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 .
Status of Claims
The office action is in response to arguments and amendments entered on January 2, 2026 for the patent application 17/983,039 originally filled on November 11, 2022. Claims 1-20 are pending. Claims 1, 3-6, 8-13, and 15-20 are amended. The first office action of October 2, 2025 is fully incorporated by reference into this final action.
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 1-20 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 an “information processing system” (i.e., a machine), claim 13 is directed to a “information processing method” (i.e., a process), and claim 17 is directed to a “non-transitory computer-readable storage medium” (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). In other words, Step 1 of the subject-matter eligibility analysis is “Yes.”
The independent claim recite the following limitations:
Per Claim 1:
“An information processing system comprising: a server including a memory and at least one processor; and a plurality of terminal devices communicably connected to the server via a network,
wherein the at least one processor is configured to: periodically aggregate, from the plurality of terminal devices, giving history information on a plurality of questions for each of a plurality of users who use the plurality of terminal devices, and store the aggregated question-giving history information for each user in a database in the memory, the question-giving history information including correctness determination results indicating whether the users have correctly answered given questions that have been given to the users;
represent each user's correctness determination results as a feature vector having a number of dimensions corresponding to a number of questions answered by the user recorded in the user's question-giving history information;
filter the plurality of users to include, with respect to a target user, comparable users whose feature vectors have at least a predetermined number of common dimensions in relation to the target user;
calculate cosine similarities between the feature vector of the target user and the respective feature vectors of the comparable users;
identify, among the comparable users, similar users having similar proficiency tendencies to a target user, based on the calculated cosine similarity;
derive a correct answer probability of the target user correctly answering a question as a deriving target question among the plurality of questions, based on correctness determination results of the similar users who have already answered the deriving target question; and
control transmission of test data to the terminal device of the target user based on the derived correct answer probability.”
Per Claim 13:
“An information processing method that is performed by a server of an information processing system, the information processing system including the server and a plurality of terminal devices communicably connected to the server via a network, the server including a memory and at least one processor, and the method comprising, under control of the at least one processor:
periodically aggregating, from the plurality of terminal devices, question-giving history information on a plurality of questions for each of a plurality of users who use the plurality of terminal devices, and storing the aggregated question-giving history information for each user in a database in the memory, the question- giving history information including correctness determination results indicating whether the users have correctly answered given questions that have been given to the users;
representing each user's correctness determination results as a feature vector having a number of dimensions corresponding to a number of questions answered by the user recorded in the user's question-giving history information;
filtering the plurality of users to include, with respect to a target user, comparable users whose feature vectors have at least a predetermined number of common dimensions in relation to the target user;
calculating cosine similarities between the feature vector of the target user and the respective feature vectors of the comparable users;
identifying, among the comparable users, similar users having similar proficiency tendencies to a target user based on the calculated cosine similarity;
deriving a correct answer probability of the target user correctly answering a question as a deriving target question among the plurality of questions, based on correctness determination results of the similar users who have already answered the deriving target question; and
controlling transmission of test data to the terminal device of the target user based on the derived correct answer probability.”
Per Claim 17:
“A non-transitory computer-readable storage medium storing a program thereon for controlling a server of an information processing system, the information processing system including the server and a plurality of terminal devices communicably connected to the server via a network, the server including a memory and at least one processor, and the program being executable by the at least one processor to control the at least one processor to perform processes comprising:
periodically aggregating, from the plurality of terminal devices, question-giving history information on a plurality of questions for each of a plurality of users who use the plurality of terminal devices, and storing the aggregated question- giving history information for each user in a database in the memory, the question- giving history information including correctness determination results indicating whether the users have correctly answered given questions that have been given to the users;
representing each user's correctness determination results as a feature vector having a number of dimensions corresponding to a number of questions answered by the user recorded in the user's question-giving history information;
filtering the plurality of users to include, with respect to a target user, comparable users whose feature vectors have at least a predetermined number of common dimensions in relation to the target user;
calculating cosine similarities between the feature vector of the target user and the respective feature vectors of the comparable users;
identifying, among the comparable users, similar users having similar proficiency tendencies to a target user, based on the calculated cosine similarity;
deriving a correct answer probability of the target user correctly answering a question as a deriving target question among the plurality of questions, based on correctness determination result of determination results of the similar users who have already answered the deriving target question; and
controlling transmission of test data to the terminal device of the target user based on the derived correct answer probability.”
