequence into the algorithm which describes a product, system and/or method which is novel, in particular at least for the algorithm, and preferably patentable; - processing the input sequence by collecting metadata about the product, system and/or process using the algorithm; - generating an output sequence based on at least part of the collected metadata, in particular at least partially text-based, by means of the algorithm; and - Provision of the output sequence, suggested. In particular, the input sequence is entered in digital form. The input sequence is particularly preferably entered as a text-based input sequence, in particular in the form of patent claims. Alternatively or additionally, the input sequence is entered as a graphic and/or a voice sequence and/or a mind map that clearly describes the product, system and/or method and/or a computer program with program code that clearly describes the product, system and/or method. If the input sequence is designed as a computer program with program code, this can preferably be analyzed and/or executed directly, and in particular on the basis of this, metadata on an analysis result and/or an execution result can be collected when processing the input sequence. Preferably, with such a design with the computer program with program code, an output sequence can be output directly based on the execution of the computer program with program code. Particularly preferably, the input sequence describes an inventive idea of a technically implementable product, system and/or method. The computer-implemented method is preferably carried out by a computing device. The algorithm, in particular machine learning, is particularly preferably carried out by the computing device. A "computing device" is to be understood in particular as a device with an information input, information processing and information output. The computing device advantageously has at least one processor, a memory unit, an input and output means, further electrical components, an operating program, control routines, control routines and/or calculation routines. The memory unit of the computing device particularly preferably comprises a computer program with program code for carrying out the algorithm. The algorithm, in particular machine learning, in particular the computer program with program code, is particularly preferably carried out with the processor. The computing device is preferably at least part of a computer. The components of the computing device are preferably arranged on a common circuit board and/or advantageously arranged in a common housing. Alternatively, however, the computing device can also be designed as a distributed, in particular virtual, computing device, such as a cloud. The product is preferably a device, machine, equipment, product or composition, which is preferably manufactured by a human. The system is preferably an arrangement of products, in particular parts or components, which preferably interact with each other to particularly preferably provide a specific function or service. Alternatively or additionally, the system is a software application, a network or another type of technical structure. The method is preferably a method for carrying out a specific task or for solving a technical problem. The method is, for example, a production process, a process for manufacturing products and/or a method for processing data or information. The fact that the product, system and/or method is “at least novel for the algorithm” should be understood to mean that the product, system and/or method is not present in any database accessible to the algorithm and in particular in any t