DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant’s arguments with respect to the rejection of the claims under 35 U.S.C. 101 have been fully considered but are not persuasive.
Applicant argues that amended claim 1 is not directed to a mental process, but rather, is directed to a specific-data processing operation (Remarks, filed 06/24/26, pp. 21-22). Further, Applicant argues that, even assuming that the claims recite an abstract idea, the claims integrate the alleged abstract idea into a practical application and/or recite significantly more than any alleged abstract idea through the use of a specific data-processing sequence/ordered combination (first matrix structure, second matrix structure, block compression, clustered components, input data, and trained neural network model) (Remarks, filed 06/24/26, pp. 22-24). Examiner respectfully disagrees.
The limitation of generating a first matrix structure encompasses a mental process. That is, the first matrix structure is a tabular organization of information (“wherein the first matrix structure includes user identification information of the target user and the plurality of users as rows and question identification information of the plurality of questions as columns”, with values placed in the corresponding cells). A human could reasonably construct the same structure mentally and/or using pen and paper (see MPEP 2106.04(a)(2)(III)(B), citing CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372-1373, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011), where constructing a “map” of credit card numbers was found to be a mental process because it amounted to making a list of transactions with pen and paper). Next, the limitation of transforming the first matrix structure into a second matrix structure by performing a block compression based on similarity between the reference values and the features values in the first matrix structure encompasses a mental process and/or mathematical concept. That is, performing a block compression based on similarity between the reference values and the feature values then clustering components in which feature values are the same as reference values encompasses a mathematical relationship (e.g., compare feature value A to reference value B, determine whether A equals B, and group A with B when they are equal) or a mathematical calculation (e.g., calculation of a similarity score). Additionally and/or alternatively, clustering components of questions in which feature values are the same as reference values may be performed as a mental process, wherein a human can mentally identify two or more questions as matching and group those questions together (i.e., look at the values, compare them, and organize the corresponding matching questions together). Furthermore, acquiring input data from the second matrix, inputting the acquired input data into a trained artificial neural network model, acquiring comparison information output through the trained artificial neural network model, and calculating the learning ability score of the target user based on the comparison information encompasses a mental process (i.e., evaluation), mathematical calculation, and/or is directed to the insignificant extra-solution activity of data gathering (“acquiring input data from the second matrix structure”). The recitation of a trained artificial neural network model, which is trained by receiving training data through an input layer and repeatedly performing an operation of adjusting parameters of at least one node included in the artificial neural network model so that output data output through an output later approaches label information indicating a relative skill between users, is recited at a high level of generality such that it amounts to using a computer with a generic artificial neural network to apply the abstract idea(s). The limitations merely recite the outcomes of “acquiring comparison information output through the trained artificial neural network model, and calculating the learning ability score of the target user based on this comparison information” without any details about how the outcomes are accomplished (see USPTO 2024 Guidance Update on Patent Subject Matter Eligibility, Including on Artificial Intelligence, Example 47 (July 2024)). Moreover, the recited training of the artificial neural network model encompasses a mathematical concept (i.e., adjusting parameters based on a difference between the generated output and the target label (optimization)), wherein the training is performed by conventional steps. The label information indicating a relative skill between users identifies what the mathematical model is trained to predict, and the training data including response comparison information between a first user and a second user merely applies the artificial neural network technology to a particular data set, but does not change/improve the underlying technology. The limitations recite the trained artificial neural network model to be used as a tool to process particular information, as opposed to an improvement to the artificial neural network model itself.
There is no indication that the limitations improve computer functionality or other technology (MPEP 2106.05(a)), apply the abstract idea(s) with a particular machine (MPEP 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and/or add meaningful limitations beyond generally linking the use of the abstract idea(s) to a particular technological environment (MPEP 2106.05(e)). Accordingly, the amended limitations are directed to the abstract idea(s), and the claim does not recite additional limitations to integrate the abstract idea(s) into a practical application or provide significantly more. Therefore, the claims remain rejected under 35 U.S.C. 101, as presented in detail below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 4-8, 11-14, 16, and 18 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea(s) without significantly more.