The non-highlighted sections of the above limitations, as drafted, define a process, that under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components. That is, other than the recitation of a “server”, “memory”, “processor”, “terminal devices”, and “non-transitory computer-readable storage medium”, nothing in the above limitations precludes the step from practically being performed in the human mind. For example, but for the recited language, the limitations above encompass observing the “question giving history” of multiple students; selecting a target student; determining which students have answered the same questions as the target student; determining which students out of the prior selected students have a comparable proficiency to the target students; determining the chances of the student answering a new question correctly; and presenting the question to the student depending on the prior determination.
If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind (including an observation, evaluation, judgment, opinion), then it falls within the “mental processes” grouping of abstract ideas. Hence, the limitations of independent claims 1, 13, and 17 are drawn to an abstract ides of “determining if a target user is likely to solve a question” which falls within the “mental processes” grouping of abstract ideas in terms of concepts performed in the human mind (including an observation, evaluation, judgment, opinion), as per MPEP 2106.04(a)(2) III. In other words, Step 2A, Prong 1 of the subject-matter eligibility analysis is “Yes.”
Furthermore, the Applicant’s claimed elements of a “server”, “memory”, “processor”, “terminal devices”, and “non-transitory computer-readable storage medium” are merely claimed to generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). In other words, the claimed abstract idea, determining if a target user is likely to solve a question, is not providing a practical application, thus Step 2A, Prong 2 of the subject-matter eligibility analysis is “No.”
Furthermore, the claimed “server” (described on page 8, paragraph 4), “memory” (described on page 14, paragraph 3), “processor” (described on page 14, paragraph 2), “terminal devices” (described on page 13, paragraph 7 and page 14, paragraph 1), and “non-transitory computer-readable storage medium” (described on page 14, paragraph 3) are reasonably interpreted as generic hardware and provide no details of anything beyond its use as ubiquitous standard equipment. Therefore, Step 2B, of the subject-matter eligibility analysis is “No.”
Claims 2-12, 14-16, and 18-20 are dependent from independent claims 1, 13, and 17 respectively, and include all the limitations of their respective independent claim. Therefore, the dependent claims recite the same abstract idea. The limitation of the dependent claims fails to amount to significantly more than the judicial exception. For Example:
The limitations of claims 2, 14, and 18 clarify additional information that is provided by the question-giving history and the types of questions that are considered to be deriving target questions. As such, these claims merely further recite the type of data included in the method and are therefore insignificant extra-solution activity. The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than a judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claims 3, 4, 10-12, 15, 16, 19, and 20 clarify which questions are to be output to the user. As such, these claims merely recite the types of data to be displayed to the user. These limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than a judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
The limitations of claim 6-9 simply clarify the methods used to determine which users are similar to the target user. These claims merely provide instructions to implement an abstract idea on a computer per MPEP §2106.05(f). The limitations fail to provide any teaching that integrates the judicial exceptions into a practical application or amounts to significantly more than a judicial exception. For this reason, the analysis performed on the independent claims is also applicable on these claims.
Independent claims 1, 13, and 17 do not provide a practical application and are insufficient to amount to significantly more than the judicial exception. Additionally, dependent claims 2-12, 14-16, and 18-20 recite an abstract idea without significantly more and are not drawn to eligible subject matter. Therefore, claims 1-20 are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject-matter.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-7, 9, 10, 13, 15, 16, 17, 19, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Bardige et al. (Document ID US 8545232 B1; 2013-10-01) in view of Platt et al. (Document ID US 20170372215 A1; 2017-12-28), Nagantani et al. (Document ID US 2020020226 A1; 2020-06-25), and Angelopoulos et al. (Document ID US 20190189025 A1; 2019-06-20).
