JP7387329B2 - Information processing device, information processing method, and program - Google Patents

Description

The present invention relates to an information processing device, an information processing method, and a program.

A patent applicant who intends to obtain a patent right files a patent application with the Japan Patent Office, and as a result of the examination of the invention described in the claims attached to the application at the Patent Office, it is determined that the invention cannot be patented. You may receive a notice of reasons for refusal stating the reasons for refusal.

In such a case, in order to resolve the reasons for refusal, the patent applicant must submit a written amendment amending the scope of claims attached to the application within the period specified by the Patent Office, and a statement that the reasons for refusal have been resolved by the amendment. The written opinion can be submitted to the Patent Office.

When amending the claims, the patent applicant cannot know whether the reasons for refusal have been resolved until he or she is notified of the results of the examination conducted at the Patent Office again. In addition, if the reasons for refusal are not resolved as a result of examining the invention stated in the amended claims, you may receive another notification of reasons for refusal or a decision of refusal, and it may take time to obtain a patent right. There is a risk of not being able to obtain patent rights.

Therefore, if the invention described in the amended scope of claims can be evaluated from the perspective of whether it will receive a grant of patent before submitting a written amendment, it may be possible to avoid receiving a notice of reasons for refusal or a decision of refusal again. .

Patent Document 1 discloses an evaluation device that evaluates the specification of a patent application filed.

Japanese Patent Application Publication No. 2012-194921

In Patent Document 1, patent applications are evaluated from the viewpoint of whether the patent application to be evaluated contributes to preventing other patent applications from obtaining rights. However, with the technique described in Patent Document 1, it is not possible to evaluate a patent application from the viewpoint of whether the invention described in the revised claims will be granted a patent.

Therefore, an object of the present invention is to provide an information processing device, an information processing method, and a program that can evaluate whether the invention described in the revised claims will be granted a patent.

An information processing device as one aspect of the present invention that solves the above problems is an information processing device that evaluates the invention described in the amended first claim, and is an information processing device that evaluates the invention described in the amended first claim. Information indicating the characteristics of the scope and information indicating the characteristics of the second claim after the amendment are included as input data, and the invention described in the second claim after the amendment is granted a patent. Information indicating the characteristics of the first claim before modification and the first patent after modification in a learning model obtained by learning using learning data including information indicating whether the training data has been received as training data. The present invention includes an evaluation section that outputs evaluation data indicating whether or not the revised invention described in the first claim will be granted a patent by inputting information indicating the features of the claim .

According to the present invention, it is possible to provide an information processing device, an information processing method, and a program that can evaluate whether the modified invention described in the claims will be granted a patent.

FIG. 1 is a diagram showing the configuration of an information processing device. FIG. 3 is a diagram showing processing in the information processing device. It is a schematic diagram showing a learning model in an evaluation part. It is a flowchart which shows the learning process by a learning part. 7 is a flowchart showing evaluation processing by an evaluation section. FIG. 3 is a diagram showing a screen displayed by a display device. FIG. 2 is a diagram showing a configuration including a plurality of information processing devices.

Below, preferred embodiments of the present invention will be described in detail with reference to the drawings. In each figure, the same reference numerals are given to the same members, and overlapping explanations will be omitted.

