DETAILED ACTION
This is a FINAL Office Action on the merits and is responsive to the papers filed on 10/23/2025. Claims 1-9 are currently pending and are examined below.
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 Amendments
The amendments filed on 10/23/2025 in response to the initial rejection made on 06/23/2025 have been acknowledged and entered. Claims 1-9 have been amended. Rejections necessitated in response to the amendments made to the claims have been made. Responses to the Applicant’s arguments are written below.
Claim Objections
Claim 6 is objected to because of the following informalities: it is recited in at least line 11 that the “controller is further configure to execute.” The underlined word should read “configured” instead. Appropriate correction is required.
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 1-9 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claim 1 is directed to “a method of recommending educational content” (i.e. a process);
Claim 5 is directed to “a non-transitory computer readable recording medium” (i.e. a machine); and
Claim 6 is directed to “a device for receiving learning data” (i.e. a machine), hence the claims are directed to one of the four statutory categories (i.e. process, machine, manufacture, or composition of matter).
Step 1 of the subject-matter eligibility analysis: Yes.
However, the claims are drawn to an abstract idea of “determining a neural network model based the target learning ability information” either in the form of “certain methods of organizing human activity,” in terms of managing personal behavior or relationships or interactions between people (including social activities, teaching and following rules or instructions), or reasonably in the form of “mental processes,” in terms of processes that can be performed in the human mind (including an observation, evaluation, judgement or opinion) which are “performed on a computer” (per MPEP 2106(III)(C) “A Claim That Requires a Computer May Still Recite a Mental Process”).
The claims are reasonably understood as either “certain methods of organizing human activity” or “mental process.” Independent claim 1, analyzed as the representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are in italics. The additional elements recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
Independent representative claim 1: A method of recommending educational content by a device for analyzing learning data of a user, the device including a memory storing computer-readable instructions and a processor configured to execute the instructions to perform the method comprising:
acquiring the learning data of the user, wherein the learning data includes first learning ability information of the user at a first time point, second learning ability information of the user at a second time point, and question answering information of the user during a time period between the first time point and the second time point;
acquiring target learning ability information of the user using a trained neural network model based on the learning data;
determining a neural network model based the target learning ability information;
monitoring computing resources of the device and distributing the computing resources of the device to correspond to the determined neural network model, wherein the computing resources include a computation amount, a memory or a network of the device; and
acquiring educational content to be recommended to the user through the determined neural network model,
wherein the determining of the neural network model comprises:
determining a first neural network model which demands first computing resources when the target learning ability information of the user includes a first target learning ability value and determining a second neural network model which demands second computing resources greater than the first computing resources when the target learning ability information of the user includes a second target learning ability value lower than the first target learning ability value, and
wherein the distributing of the computing resources comprises:
distributing the first computing resources to the first neural network model based on the first neural network model being determined and distributing the second computing resources to the second neural network model based on the second neural network model being determined, and
wherein the acquiring of the educational content comprises:
acquiring a first educational content through the first neural network model based on the first neural network model being determined and acquiring a second educational content through the second neural network model based on the second neural network model being determined, at least a part of the second educational content being different from the first educational content.
Independent Claim 5: A non-transitory computer-readable recording medium in which a computer program executed by a computer is recorded, the computer program comprising:
acquiring learning data of a user, wherein the learning data includes first learning ability information of the user at a first time point, second learning ability information of the user at a second time point, and question answering information of the user during a time period between the first time point and the second time point;
acquiring target learning ability information of the user using a trained neural network model based on the learning data;
determining a neural network model based on the target learning ability information;
monitoring computing resources of the computer and distributing the computing resources of the computer to correspond to the determined neural network model wherein the computing resources include a computation amount, a memory or a network of the computer; and
acquiring educational content to be recommended to the user through the determined neural network model,
wherein the determining of the neural network model comprises:
determining a first neural network model which demands first computing resources when the target learning ability information of the user includes a first target learning ability value and determining a second neural network model which demands second computing resources greater than the first computing resources when the target learning ability information of the user includes a second target learning ability value lower than the first target learning ability value,
wherein the distributing of the computing resources comprises:
distributing the first computing resources to the first neural network model based on the first neural network model being determined and distributing the second computing resources to the second neural network model based on the second neural network model being determined, and
wherein the acquiring of the educational content comprises:
acquiring a first educational content through the first neural network model based on the first neural network model being determined and acquiring a second educational content through the second neural network model based on the second neural network model being determined, at least a part of the second educational content being different from the first educational content.
Independent Claim 6: A device for receiving learning data of a user from an external user terminal and recommending educational content, the device comprising:
a transceiver configured to communicate with the user terminal;
a memory storing computer-readable instructions; and
a controller configured to execute the instructions to acquire the learning data of the user through the transceiver and determine educational content based on learning data,
wherein the learning data includes first learning ability information of the user at a first time point, second learning ability information of the user at a second time point, and question answering information of the user during a time period between the first time point and the second time point,
wherein the controller is further configure to execute the instructions to:
acquire target learning ability information of the user using a trained neural network model based on the learning data,
determine a neural network model based on the target learning ability information,
monitor computing resources of the device and distribute the computing resources of the device to correspond to the determined neural network model, wherein the computing resources includes a computation amount, a memory or a network of the device, and
acquire educational content to be recommended to the user through the determined neural network model,
wherein the controller is further configure to execute the instructions to:
determine a first neural network model which demands first computing resources when the target learning ability information of the user includes a first target learning ability value and determine a second neural network model which demands second computing resources greater than the first computing resources when the target learning ability information of the user includes a second target learning ability value lower than the first target learning ability value,
wherein the controller is further configure to execute the instructions to:
distribute the first computing resources to the first neural network model based on the first neural network model being determined and distribute the second computing resources to the second neural network model based on the second neural network model being determined, and
wherein the controller is further configure to execute the instructions to:
acquire a first educational content through the first neural network model based on the first neural network model being determined and acquire a second educational content through the second neural network model based on the second neural network model being determined, at least a part of the second educational content being different from the first educational content.