Bardige et al. teaches:
An information processing system comprising: a server including a memory and at least one processor; and a plurality of terminal devices communicably connected to the server via a network,
wherein the at least one processor is configured to (Col 3:56-67 and Col 5:19-26, show that the system comprises of programs on the student’s machines, i.e. terminal devices, communicating over a network with a server that contains a database): periodically aggregate, from the plurality of terminal devices, giving history information on a plurality of questions for each of a plurality of users who use the plurality of terminal devices, and store the aggregated question-giving history information for each user in a database in the memory, the question-giving history information including correctness determination results indicating whether the users have correctly answered given questions that have been given to the users (Col 3:56-65, shows that the server’s database stores data on each of the students that includes students’ performance on every problem, date/time stamps, and the students learning progress, i.e. question-giving history information and correctness determination results; Col 3:66-67 and col 4:1-8, further show that this data is retrieved from the students’ machines, i.e. terminal devices); derive a correct answer probability of the target user correctly answering a question as a deriving target question among the plurality of questions, based on correctness determination results of the similar users who have already answered the deriving target question; and control transmission of test data to the terminal device of the target user based on the derived correct answer probability (Col. 8:28-45 and col. 9:10-27, show that assignment server 105 selects problems to provide to the user, i.e. the preforming of a specific process, with difficulty levels, that corresponds to the progression and question answer history of the user; Col 6:38-41, shows that the difficult level is a value that is representative of the probability that a user at the same level of progress will answer a problem correctly, i.e. a derived correct answer probability of a question).
Bardige et al. fails to explicitly teach:
represent each user's correctness determination results as a feature vector having a number of dimensions corresponding to a number of questions answered by the user recorded in the user's question-giving history information; filter the plurality of users to include, with respect to a target user, comparable users whose feature vectors have at least a predetermined number of common dimensions in relation to the target user; calculate cosine similarities between the feature vector of the target user and the respective feature vectors of the comparable users; identify, among the comparable users, similar users having similar proficiency tendencies to a target user, based on the calculated cosine similarity;
Platt et al. teaches:
filter the plurality of users to include, with respect to a target user, comparable users whose feature vectors have at least a predetermined number of common dimensions (Platt does not compare the users via vectors that contain the question-giving history information, but users that have answered the same questions, i.e. have at least a predetermined number of common dimensions, are the users that are compared, para. [0063]) in relation to the target user; calculate cosine similarities between the feature vector of the target user and the respective feature vectors of the comparable users; identify, among the comparable users, similar users having similar proficiency tendencies to a target user, based on the calculated cosine similarity (Para. [0063], shows that a cohort of similar users that have a threshold level of similarity with the target user is identified, i.e. the identification of a similar user, based on the questions they have answered and the scores they have received); derive a correct answer probability of the target user correctly answering a question as a deriving target question among the plurality of questions, based on correctness determination results of the similar users who have already answered the deriving target question (Para. [0071]-[0072], shows that the probability of a user correctly answering a question, i.e. the deriving target question, is based on if the cohort of similar users have answered that question correctly, i.e. the correctness determination result).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Platt et al., regarding the prediction of if a user will correctly answer a question based on the probability that peers of a similar skill level have properly answered the aforementioned question, to the similar device of Bardige et al., a system that dynamically assigns problems to users with partial respect to the probability that a user will properly answer a question, to yield the predictable result of yielding a more robust system for question assignment. One of ordinary skill in the art would be motived to incorporate the known techniques of Platt et al. with the similar device of Bardige et al. as the methodology of Platt et al. would allow for an accurate prediction of the “difficulty level” variable as it is defined on column 6, lines 38-41 of Bardige et al.