<First embodiment>
FIG. 1 is a diagram showing the configuration of an information processing device 10. As shown in FIG. The processing unit 101 is a central processing unit (CPU) that executes an OS (Operating System) and various application programs. Further, the processing unit 101 is not limited to a central processing unit, and may be a processor or circuit such as a microprocessing unit (MPU), a graphics processing unit (GPU), or an application specific integrated circuit (ASIC). Further, the processing unit 101 may be a processor or circuit such as a digital signal processor (DSP), a data flow processor (DFP), or a neural processing unit (NPU). Furthermore, the processing unit 101 may be any combination of these processors or circuits. The ROM 102 is a memory that stores fixed data among programs executed by the processing unit 101 and parameters for calculations. The RAM 103 is a memory that provides a work area for the processing unit 101 and a temporary storage area for data. The input unit 105 is an input device including a mouse, a keyboard, etc., and the display unit 106 is a display device such as a CRT or a liquid crystal display. Further, the input unit 105 and the display unit 106 may be an integrated device such as a touch panel. Further, the input unit 105 and the display unit 106 may be configured as devices separate from the computer. The storage unit 104 is a storage device such as a hard disk device, CD, DVD, or memory card, and stores various programs, various data, and the like. The communication unit 107 connects to a network and performs communication. The communication unit 107 is used, for example, when connected to a LAN to perform data communication using a communication protocol such as TCP/IP, and to communicate with other communication devices. The bus 108 is connected to each section in the information processing apparatus 10, such as the processing section 101, ROM 102, RAM 103, storage section 104, input section 105, display section 106, and communication section 107, to perform data communication between each section. used for. For example, data received by the communication unit 107 from another communication device is transmitted to the storage unit 104 via the bus 108 and stored therein.

Further, the information processing device 10 may be a stationary computer such as a workstation (WS) or a personal computer (PC), or may be a portable computer such as a notebook PC or a smartphone.

FIG. 2 is a diagram showing processing in the information processing device. The processing section 101 includes a learning section 201 and an evaluation section 202. The learning unit 201 acquires a learning model 213 by learning using the learning data 211 acquired from the storage unit 104.

Here, the learning data 211 includes, as input data 221, information indicating the characteristics of the claims before amendment and information indicating the characteristics of the claims after amendment. Further, the learning data 211 includes, as the teacher data 222, information indicating whether or not the invention described in the claims after the amendment has been granted a patent. Information including a plurality of combinations of input data 221 and teacher data 222 is assumed to be learning data 211. Further, the learning data 211 can be collected from the description of the scope of claims before and after amendment in a patent application that has already undergone examination and a decision has been made, and information indicating whether a decision has been made.

The information indicating the features of the claims may include the number of characters or words in the claims included in the claims. Generally, as the number of characters or words in a claim statement increases, the technical scope of the claimed invention will be interpreted narrowly, and differences with prior art documents will likely occur, leading to rejection of novelty or inventive step violation. There is a high possibility that the reason will be resolved and a patent will be granted. Additionally, if a term in the scope of a claim is unclear and a reason for refusal is received as a violation of description requirements, adding a description to explain the term will resolve the reason for refusal. There is a higher possibility that a patent will be granted. Therefore, the number of characters or words in a claim has a correlation with whether or not a claim can be granted a patent.

Further, the information indicating the features of the claim may include the number of characters or words in the first part of the claim included in the claim. Here, the first part of the claim is also called a preamble, and is a part in which the general concept or preconditions of the invention, indicating the technical field and application of the invention, are described. By amending the first part of the claim, the technical field and application of the invention will be limited, making it more likely that the invention will differ from prior art documents, and increasing the possibility that the invention will be granted a patent. Therefore, the number of characters or words in the first part of a claim has a correlation with whether or not a patent can be granted.

Further, the information indicating the feature of the claim can include the number of characters or words in the feature part of the claim included in the claim. Here, the characteristic part of the claims is also called the latter part, and is a part in which the features of the invention are described. By amending the characteristic parts of the claims, the characteristics of the invention will become clearer, making it more likely that the invention will differ from prior art documents, and increasing the possibility of being granted a patent. Therefore, the number of characters or words in the characteristic part of a claim has a correlation with whether or not a patent can be granted.

Further, the information indicating the features of the claims may include the number of constituent features constituting the claims included in the claims. Here, the constituent elements include technical elements listed for specifying the claimed invention. As the number of constituent elements increases, differences with prior art documents are more likely to occur, and the possibility of being granted a patent increases. Therefore, the number of constituent features constituting a claim has a correlation with whether or not a claim will be granted a patent.

