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 .
Application Status
Present office action is in response to the amendment filed 11/21/2025. Claims 1, 7 and 10-17 are amended. Claims 1-17 are currently pending in the application.
Claim Objections
Claims 1-17 are objected to because of the following informalities: The recitation of “the concept-specific understanding level” in claims 1 and 9, lines 18-19 and claim 10, lines 17-18, lack antecedent basis.
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 1-17 are rejected under 35 U.S.C. 101 because the claimed invention is directed to abstract idea without significantly more.
Step 1: Statutory Category?
Independent claims 1, 9 and 10 respectively recite “a method …” (i.e. a process), “a non-transitory computer-readable storage medium …” (i.e., a “manufacture”), and “a system …” (i.e., a “machine”). As such, independent claims 1, 9 and 10 are each directed to a statutory category of invention within § 101, i.e., process, manufacture, and machine. (Step 1: YES).
Step 2A – Prong 1: Judicial Exception Recited?
Independent claim 1, analyzed as representative of the claimed subject matter, is reproduced below. The limitations determined to be abstract ideas are shown in italics. The additional element(s) recited at a high level of generality are shown in bold. The limitation(s) determined to be extra-solution activity are underlined.
A method performed in a system for determining remedial problems provided to a user, the system comprising one or more processors and the method comprising the steps of:
[L1] by the one or more processors, determining, with reference to information on the user's understanding level for at least one concept, problems included in a remedial problem set for improving the user's understanding level; and
[L2] by the one or more processors, updating the problems included in the remedial problem set in response to a change in the user's understanding level for the at least one concept,
[L3] wherein the user's understanding level for the at least one concept is estimated using a concept-specific understanding level estimation model that is trained on the basis of concept-specific correctness/incorrectness sequence data,
[L4] wherein the concept-specific correctness/incorrectness sequence data is generated with respect to at least one user, with reference to data on a result of the at least one user solving at least one problem associated with at least one concept,
[L5] wherein the concept-specific correctness/incorrectness sequence data includes first sequence data generated at a first time point and second sequence data generated at a second time point that follows the first time point by a predetermined amount of time, and
[L6] wherein the concept-specific understanding level estimation model is trained such that the concept-specific understanding level is estimated by assigning a greater weight to the second sequence data than to the first sequence data.
The following limitations describe steps that could be performed by a teacher, as part of teaching activities to: ([L1]) determine based on a user's understanding level for at least one concept, problems included in a remedial problem set for improving the user's understanding level (observations, evaluations); ([L2]) update the problems included in the remedial problem set in response to a change in the user's understanding level for the at least one concept (observations, evaluations, and judgments); ([L3]) estimate the user's understanding level for the at least one concept (observations, evaluations, and judgments); ([L4]) generate the concept-specific correctness/incorrectness sequence data with respect to at least one user with reference to data on a result of the at least one user solving at least one problem associated with at least one concept (observations, evaluations, and judgments); ([L5]) generate the concept-specific correctness/incorrectness sequence data includes first sequence data at a first time point and generate second sequence data at a second time point that follows the first time point by a predetermined amount of time (observations, evaluations, and judgments); ([L6]) estimate the concept-specific understanding level by assigning a greater weight to the second sequence data than to the first sequence data (observations, evaluations, and judgments). The instant specification, as published, acknowledges “[W]ith the advancement of computer-related technology, attempts have been made to apply various computer-related techniques to education-related fields” (¶ 2), that “[I]n order to enhance the learning effectiveness of a user (e.g., elementary school student, middle school student, or high school student), intensive learning may be necessary for concepts in which the user is weak (e.g., concepts for which the user's understanding levels are low compared to other concepts)” (¶ 3) and that “if the user is not aware of what concepts the user is weak in, repetitive learning of such concepts cannot be carried out, resulting in a decrease in learning efficiency” (¶ 4). Thus, other than reciting the “one or more processors” and “concept-specific understanding level estimation model” additional non-abstract elements in representative independent claim 1 above, under the broadest reasonable interpretation, at least the italicized claim limitations may be performed in the human mind, including observations, evaluations, and judgments and may also be characterized as a certain method of organizing human activity, i.e., managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions). Accordingly, the claim recites an abstract idea under Step 2A: Prong 1. (Step 2A – Prong 1: YES).
Step 2A – Prong 2: Integrated into a Practical Application?