Regarding claim 1, analyzed as representative claim:
[Step 1] Claim 1 recites “A method”, which falls within the “process” statutory category of invention.
[Step 2A – Prong 1] The claim recites a series of steps which can be practically performed by one or more humans through mental process (i.e., observation, evaluation, judgment, and/or opinion) (see MPEP 2106.04(a)(2)(III)), certain methods of organizing human activity (i.e., managing personal behavior or relationships or interactions between people – including social activities, teaching, and following rules or instructions) (see MPEP 2106.04(a)(2)(II)), and/or mathematical concepts (i.e., mathematical relationships, mathematical formulas or equations, mathematical calculations) (see MPEP 2106.04(a)(2)(I)).
Claim 1 recites: A method of recommending educational content by a device for analyzing search information of a target user, the method comprising:
acquiring the search information of the target user (mental process: observation/evaluation; human activity: interactions between two individuals, e.g., teaching);
extracting searched question information based on the search information (mental process: observation/evaluation);
acquiring a solution content set related to the question information, the solution content set including first solution information and second solution information (mental process: observation/evaluation; human activity: interactions between two individuals, e.g., teaching);
calculating learning ability information of the target user by using an artificial neural network model including an input layer that receives data, an output layer that outputs a result, and a hidden layer that processes data between the input layer and the output layer, based on the search information, wherein the artificial neural network model is an artificial neural network model trained by receiving training data through the input layer and repeatedly performing an operation of adjusting parameters of at least one node included in the artificial neural network model so that output data output through the output layer approaches label information, wherein the label information indicates a relative skill between users (mental process: evaluation/judgment; and/or mathematical concept: calculation);
calculating an index related to an expected educational effect based on the learning ability information and the solution content set (mental process: evaluation/judgment; mathematical concept: calculation);
selecting target solution content from the solution content set based on the index (mental process: evaluation/judgment/opinion); and
transmitting the target solution content to the target user (human activity: interactions between two individuals, e.g., teaching),
wherein the calculating of the learning ability information of the target user comprises: acquiring learning set information based on the question information and log data included in the search information, the learning set information including a plurality of questions, wherein the log data includes searched time data and reading time data of a search result (mental process: observation/evaluation; human activity: interactions between two individuals, e.g., teaching; and/or insignificant extra-solution activity (data gathering)),
acquiring a search database of a plurality of users based on the learning set information, wherein the search database of the plurality of users includes identification information of each of the plurality of users and a reference value allocated to each of the plurality of questions according to whether each of the plurality of questions was searched by a user of the plurality of users (insignificant extra-solution activity (data gathering)),
allocating a feature value to each of the plurality of questions for the target user according to whether the target user searched for each of the plurality of questions (mental process: observation/evaluation),
generating a first matrix structure including reference values allocated to the plurality of questions for the plurality of users and feature values allocated to the plurality of questions for the target user, wherein the first matrix structure includes user identification information of the target user and the plurality of users as rows and question identification information of the plurality of questions as columns and includes (i) the feature values allocated to the plurality of questions for the target user and (ii) the reference values allocated to the plurality of questions for the plurality of users as components of the first matrix structure (mental process: evaluation/judgment),
transforming the first matrix structure into a second matrix structure by performing a block compression on the first matrix structure based on similarity between the reference values and the feature values included in the first matrix structure, wherein the performing of the block compression comprises clustering components of questions in the first matrix structure in which feature values allocated to the questions for the target user are the same as reference values allocated to the questions for users among the plurality of users by the block compression to generate the second matrix structure including the clustered components (mental process: evaluation/judgment; and/or mathematical concept: mathematical calculation and/or relationship),
acquiring input data from the second matrix structure, wherein the input data is acquired based on (i) the search information of the target user and (ii) search information included in the search database (mental process: evaluation; and/or insignificant extra-solution activity (data gathering)), and
calculating a learning ability score of the target user by using the trained artificial neural network model based on the clustered components in the second matrix structure, wherein the calculating of the learning ability score of the target user comprises: (i) inputting the input data acquired from the second matrix structure into the trained artificial neural network model, (ii) acquiring comparison information output through the trained artificial neural network model, and (iii) calculating the learning ability score of the target user based on the comparison information, wherein the comparison information is information indicating a relative skill of the target user with respect to the plurality of users, wherein the training data includes response comparison information between a first and second user, and wherein the response comparison information includes information related to: (i) a number of questions that both the first user and the second user answered correctly, (ii) a number of questions that only the first user answered correctly, (iii) a number of questions that only the second user answered correctly, and (iv) a number of questions for which both the first user and the second user answered incorrectly (mental process: evaluation; mathematical concept: calculation).