Further, the information indicating the characteristics of the claims may include the number of times that words included in the different parts of the claims before amendment and the claims after amendment appear in the prior art document. . Here, the number of times it appears in prior art documents can be, for example, the number of times it appears in cited documents listed in the notice of reasons for refusal. If the number of occurrences in cited documents is small, differences from the cited documents are likely to occur, and the invention described in the claims is more likely to satisfy patent requirements such as novelty and inventive step. Therefore, the number of times a word included in a different part appears in prior art documents has a correlation with whether or not a patent will be granted. In addition, if there are multiple words included in the different parts, the number of times they appear in the prior art document may be the number of times each of the multiple words appear, the total number of times the multiple words appear, or the average number of times the words appear. It may be the number of times. Here, prior art documents include cited documents cited in the notice of reasons for refusal for reasons of refusal for violation of novelty or inventive step, well-known technical documents indicating well-known technology, or patent documents described in the specification. , non-patent literature, etc.

Further, the information indicating whether or not a patent has been granted may include, for example, a value of 1 if a patent has been granted, and 0 if a patent has not been granted. Furthermore, the information indicating whether or not a patent has been granted may include, for example, a value of 100% if a patent has been granted, and 0% if a patent has not been granted.

Further, the learning model 213 can be configured by, for example, a neural network. FIG. 3 is a diagram showing the learning model 213 in the evaluation unit 202. Here, the neural network is a model having a multilayer network structure including an input layer, a middle layer, and an output layer. The learning model 213 can be obtained by optimizing the connection weighting coefficients within the neural network using an algorithm such as error backpropagation using learning data indicating the relationship between input data and teacher data. The error backpropagation method is a method of adjusting connection weighting coefficients between nodes of each neural network so that the error between output data and teacher data is reduced.

Here, an example has been described in which the learning model 213 configured by a neural network is acquired, but the learning model 213 is not limited to a neural network. For example, it may be configured using other models such as a support vector machine or a decision tree. Further, the learning model 213 may include a model that obtains the input data 221 and the evaluation data 214 using a table, polynomial, etc. that shows the relationship between the input data 221 and the evaluation data 214. Then, by inputting new input data 221 to the acquired learning model 213, evaluation data 214 is output as output.

Next, learning processing by the learning unit 201 will be explained. FIG. 4 is a flowchart showing learning processing by the learning unit 201. In S401, the learning unit 201 acquires learning data 211 from the storage unit 104. Here, the learning unit 201 may acquire learning data 211 stored in the RAM 103 or an external storage device.

In S402, the learning unit 201 acquires the learning model 213 through learning. For example, when the learning model 213 is configured by a neural network, the learning section 201 may include an error detection section (not shown) and an updating section (not shown). The error detection unit obtains the error between the evaluation data 214 output from the output layer of the neural network and the teacher data according to the input data 212 input to the input layer. The error detection unit may use a loss function to calculate the error between the evaluation data 214 from the neural network and the teacher data. The updating unit updates connection weighting coefficients between nodes of the neural network, etc., based on the error obtained by the error detection unit, so that the error becomes smaller. This updating unit updates the connection weighting coefficients and the like using, for example, an error backpropagation method.

In S403, the learning unit 201 outputs the learning model 213 acquired in S402 to the evaluation unit 202. Here, the learning unit 201 may store the learning model 213 in the storage unit 104, and the evaluation unit 202 may acquire the stored learning model 213. Further, the learning model 213 may be stored in an external storage device via the RAM 103 or the communication unit 107.

Next, evaluation processing by the evaluation unit 202 will be explained. FIG. 5 is a flowchart showing evaluation processing by the evaluation unit 202. In S501, the evaluation unit 202 obtains input data 212 from the storage unit 104. Here, the evaluation unit 202 may obtain input data 212 stored in the RAM 103 or the external storage unit 104. Furthermore, the evaluation unit 202 may obtain input data 212 input via the input unit 105.

Here, the input data 212 includes information indicating the characteristics of the claims before modification and information indicating the characteristics of the claims after modification, which are to be evaluated as to whether a grant of a patent will be granted or not. The details are the same as the input data 221 included in the learning data 211.

In S502, the evaluation unit 202 acquires the learning model 213 from the learning unit 201. Here, the evaluation unit 202 may acquire the learning model 213 stored in the RAM 103 or an external storage device.

In S503, the evaluation unit 202 inputs the input data 212 obtained in S501 to the learning model 213 obtained in S502, and obtains evaluation data 214 output from the learning model 213.