The computer component(s), namely the “one or more processors” and “concept-specific understanding level estimation model” are recited at a high level of generality (see published Specification, at least ¶ 28: the device 300 according to one embodiment of the invention is digital equipment capable of connecting to and then communicating with the remedial problem management system 200, any type of digital equipment having a memory means and a microprocessor for computing capabilities, such as a smart phone, a tablet, a smart watch, a smart band, smart glasses, a desktop computer, a notebook computer, a workstation, a personal digital assistant (PDA), a web pad, and a mobile phone, may be adopted as the device 300 according to the invention; ¶ 29: … at least a part of the application may be replaced with a hardware device or a firmware device that may perform a substantially equal or equivalent function, as necessary; ¶ 32: The program modules may be included in the remedial problem management system 200 in the form of operating systems, application program modules, and other program modules, while they may be physically stored in a variety of commonly known storage device…; ¶ 40: using a concept-specific understanding level estimation model that is trained on the basis of the concept-specific correctness/incorrectness sequence data; ¶ 41: Here, the concept-specific understanding level estimation model according to one embodiment of the invention may be trained using a Bayesian knowledge tracing algorithm …; ¶ 48: … analyzing the user's behavior associated with learning using a behavior analysis model; ¶ 49: … the behavior analysis model according to one embodiment of the invention may include a machine learning model or an artificial intelligence model capable of deriving an analysis result in response to the user's behavior associated with learning; ¶ 51: As another example, the behavior analysis model according to one embodiment of the invention may be further in conjunction with a generative artificial trained intelligence model (e.g., ChatGPT); ¶ 62: using computer technology (e.g., technology related to artificial intelligence models …; ¶ 93: For example, the concept-specific understanding level estimation model according to one embodiment of the invention may be trained using a Bayesian knowledge tracing algorithm …; ¶¶ 100, 101: conventional Bayesian knowledge tracing algorithm…; ¶ 135: the invention as described above may be implemented in the form of program instructions that can be executed by various computer components, and may be stored on a computer-readable recording medium. The lack of details about the “one or more processors” and “concept-specific understanding level estimation model” indicates that the additional element(s) is/are generic, or part of generic computer elements performing or being used in performing the generic functions claimed. The claim does not change the way in which each of the recited “one or more processors” and “concept-specific understanding level estimation model” performs its tasks, it simply uses each component for its ordinary purpose to carry out the abstract idea of determining remedial problems provided to a user. The claim does not recite (i) an improvement to the functionality of a computer or other technology or technical field (see MPEP § 2106.05(a)); (ii) a “particular machine” to apply or use the judicial exception (see MPEP § 2106.05(b)); (iii) a particular transformation of an article to a different thing or state (see MPEP § 2106.05(c)); or (iv) any other meaningful limitation (see MPEP § 2106.05(e)). See 84 Fed. Reg. at 55. The claimed invention merely implements the abstract idea using instructions executed on generic computer components, as shown in bold above, and as supported in the above noted pertinent portions of the Specification (¶¶ 28, 29, 32, 40, 41, 48, 49, 51, 62, 93, 100, 101, 135). The instant claim merely uses a programmed computer as a tool to perform an abstract idea. See MPEP § 2106.05(f). The additional limitations [L3] (“a concept-specific understanding level estimation model that is trained on the basis of concept-specific correctness/incorrectness sequence data”, i.e., data gathering), and [L6] (“the concept-specific understanding level estimation model is trained such that the concept-specific understanding level is estimated”, i.e., data gathering) reflect the type of extra-solution activity (i.e., in addition to the judicial exception) the courts have determined insufficient to transform judicially excepted subject matter into a patent-eligible application when they are claimed in a merely generic manner. See MPEP § 2106.05(g); see In re Bilski, 545 F.3d at 963 (characterizing data gathering steps as insignificant extra-solution activity); see also Content Extraction & Transmission LLC v. Wells Fargo Bank, Nat’l Ass’n, 776 F.3d 1343, 1347 (Fed. Cir. 2014) (determining that claims drawn to collecting data, recognizing certain data within the collected set, and storing the recognized data were patent ineligible, noting that “humans have always performed these functions”); see also WhitServe LLC v. Dropbox, Inc., 854 F. App'x 367, 372 (Fed. Cir. 2021), cert. denied, 142 S. Ct. 778, 211 L. Ed. 2d 486 (2022) (maintaining data records by updating and deleting data is an abstract idea); Versata Dev. Grp., Inc. v. SAP Am., Inc., 793 F.3d 1306, 1333–34 (Fed. Cir. 2015) (using organizing and group hierarchies in determinations “is a building block, a basic conceptual framework for organizing information”). The representative claim as a whole merely uses computer instructions to implement the abstract idea on a computer or, alternatively, merely uses a computer as a tool to perform the abstract idea. The claim limitations amount to merely indicating a field of use or technological environment (a computer) in which to apply a judicial exception and, as such, cannot integrate the judicial exception into a practical application. See MPEP § 2106.05(h). Hence, as per MPEP §§ 2106.05(a)–(c), (e)–(h), the additional elements in claim 1, namely the “one or more processors” and “concept-specific understanding level estimation model” do not, either individually or in combination, integrate the abstract idea into a practical application. Because the abstract idea is not integrated into a practical application, the claim is directed to the judicial exception. (Step 2A, Prong 2: NO).