Nagatani et al. teaches:
Represent each user's correctness determination results as a feature vector having a number of dimensions corresponding to a number of questions answered by the user recorded in the user's question-giving history information (Para. [0085], denotes the saving of question answer information in a vector);
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Nagatani et al., regarding the storage of question answer history information in a vector format, to the similar device of Bardige et al., a system that records question answer history information, to yield the predictable result of yielding a more robust system for question assignment. One of ordinary skill in the art would be motived to incorporate the known techniques of Nagatani et al. with the similar device of Bardige et al. as a vector format take little storage space and there are numerous mathematical calculation that exist for both comparing vectors and preforming transformations on vectors.
Angelopoulos et al. teaches:
Calculate cosine similarities between the feature vector of the target user and the respective feature vectors of the comparable users (Para. [0036]-[0038], show the utilization of cosine similarity to compare one user to all other users to create a cohort of similar users for purposes which may include intervention for education and training; Para. [0012] and [0032], show that the intervention may comprise providing the user with prescriptive recommendations or questions).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Angelopoulos et al., regarding the utilization of cosine similarity to determine a cohort of users that are similar to a target user, to the similar device of Platt et al., a system that predicts of if a user will correctly answer a question based on the probability that a curated cohort of peers similar to the user have properly answered the aforementioned question, to yield the predictable result of yielding a more robust system for cohort identification. One of ordinary skill in the art would be motived to incorporate the known techniques of Angelopoulos et al. with the similar device of Platt et al. as the methodology of Angelopoulos et al. allow for the inclusion of additional user characteristics and attributes when determining which users are similar.
Regarding claim 3, Bardige et al. teaches:
The information processing apparatus according to claim 1, wherein the processor is configured to perform a process of determining, based on the derived correct answer probability, a question to be given to the target user from among the plurality of questions (Col. 8:28-45 and col. 9:10-27, show that assignment server 105 selects problems to provide to the user with difficulty levels that correspond to the progression and question answer history of the user; Col 6:38-41, shows that the difficult level is a value that is representative of the probability that a user at the same level of progress will answer a problem correctly, i.e. a derived correct answer probability of a question).
Regarding claim 4, Bardige et al. teaches:
The information processing apparatus according to claim 1, wherein the processor is configured to perform a process of performing, based on the derived correct answer probability, control including giving or not giving a question to the target user (Col 8: 16-27, shows that answers that a user has about an 80% of solving correctly, i.e. a derived correct answer probability, will be provided to the user; Col. 8:28-45 and col. 9:10-27, further show that assignment server 105 selects problems to provide to the user with difficulty levels that correspond to the progression and question answer history of the user).
Regarding claim 5, Platt et al. teaches:
The information processing apparatus according to claim 1, wherein the processor is configured to treat an average of correctness determination results of determination as to whether the similar users have correctly answered the deriving target question, as the correct answer probability of the target user for the deriving target question (Para. [0071]-[0072], shows that the probability of a user correctly answering a question, i.e. the deriving target question, is based on the percentage of the cohort of similar users that have answered the target question correctly, i.e. the correctness determination result).
Regarding claim 6, Platt et al. teaches:
The information processing apparatus according to claim 1, wherein the processor is configured to identify each similar user using a cosine similarity derived based on (i) a vector of the target user including, as elements, correctness determination results of determination as to whether the target user has correctly answered the given questions and (ii) a vector of each of the users except the target user including, as elements, correctness determination results of determination as to whether each of the users except the target user has correctly answered the given questions (Para. [0063], shows that the system find similar users based on how similarly they answered the same questions as the target user, i.e. based on the element of correctness determination results of determination as to whether the target user has correctly answered the given questions; while Platt teaches finding similar users with similar inputs, it does not teach a utilization of cosine similarity).
Angelopoulos et al. teaches:
The information processing apparatus according to claim 1, wherein the processor is configured to identify each similar user using a cosine similarity derived based on (i) a vector of the target user including, as elements, correctness determination results of determination as to whether the target user has correctly answered the given questions and (ii) a vector of each of the users except the target user including, as elements, correctness determination results of determination as to whether each of the users except the target user has correctly answered the given questions (Para. [0036]-[0038], show the utilization of cosine similarity to compare one user to all other users to create a cohort of similar users for purposes which may include intervention for education and training; Para. [0012] and [0032], show that the intervention may comprise providing the user with prescriptive recommendations or questions).