Here, the evaluation data 214 is, for example, 1 if the invention described in the revised claims can be granted a patent; If this is not possible, the information may include a value of 0. Furthermore, the evaluation data 214 can be information that includes a value between 0% and 100%, for example, as the probability that the invention described in the revised claims will be granted a patent.

In S504, the evaluation unit 202 outputs the evaluation data 214 acquired in S503 to the display unit 106. Here, the evaluation section 202 may store the evaluation data 214 in the storage section 104, and the display section 106 may display the stored evaluation data 214. Furthermore, the evaluation data 214 may be stored in an external storage device via the RAM 103 or the communication unit 107.

The display unit 106 displays the evaluation data 214 output by the evaluation unit 202 on a screen. FIG. 6 is a diagram showing a screen displayed by the display device. The screen 600 displays a display area 601 that displays the claims before modification, a display area 602 that displays the claims after modification, and a display area 603 that displays the evaluation data 214. Ru. In addition, in the display area 601 and the display area 602, the color and font of the characters of the different parts are added so that the different parts between the claims before amendment and the claims after amendment stand out. etc. may be changed and displayed.

As described above, according to the information processing apparatus of the present embodiment, evaluation data regarding the amended scope of claims can be acquired using the learning model acquired through learning. It is possible to evaluate whether an invention will be granted a patent.

<Second embodiment>
Next, an information processing apparatus according to a second embodiment will be described. Note that matters not mentioned here may follow the first embodiment. In this embodiment, in addition to the information indicating the features of the claims, other information is input to the input data 221 included in the learning data 211 used in the learning process of the learning unit 201 and the evaluation process in the evaluation unit 202. It is used as input data 212 used for.

The input data 221 and the input data 212 can include information indicating reasons for refusal notified to the invention described in the claims before amendment or modification. The notification of reasons for refusal includes, for example, violation of description requirements, violation of inventive step, violation of inventive step, etc., and information indicating the reason for refusal may include letters, numbers, etc. indicating the type of reason for refusal. Further, a notice of reasons for refusal may be received multiple times, and the requirements for amending the scope of claims may differ depending on the number of times a notice of reasons for refusal is received or whether it is the last notice of reasons for refusal. The information indicating the reasons for refusal may include characters, numbers, etc. indicating the number of times the notification of reasons for refusal has been received, or whether it is the last notification of reasons for refusal. Depending on the information indicating the reason for refusal, what kind of information among the information indicating the features of the claims that contributes to the possibility of receiving a patent decision will differ. Therefore, information indicating the reasons for refusal has a correlation with whether or not a patent will be granted.

The input data 221 and the input data 212 can include information indicating the patent classification of the invention described in the claims. The information indicating the patent classification may include, for example, letters, numbers, etc. indicating codes included in the International Patent Classification (IPC). The patent classification represents the classification of the technical content of the invention described in the claims. Depending on the patent classification, the type of information that contributes to the possibility of receiving a grant of a patent differs among the information indicating the characteristics of the scope of a claim. Therefore, the information indicating the patent classification has a correlation with whether or not a patent can be granted.

The input data 221 and the input data 212 can include information indicating the country in which the patent application was filed. In principle, patent applications are examined in each country, and the examination methods and standards differ from country to country, so it is important to know what information, out of the information indicating the characteristics of the scope of claims, contributes to the possibility of receiving a patent grant. It will be different. Therefore, information indicating the country in which the patent application was filed has a correlation with whether or not the patent application will be granted.

The input data 221 and the input data 212 can include information indicating the filing date of the patent application. Patent applications are examined in accordance with patent laws and examination standards and guidelines regarding examinations conducted by the Patent Office. If those laws, examination standards, and guidelines are revised, the contents of the applicable laws, examination standards, and guidelines may differ depending on the filing date. Therefore, information indicating the filing date of a patent application has a correlation with whether or not the patent application will be granted. Further, in order to specify the time when the examination was conducted, the input data 221 and the input data 212 may include information indicating the date on which the notice of reasons for refusal was issued. In addition, in the case of countries that have adopted an application examination system, information indicating the date on which a request for application examination was filed may be included as input data 221 and input data 212 in order to specify the time when examination was started. good. Further, in the case of an application claiming priority, the input data 221 and the input data 212 may include information indicating the priority date.

