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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 02/09/26 has been entered.
Response to Arguments
The previous rejections under 35 U.S.C. 112(b) have been withdrawn in light of the amendments to the claims, filed 02/09/26.
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 the amended independent claims recite specific data structures and transformations that cannot be performed mentally (Remarks, filed 02/09/26, pp.15-17). Examiner respectfully disagrees. The recited block compression encompasses the abstract idea(s) of mental process (evaluation/judgment) and/or mathematical concept (i.e., mathematical calculation and/or relationship) (See MPEP 2106.04(a)(2)(I) & (III)). For example, a human can mentally transform a first set of data (first matrix structure) into a second, more compact (clustered) representation (second matrix structure) based on similarity (i.e., known as “chunking” or similarity-based compression). Additionally, and/or alternatively, the block compression encompasses a mathematical concept (i.e., mathematical calculation and/or relationship). Thus, the amended limitations encompass an abstract idea(s).
Applicant further argues that the claims recite additional elements reflecting a particular machine implementation or a technological improvement (Remarks, filed 02/09/26, pp. 17-19). Examiner respectfully disagrees. As noted above, the amended limitations encompass the abstract idea(s) of mental process and/or mathematical concepts. Further, the artificial neural network performs the evaluation that a human would otherwise perform (i.e., mentally and/or using pen and paper). Moreover, the alleged data savings appears to be part of the abstract idea or incidental to the inventive concept, which is evaluating people. The abstract ideas alone cannot contribute to a technological improvement; rather, the improvement must come from the additional elements (see MPEP 2106.05(a)).
Accordingly, the rejection of the claims under 35 U.S.C. 101 has been maintained, 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 1-2, 4-9, and 11-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to 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 2] 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 (mental process: evaluation/judgment; 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 (mental process: observation/evaluation; human activity: interactions between two individuals, e.g., teaching),
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,
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; and/or mathematical concept: mathematical calculation and/or relationship),
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), and
calculating a learning ability score of the target user by using the artificial neural network model based on the clustered components in the second matrix structure, the artificial neural network model being a 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 neural network model (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 pencil 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 an artificial neural network model, wherein the artificial neural network model is also recited at a high level of generality. The Specification provides supporting exemplary, non-limiting descriptions of the generic artificial neural network model (see 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.”). The lack of details about the additional element indicates that the above-mentioned additional element is a generic computing component, performing generic functions. The instant claim as a whole merely applies the generic computing component to implement the abstract idea(s), or alternatively uses the generic computing component as a tool to perform the abstract idea(s), and generally links the abstract idea(s) to the particular technological environment of machine learning.
Moreover, the limitation of “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” is directed to mere data gathering recited a high level of generality, and thus encompasses 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 of 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 an 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. Moreover, the “acquiring a search database” imitation is recited at a high level of generality and is 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 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 amount 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 2, 4-6, 9, and 11-17 are dependent on claims 1 and 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). Therefore, claims 2, 4-6, 9, and 11-17 are also not patent eligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ALYSSA N BRANDLEY whose telephone number is (571)272-4280. The examiner can normally be reached M-F: 8:30am-5:00pm.
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/ALYSSA N BRANDLEY/Examiner, Art Unit 3715