These limitations simply describe a process of data gathering and manipulation, which is partially analogous to “collecting information, analyzing it, and displaying certain results of the collection analysis” (i.e. Electric Power Group, LLC, v. Alstom, 830 F.3d 1350, 119 U.S.P.Q.2d 1739 (Fed. Cir. 2016)). Hence, these limitations are akin to an abstract idea which has been identified among non-limiting examples to be an abstract idea.
Step 2A, Prong 1 of the subject-matter eligibility analysis: Yes.
Furthermore, the claims do not include additional elements that either alone or in combination are sufficient to claim a practical application because to the extent that, e.g., “device,” “memory,” “processor,” “neural network model,” “transceiver,” and “controller” are claimed, as these are merely claimed to add insignificant extra-solution activity to the judicial exception (e.g., data gathering) and/or do no more than generally link the use of a judicial exception to a particular technological environment or field of use. In other words, the claimed “determining a neural network model based the target learning ability information” is not providing a practical application.
Step 2A, Prong 2 of the subject-matter eligibility analysis: No.
Likewise, the claims do not include additional elements that either alone or in combination are sufficient to amount to significantly more than the judicial exception because to the extent that, e.g. “device,” “memory,” “processor,” “neural network model,” “transceiver,” and “controller” are claimed these are all generic, well-known, and conventional computing elements. As evidence that these are generic, well-known, and conventional computing elements, Applicant’s specification discloses them 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 35 U.S.C. § 112(a), per MPEP § 2106.07(a) III (a), which satisfies the Examiner’s evidentiary burden requirement per the Berkheimer memo.
Specifically, the Applicant’s claimed “device,” “memory,” “processor,” “neural network model,” “transceiver,” and “controller” are described in the following paragraphs:
“[0050] The memory 1200 may store various pieces of information. In the memory 1200, various pieces of data may be temporarily or semi-permanently stored. Examples of the memory 1200 include a hard disk driver (HDD), a solid state drive (SSD), a flash memory, a read-only memory (ROM), a random access memory (RAM), etc.”
“[0051] The controller 1300 may be implemented as an application processor (AP), a central processing unit (CPU), or a similar device on the basis of hardware, software, or a combination of hardware and software. As hardware, the controller 1300 may be provided in the form of an electronic circuit for processing an electrical signal to perform a control function.”
“[0048] The educational content recommendation device 1000 may access a network through the transceiver 1100 to transmit and receive various pieces of data. The transceiver 1100 may be a wired type or a wireless type. Since each of the wired type and the wireless type has advantages and disadvantages, both the wired type and the wireless type may be provided in the educational content recommendation device 1000 in some cases. The wireless type may employ a wireless local area network (WLAN)-based communication method such as Wi-Fi. Alternatively, the wireless type may employ cellular communication, for example, Long Term Evolution (LTE) or a fifth generation (5G) communication method.”
“[0051] The controller 1300 may be implemented as an application processor (AP), a central processing unit (CPU), or a similar device on the basis of hardware, software, or a combination of hardware and software.”
This element is reasonably interpreted as a generic computer which provides no details of anything beyond ubiquitous standard equipment or ones that are well known in the art if there isn’t . As such, the claimed limitation of “device,” “memory,” “processor,” “neural network model,” “transceiver,” and “controller” are reasonably understood as not providing anything significantly more. The lack of details indicates that they are generic, or part of generic computer elements performing or being used in performing the generic functions claimed
Step 2B, of the subject-matter eligibility analysis: No.
In addition, dependent claims 2-4, and 7-9, do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. As such, dependent claims 2-4, and 7-9 are also rejected under 35 U.S.C. § 101, based on their respective dependencies to independent claims 1, and 5.
Therefore, claims 1-9 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
Response to Arguments
35 U.S.C. § 112(b):
The amendments made to the claims overcome the rejections made under 35 U.S.C. § 112(b) set forth in the Non-Final Action mailed out on 06/23/2025.
35 U.S.C. § 101:
The applicant states on page 2 of the remarks that to effectively respond to the rejections made under 35 U.S.C. 101 that the Applicant amends the independent claim 1, as well as independent claims 5 and 6 to correspond to amended claim 1. The amendments made to the claims necessitated a further consideration under 35 U.S.C. §101 which is made above.
35 U.S.C. § 103:
The amendments made to the claims overcome the rejections made under 35 U.S.C. § 103 set forth in the Non-Final Action mailed out on 06/23/2025. The prior art does not teach “acquiring a second educational content through the second neural network based on the second neural network model.”
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SELWA A ALSOMAIRY whose telephone number is (703)756-5323. The examiner can normally be reached M-F 7:30AM to 5PM EST.
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/SELWA A ALSOMAIRY/Examiner, Art Unit 3715
/Jay Trent Liddle/Primary Examiner, Art Unit 3715