The input data 221 and the input data 212 can include information indicating the examiner who examined the patent application. The information indicating the examiner may include, for example, letters, numbers, etc. indicating the examiner. Examination procedures and methods may differ depending on the examiner, and the type of information that indicates the characteristics of the claims that contributes to the possibility of being granted a patent will vary. Therefore, the information indicating the examiner has a correlation with whether or not a patent will be granted. Furthermore, the input data 221 and the input data 212 can include information indicating the department to which the examiner who examined the patent application belongs. Similarly, examination procedures and methods may differ depending on the department to which the examiner belongs, and what information, out of the information indicating the characteristics of the claims, contributes to the possibility of receiving a patent grant. It will be different. Therefore, information indicating the department to which an examiner belongs has a correlation with whether or not a patent will be granted.

As described above, according to the information processing apparatus of the present embodiment, it is possible to obtain evaluation data regarding the amended scope of claims using the learning model acquired through learning. It is possible to evaluate whether an invention will be granted a patent. In addition to the information indicating the characteristics of the claims, other information is used as the input data 221 and the input data 212, so it is possible to more accurately determine whether the invention described in the revised claims will be granted a patent. can be evaluated.

<Third embodiment>
Next, an information processing apparatus according to a third embodiment will be described. Note that matters not mentioned here may follow the first embodiment and the second embodiment. In this embodiment, a mode will be described in which learning processing by the learning section 201 and evaluation processing by the evaluation section 202 are performed in different information processing apparatuses. In this embodiment, for example, it is possible to implement a configuration in which learning processing that requires higher processing power is performed on a stationary computer, and evaluation processing that does not require higher processing power than learning processing is performed on a portable computer.

FIG. 7 is a diagram showing a configuration consisting of a plurality of information processing devices. The information processing device according to this embodiment includes a first information processing device 71 and a second information processing device 72. The first information processing device 71 and the second information processing device 72 are information processing devices having the same configuration as the information processing device 10 described in the first embodiment. Further, the first information processing device 71 and the second information processing device 72 can be connected to a network such as a LAN and can transmit and receive data through each other's communication unit 107 (not shown in FIG. 7). Further, the network may not only connect the first information processing device 71 and the second information processing device 72 by wire, but also connect the first information processing device 71 and the second information processing device 72 wirelessly by using a wireless LAN or the like. good.

The processing section 101 in the first information processing device 71 includes a learning section 201. Furthermore, the processing section 101 in the second information processing device 72 includes an evaluation section 202. The processing unit 101 in the first information processing device 71 performs a learning process using the learning unit 201 and transmits information regarding the acquired learning model 213 to the second information processing device 72 . Here, the information regarding the learning model 213 includes data such as the number of nodes in each layer, the number of intermediate layers, and connection weighting coefficients between nodes, as parameters that define the structure of the neural network.

The processing unit 101 in the second information processing device 72 performs evaluation processing using the evaluation unit 202, outputs evaluation data 214, and stores the evaluation data 214 in the storage unit 104. Further, the evaluation data 214 may be stored in an external storage device via the RAM 103 or the communication unit 107, or may be displayed on a screen by the display unit 106.

As described above, according to the information processing apparatus of the present embodiment, it is possible to obtain evaluation data regarding the amended scope of claims using the learning model acquired through learning. It is possible to evaluate whether an invention will be granted a patent. Furthermore, according to the information processing apparatus of this embodiment, a stationary computer performs learning processing that requires higher processing power, and a portable computer performs evaluation processing that does not require higher processing power than learning processing. can be realized.

The present invention can also be realized by a process in which a program that implements one or more functions of the above-described embodiments is supplied to a system or device via a network or a storage medium, and a computer of the system or device reads and executes the program. be. A computer has one or more processors or circuits and may include separate computers or a network of separate processors or circuits for reading and executing computer-executable instructions.