Thereby, as indicated above, the claim elements, under their broadest reasonable interpretation, encompass limitations that can be performed by a human in the human mind and/or using pen and paper, as a certain method of organizing human activity, and/or mathematical concepts, but for the recitation of generic computing components (device, artificial neural network model). Therefore, the claim recites an abstract idea(s).
[Step 2A – Prong 2] The claim fails to recite additional limitations to integrate the abstract idea(s) into a practical application. That is, while the claim recites that search information of a user is analyzed “by a device”, the “device” is recited at a high level of generality such that it amounts to no more than mere automation of a manual process. The generic manner in which the device is claimed amounts to no more than instructions to implement the abstract idea(s) in a computer environment, i.e., field of use. Similarly, the claim further recites performing the calculations by using a trained artificial neural network model, wherein the trained artificial neural network model is also recited at a high level of generality. The limitations recite the outcomes of “acquiring comparison information output through the trained artificial neural network model, and calculating the learning ability score of the target user based on this comparison information” without any details about how the outcomes are accomplished. Moreover, the recited training of the artificial neural network model encompasses a mathematical concept (i.e., adjusting parameters based on a difference between the generated output and the target label (optimization)), wherein the training is performed by conventional steps. The recitation of the label information indicating a relative skill between users identifies what the mathematical model is trained to predict, and the training data including response comparison information between a first user and a second user merely applies the artificial neural network model to a particular data set. Accordingly, the limitations recite the trained artificial neural network model to be used as a tool to perform the abstract idea(s) (see MPEP 2106.05(f)), and/or generally links the abstract idea(s) to a particular technological environment (machine learning/neural networks) (see MPEP 2106.05(h)).
Moreover, the limitations of “acquiring learning set information based on the question information and log data included in the search information, the learning set information including a plurality of questions, wherein the log data includes searched time data and reading time data of a search result”, “acquiring a search database of a plurality of users based on the learning set information, wherein the search database of the plurality of users includes identification information of each of the plurality of users and a reference value allocated to each of the plurality of questions according to whether each of the plurality of questions was searched by a user of the plurality of users” and “acquiring input data from the second matrix structure, wherein the input data is acquired based on (i) the search information of the target user and (ii) search information included in the search database” additionally and/or alternatively are directed to mere data gathering recited a high level of generality, and thus encompass insignificant extra-solution activity (see MPEP 2106.05(g)).
Even when viewed as a whole, these additional elements do not integrate the recited abstract idea(s) into a practical application. The claim does not recite an improvement to the functionality of a computer or other technology or technological field (see MPEP 2106.05(a)), apply the abstract idea(s) with a particular machine (see MPEP 2106.05(b)), effect a transformation or reduction of a particular article to a different state or thing (see MPEP 2106.05(c)), and/or add meaningful limitations beyond generally linking the use of the abstract idea(s) to a particular technological environment (see MPEP 2106.05(e)). Thus, the claim is directed to the abstract idea(s).