Step 2B: Claim provides an Inventive Concept?
As discussed with respect to Step 2A Prong Two, the additional elements in the claim amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in Step 2B, i.e., mere instructions to apply an exception using generic computer components cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. Because the published Specification, as noted above (¶¶ 28, 29, 32, 40, 41, 48, 49, 51, 62, 93, 100, 101, 135) describes the “one or more processors” and “concept-specific understanding level estimation model” in general terms, without describing the particulars, the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of the “one or more processors” and “concept-specific understanding level estimation model” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. In particular, each of the recited data gathering limitations: [L3] “a concept-specific understanding level estimation model that is trained on the basis of concept-specific correctness/incorrectness sequence data”, and [L6] (“the concept-specific understanding level estimation model is trained such that the concept-specific understanding level is estimated” is nothing more than well-understood, routine, and conventional activity because these limitations are not distinguished from generic, conventional data gathering with a computer. Considered as an ordered combination, the computer components of representative independent claim 1 add nothing that is not already present when the steps are considered separately. The sequence of determining, updating, estimating, training, generating, generating, training and estimating is equally generic and conventional. See In re Bilski, 545 F.3d 943, 963 (Fed. Cir. 2008) (en banc), aff’d Bilski v. Kappos, 561 U.S. 593 (2010) (characterizing data gathering steps as insignificant extra-solution activity). Hence, the additional element(s) is/are generic, well-known, and conventional computing element(s). The use of the additional element(s) either alone or in combination amounts to no more than mere instructions to apply the judicial exception using generic computer component(s). Mere instructions to apply an exception using generic computer components cannot provide an inventive concept, and thus the claims are patent ineligible. (Step 2B: NO).
In regard to Claim 9:
Claim 9 recites a non-transitory computer-readable recording medium having stored thereon a computer program for executing the method of Claim 1. Accordingly, claim 9 is rejected similarly to representative claim 1.
In regard to independent Claim 10:
Independent claim 10 recites a system for determining remedial problems provided to a user, the system comprising one or more processors configured to perform steps comparable to those of representative claim 1. Accordingly, independent claim 10 is rejected similarly to representative claim 1.
In regard to the dependent claims:
Dependents claims 2-8 and 11-17 include all the limitations of corresponding independent claims 1 and 10 from which they depend and, as such, recite the same abstract idea(s) noted above for corresponding independent claims 1 and 10. The dependent claims do not appear to remedy the issues noted above. Any other additional claim element, for example, “behavior analysis model” (claims 3 and 12) is recited as being used according to its conventional purpose in a conventional manner (¶¶ 49, 51). The Examiner fails to see any claim activity used in some unconventional manner nor does any produce some unexpected result. An invocation to use known technology in the manner it is intended to be used for its ordinary purpose is both generic and conventional. As per MPEP §§ 2106.05(a)–(c), (e)–(h), none of the limitations of claims 2-8 and 11-17 integrates the judicial exception into a practical application. While dependent claims 2-8 and 11-17 may have a narrower scope than the representative claims, no claim contains an “inventive concept” that transforms the corresponding claim into a patent-eligible application of the otherwise ineligible abstract idea(s). Therefore, dependent claims 2-8 and 11-17 are not drawn to patent eligible subject matter as they are directed to (an) abstract idea(s) without significantly more.
Response to Arguments
Rejections under 35 U. S. C. § 101
Applicant first argues that “[T]he claimed subject matter is not directed to (b) certain methods of organizing human activity” and that “[T]he claimed subject matter is not directed to (c) mental processes such as concepts performed in the human mind”. In support of the above arguments, Applicant references the Office’s Hypothetical Example 39 asserting that "the claim does not recite a mental process because the steps are not practically performed in the human mind.", and that “it is highly impractical for a human being to perform all of the steps recited in claim 1”, reciting portions of the amended representative claim in support of these assertions. Applicant’s remarks are not persuasive because simply reciting claim language in support of arguments does not constitute evidence of Applicant’s arguments. See Invitrogen Corp. v. Clontech Labs., Inc., 429 F.3d 1052, 1068 (Fed. Cir. 2005) ("Unsubstantiated attorney argument regarding the meaning of technical evidence is no substitute for competent, substantiated expert testimony."). By definition, “teaching is the practice implemented by a teacher aimed at transmitting skills (knowledge, know-how, and interpersonal skills) to a learner, a student, or any other audience of an educational institution”1. Additionally, teachers have long used desired strategies to assess students and provide content to the students based on assessment results. Hence, the teacher “improving the user's understanding level” can be achieved without using a computer and, as such, is no more than an improvement to an abstract idea.