Furthermore, the first to third embodiments can be implemented not only independently, but also in any combination of the first to third embodiments.

Claims (10)

An information processing device for evaluating the invention recited in the revised first claim, comprising:
Information indicating the characteristics of the second claim before the amendment and information indicating the characteristics of the second claim after the amendment are included as input data, and the information of the second claim after the amendment is Information indicating the characteristics of the first claim before modification in a learning model obtained by learning using learning data that includes information indicating whether the invention described in the scope has been granted a patent as training data ; By inputting information indicating the characteristics of the revised first claim , output evaluation data indicating whether the invention described in the revised first claim will be granted a patent. An information processing device characterized by having an evaluation section that performs. The information processing apparatus according to claim 1, further comprising a learning unit that acquires the learning model by learning using the learning data. The information indicating the feature of the first claim includes the number of characters or words of the claim included in the first claim , and the information indicates the feature of the second claim. 3. The information processing apparatus according to claim 1, wherein the information indicating the number of characters or words included in the second claim . The information indicating the features of the first claim includes the number of characters or words in the first part or the characteristic part of the claim included in the first claim, and The information indicating the feature of the claim includes the number of characters or words in the first part or the characteristic part of the claim included in the second claim. The information processing device according to any one of Items 1 to 3. The information indicating the features of the first claim includes the number of constituent features constituting the claims included in the first claim , and the information indicates the features of the second claim. The information processing apparatus according to any one of claims 1 to 4, wherein the information indicating the number of constituent features constituting the claim included in the second claim. . The information indicating the characteristics of the first claim includes words included in the difference between the first claim before the amendment and the first claim after the amendment as prior art. The information indicating the characteristics of the second claim, including the number of times it appears in the literature, is the difference between the second claim before the amendment and the second claim after the amendment. 6. The information processing apparatus according to claim 1, wherein the information processing apparatus includes the number of times a word included in the part appears in a prior art document . The learning data includes information indicating the reasons for refusal notified to the invention described in the second claim before amendment, and information indicating the patent classification of the invention described in the second claim. , information indicating the country in which the patent application was filed, the filing date in which the patent application was filed, the date on which a notice of reasons for refusal was issued, the date on which a request for examination of the application was filed, and the priority date of the patent application with a priority claim. and information indicating the examiner who examined the patent application or the department to which the examiner belongs. The information processing device described in . The evaluation unit includes information indicating the reasons for refusal notified to the invention described in the first claim before modification, and information indicating the patent classification of the invention described in the first claim. , information indicating the country in which the patent application was filed, the filing date in which the patent application was filed, the date on which a notice of reasons for refusal was issued, the date on which a request for examination of the application was filed, and the priority date of the patent application with a priority claim. outputting the evaluation data by inputting into the learning model at least one of information indicating one of the patent applications and information indicating the examiner who examined the patent application or the department to which the examiner belongs; An information processing device according to any one of claims 1 to 7 . An information processing method for evaluating the invention recited in the revised first claim, comprising:
Information indicating the characteristics of the second claim before the amendment and information indicating the characteristics of the second claim after the amendment are included as input data, and the information of the second claim after the amendment is Information indicating the characteristics of the first claim before modification in a learning model obtained by learning using learning data that includes information indicating whether the invention described in the scope has been granted a patent as training data ; By inputting information indicating the characteristics of the revised first claim , output evaluation data indicating whether the invention described in the revised first claim will be granted a patent. An information processing method comprising an evaluation step. A program that causes a computer to execute an information processing method for evaluating the invention recited in the revised first claim, comprising:
Information indicating the characteristics of the second claim before the amendment and information indicating the characteristics of the second claim after the amendment are included as input data, and the information of the second claim after the amendment is Information indicating the characteristics of the first claim before modification in a learning model obtained by learning using learning data that includes information indicating whether the invention described in the scope has been granted a patent as training data ; By inputting information indicating the characteristics of the revised first claim , output evaluation data indicating whether the invention described in the revised first claim will be granted a patent. A program for causing a computer to execute an information processing method characterized by having an evaluation step.

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