[Step 2B] As discussed above with respect to integration of the abstract idea(s) into a practical application, the claim does not further include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements of the device and implementation of a trained artificial neural network model amount to no more than mere instructions to apply the abstract idea(s) using generic computing components and also merely indicate a field of use or technological environment in which the abstract idea(s) is performed. Because the Specification describes the additional limitations in general terms, without the need to describe the particulars of such additional elements in order to satisfy 35 U.S.C. 112(a), the additional claim limitations may be broadly but reasonably construed as reciting conventional computing components and techniques (see Specification, Fig. 2, device 1000, and wherein the device is not further defined in the Specification; see further Specification, p. 44, ln. 19-p.45, ln. 8, “Specific examples of the artificial neural network include a convolution neural network, a recurrent neural network, a deep neural network, a generative adversarial network, and the like. In the present specification, the neural network should be interpreted in a comprehensive sense including all of the artificial neural networks described above, other various types of artificial neural networks, and artificial neural networks in a combination thereof, and does not necessarily have to be a deep learning series. In addition, the machine learning model does not necessarily have to be in the form of the artificial neural network model, and in addition, there may be k- nearest neighbor algorithm (KNN), random forest, support vector machine (SVM), principal component analysis (PCA), etc. Alternatively, the above-described techniques may include an ensemble form or a form in which various other methods are combined. On the other hand, it is stated in advance that the artificial neural network can be replaced with another machine learning model unless otherwise specified in the embodiments mainly described with the artificial neural network.”). Moreover, the “acquiring learning set information”, “acquiring a search database”, and “acquiring input data from the second matrix structure” limitations are recited at a high level of generality and are additionally and/or alternatively directed to the insignificant extra-solution activity of data gathering, which does not provide significantly more to the abstract idea(s). Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea(s) on a computer, link the abstract idea(s) to a particular technological environment, and insignificant extra-solution activity, which do not amount to significantly more than the abstract idea(s) (i.e., provide an inventive concept). Therefore, claim 1 is not patent eligible.
Independent claim 7 recites a non-transitory computer-readable recording medium in which a computer program executed by a computer is recorded, the computer program comprising the limitations recited above, while independent claim 8 recites the additional limitations of a device comprising a transceiver for communicating with an external user terminal, and a controller for performing the limitations above. These additional limitations are recited at a high level of generality such that they do not amount to a particular machine or technical improvement thereof, nor do they represent an improvement in any other technology. Rather, the generic manner in which these additional elements are claimed amounts to mere instructions to implement the abstract idea(s) in a computer environment, i.e., field of use. The claimed use of a device to receive and communicate/transmit information is recited at a high level of generality with no details whatsoever as to how this function is achieved, and thus amounts to the insignificant extra-solution activity of data gathering and data transmission/display. Thus, the additional limitations do not integrate the abstract idea(s) into a practical application or provide significantly more (i.e., an inventive concept).
Furthermore, the Specification further demonstrates that the additional elements are recited for their well-understood, routine, and conventional functionality, and which refers to elements of the device in a manner that indicates that the additional elements are sufficiently well-known that the Specification does not need to describe the particulars of such additional elements to satisfy enablement (see Specification, p. 19, ln. 14-p. 20, ln. 8, noting that “The transceiver may largely include a wired type and a wireless type. [….] Herein, the case of a wireless type, a wireless local area network (WLAN)-based communication method such as Wi-Fi may be mainly used. Alternatively in the case of the wireless type, cellular communication […] may be used. However, the wireless communication protocol is not limited to the above-described example, and any suitable wireless type communication method may be used. In the case of the wired type, local area network (LAN) or universal serial bus (USB) communication is a representative example, and other methods are also possible.” (emphases added); see further Specification, p. 20, ln. 24-p. 21, ln. 12, “The controller 1300 may be implemented as an application processor (AP), a central processing unit (CPU), or a device similar thereto according to hardware, software, or a combination thereof.”). Thus, the additional elements define the field of use as a computer-implemented environment with the components listed above, amounting to merely automating a manual process. Thereby, claims 7-8 are also not patent eligible.
Claims 4-6, 11-14, 16, and 18 are dependent on claims 1 and 7-8, and therefore recite the same abstract idea(s) noted above. While the dependent claims may have a narrower scope than the independent claims, the claims fail to recite additional limitations that would integrate the abstract idea(s) into a practical application or provide significantly more (i.e., an inventive concept). Rather, the claims further define the acquired data and/or mental processes (e.g., selecting the target solution content by comparing indexes representative of acquired data regarding expected educational effect, and allocating the feature values based on observed/evaluated or gathered data). Therefore, claims 4-6, 11-14, 16, and 18 are also not patent eligible.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ALYSSA N BIANCAMANO/Examiner, Art Unit 3715
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