Furthermore, the claimed invention and the hypothetical claim in Example 39 of the USPTO’s 2019 “Subject Matter Eligibility Examples: Abstract Ideas” (the “Eligibility Examples”) are distinguishable. In particular, other than restating the claim language, Applicant does not explain why the claimed one or more processors and concept-specific understanding level estimation model that is trained as claimed provide an improvement to a technical field. Each of the one or more processors and concept-specific understanding level estimation model is described at a high level of generality and in a generic way in the Specification indicating that it is a well-known component or algorithm that can be used to implement the claimed method performed in a system for determining remedial problems provided to a user. For example, the concept-specific understanding level estimation model according to one embodiment of the invention may be trained using a Bayesian knowledge tracing algorithm (¶ 93: For example, the concept-specific understanding level estimation model according to one embodiment of the invention may be trained using a Bayesian knowledge tracing algorithm; ¶¶ 100, 101: conventional Bayesian knowledge tracing algorithm). In other words, the concept-specific understanding level estimation model … may be trained using an artificial intelligence algorithm. However, the Specification does not describe any inventive artificial intelligence algorithm, any inventive way of training an artificial intelligence algorithm, or any technical details thereof such that the recited artificial intelligence algorithm would integrate the abstract idea into a practical application. The claimed invention, as per the Specification, simply uses generic artificial intelligence algorithm to determine remedial problems provided to a user. Applying generic artificial intelligence to a new field of use does not create patent eligibility. As a whole, the additional elements are generic computers and components used as tools to perform the abstract idea without improving computers or other technologies. They are not integral to the claim or arranged in any particular, non-conventional arrangement to collect and process data. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212–14 (Fed. Cir. 2025) (applying generic, conventional machine learning to a new field of use or technological environment did not make the abstract idea patent eligible where neither the claims nor the specification described how improvements are accomplished; using existing machine learning technology to perform tasks with greater speed and efficiency than could be achieved by humans previously is not inventive).
Moreover, in Example 39, the USPTO’s supplemental guidance explains that a claim does not recite a mental process when the recited steps cannot practicably be performed in the human mind. The USPTO describes in the “Background” section of Example 39 that prior methods for identifying human faces in digital images use neural networks to classify images as either containing a human face or not based on the model being previously trained on a set of facial and non-facial images. However, prior methods suffered from an inability to detect human faces in images having shifts, distortions, and variations in scale and rotation of the face pattern. Id. To address this problem, the hypothetical claim uses a combination of features, where the first feature uses an expanded training set of facial images to train the neural network. Id. Specifically, the claim applies mathematical transformation functions to an acquired set of facial images, thereby introducing shifts, distortions, and variations in scale and rotation of the face pattern in each image, to develop an expanded training set of facial images, and trains the neural network using the expanded training set. Id. Training the neural network using this expanded set improves detecting faces in distorted images; however, it also increases the number of false positives produced by performing face detection on non-facial images. The combination of features allows the claimed method of Example 39 to train a neural network for facial detection to detect faces in distorted images and limit the number of false positives. Id. The hypothetical claim of Example 39, then, recites steps of (1) collecting digital facial images; (2) applying transformations to each digital facial image; (3) creating a first training set; (3) training the neural network in a first stage; and (4) training the neural networks in a second stage. The USPTO determined that the hypothetical claim in Example 39 does not recite (1) any mathematical relationships, formulas, or calculations; (2) mental processes; and (3) methods of organizing human activity. Id. at 9.
Therefore, despite Example 39’s hypothetical claim involving training a neural network, it nevertheless differs from the present claims in significant respects. The claim in Example 39 transforms image data into modified image data that is then used to train the neural network. To the extent Applicant contends that the claimed invention here involves such a transformation, let alone using modified image data resulting from that transformation to train the network as in Example 39, there is no persuasive evidence on this record to substantiate such a contention. Rather, as shown in the rejections above, the claimed invention here can be performed entirely mentally or using pen and paper.
Applicant then asserts that “Even if, arguendo, the instant claims recite a judicial exception under prong one of Step 2A (which Applicant refutes), the claims nevertheless are integrated into a practical application”. In support of this assertion, Applicant additionally remarks “the claimed subject matter includes an additional element that reflects an improvement in the functioning of a computer, or an improvement to other technology or technical field” and that “[T]he claimed subject matter is directed to determining a remedial problem set for improving the user's understanding level”. In support of the above, Applicant references the concept-specific understanding level estimation model in restating associated claim language. Applicant’s arguments have been fully considered but they are not persuasive. As noted earlier, each of the one or more processors and concept-specific understanding level estimation model is described at a high level of generality and in a generic way in the Specification (at least ¶¶ 28, 62, 93, 100, 101) indicating that it is a well-known component or algorithm that can be used to implement the claimed method performed in a system for determining remedial problems provided to a user. As a whole, the additional elements are generic computers and components used as tools to perform the abstract idea without improving computers or other technologies. They are not integral to the claim or arranged in any particular, non-conventional arrangement to collect and process data. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1212–14 (Fed. Cir. 2025) (applying generic, conventional machine learning to a new field of use or technological environment did not make the abstract idea patent eligible where neither the claims nor the specification described how improvements are accomplished; using existing machine learning technology to perform tasks with greater speed and efficiency than could be achieved by humans previously is not inventive).
Applicant’s Berkheimer arguments have been fully considered but they are not persuasive. As noted in the rejections above, because the instant Specification, describes the “one or more processors” and “concept-specific understanding level estimation model” in general terms, without describing the particulars (¶¶ 28, 29, 32, 40, 41, 48, 49, 51, 62, 93, 100, 101, 135), the claim limitations may be broadly but reasonably construed as reciting conventional computer components and techniques, particularly in light of the published Specification sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a). See MPEP 2106.05(d), as modified by the USPTO Berkheimer Memorandum. Furthermore, the Berkheimer Memorandum, Section III (A)(1) explains that a specification that describes additional element(s) “in a manner that indicates that the additional element(s) is/are sufficiently well-known that the specification does not need to describe the particulars of such additional element(s) to satisfy 35 U.S.C. § 112(a)” can show that the elements are well understood, routine, and conventional); Intellectual Ventures I LLC v. Erie Indem. Co., 850 F.3d 1315, 1331 (Fed. Cir. 2017) (“The claimed mobile interface is so lacking in implementation details that it amounts to merely a generic component (software, hardware, or firmware) that permits the performance of the abstract idea, i.e., to retrieve the user-specific resources.” The generic description of the “one or more processors” and “concept-specific understanding level estimation model” indicates the steps are well-known enough that no further description is required for a skilled artisan to understand the process and that these computer components are all used in a manner that is well-understood, routine, and conventional in the field. As previously noted, the concept-specific understanding level estimation model is described at a high level and in a generic way in the Specification indicating that it is a well-known component or algorithm that can be used to implement the claimed method performed in a system for determining remedial problems provided to a user. Here, the concept-specific understanding level estimation model according to one embodiment of the invention may be trained using a Bayesian knowledge tracing algorithm: ¶ 93: For example, the concept-specific understanding level estimation model according to one embodiment of the invention may be trained using a Bayesian knowledge tracing algorithm; ¶¶ 100, 101: conventional Bayesian knowledge tracing algorithm. In other words, the concept-specific understanding level estimation model … may be trained using an artificial intelligence algorithm. However, the Specification does not describe any inventive artificial intelligence algorithm, any inventive way of training an artificial intelligence algorithm, or any technical details thereof such that the recited artificial intelligence algorithm would integrate the abstract idea into a practical application. The claimed invention, as per the Specification, simply uses generic artificial intelligence algorithm to determine remedial problems provided to a user. Applying generic artificial intelligence to a new field of use does not create patent eligibility. See Recentive Analytics, Inc. v. Fox Corp., 134 F.4th 1205, 1213 (Fed. Cir. 2025) (“We see no merit to Recentive’s argument that its patents are eligible because they apply machine learning to this new field of use.”).
In view of the foregoing, the Examiner maintains that each of Applicant’s claims
1-17, considered as a whole, is directed to a patent-ineligible abstract idea that is not
integrated into a practical application, and does not include an inventive concept. The
claims remain rejected under 35 U.S.C. 101 as being directed to non-statutory subject
matter.
Rejections under 35 U.S.C. §§ 102 and 103
The prior art rejections of the claims are withdrawn in view of Applicant’s amendment.
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.
The prior art made of record and not relied upon is listed in the attached PTO
Form 892 and is considered pertinent to applicant's disclosure.
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/EDDY SAINT-VIL/Primary Examiner, Art Unit 3715
1 https://en.wikipedia.org/wiki/Teaching#:~:text=Teaching%20is%20the%20practice%20implemented,audience%20of%20an%20educational%20institution.