Regarding claim 7, Platt et al. teaches
The information processing apparatus according to claim 6, wherein the processor is configured to identify, among the users except the target user, a user having the cosine similarity to the target user of a reference value or greater as the similar user (Para. [0063], show that a cohort of users that have a threshold similarity, i.e. a refence value that defines the minimum required similarity value, to the user).
Regarding claim 9, Platt et al. teaches
The information processing apparatus according to claim 1, wherein the processor is configured to identify the similar users based on the question-giving history information and feature information including at least one of an attribute and a characteristic of each of the users (Para. [0063], shows that the system find similar users based on how similarly they answered the same questions as the target user, i.e. based on the question giving history).
Angelopoulos et al. teaches:
The information processing apparatus according to claim 1, wherein the processor is configured to identify the similar users based on the question-giving history information and feature information including at least one of an attribute (page 10, para. 5 of Applicant’s specification defines an attribute as a thing such as gender or age) and a characteristic (page 10, para. 5 of Applicant’s specification defines a characteristic as a thing such as study hours, study time slot, or favorite category of words) of each of the users (Para. [0036]-[0038], shows that matching of users based on their respective behavior profiles, i.e. user feature information; Para. [0032], show that the user behavior profiles contain information such as age, i.e. an attribute, and preferences, i.e. characteristics).
Regarding claim 10, Bardige et al. teaches:
The information processing apparatus according to claim 3, wherein the processor is configured to receive a difficulty level for the question to be given to the target user, and the process of determining includes, based on correct answer probabilities of the target user correctly answering questions as the deriving target question, with the correct answer probabilities each being the derived correct answer probability, determining a question having a correct answer probability corresponding to the difficulty level as the question to be given to the target user (Col 8: 16-27, shows that answers that a user has about an 80% of solving correctly, i.e. a derived correct answer probability, will be provided to the user; Col. 8:28-45 and col. 9:10-27, further show that assignment server 105 selects problems to provide to the user with difficulty levels that correspond to the progression and question answer history of the user).
Regarding claims 13 and 17, they are mirrored claims to claim 1 and are rejected in like manner.
Regarding claims 15 and 19, they are mirrored claims to claim 3 and are rejected in like manner.
Regarding claims 16 and 20, they are mirrored claims to claim 4 and are rejected in like manner.
Claims 2, 11, 12, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Bardige et al. (Document ID US 8545232 B1; 2013-10-01) in view of Platt et al. (Document ID US 20170372215 A1; 2017-12-28), Nagantani et al. (Document ID US 2020020226 A1; 2020-06-25), and Angelopoulos et al. (Document ID US 20190189025 A1; 2019-06-20) and in further view of Willaims et al. (Document ID US 20200302817 A1; 2020-09-24).
Regarding claim 2, Bardige et al. in view of Platt et al. fails to explicitly teach:
The information processing apparatus according to claim 1, wherein the question-giving history information further includes information on whether each of the plurality of questions has been given to each of the users, and wherein the processor is configured to set, among the plurality of questions, at least one of a not-yet-given question that has not been given to the target user and an incorrectly-answered question that the target user has incorrectly answered, as the deriving target question.
Williams et al. explicitly teaches:
The information processing apparatus according to claim 1, wherein the question-giving history information further includes information on whether each of the plurality of questions has been given to each of the users, and wherein the processor is configured to set, among the plurality of questions, at least one of a not-yet-given question that has not been given to the target user and an incorrectly-answered question that the target user has incorrectly answered, as the deriving target question (Para. [0190], shows a system that will both select questions that the user has not seen before and questions that user has previously answered incorrectly).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Williams et al., regarding the utilization of both new questions and questions the user as previously answered incorrectly, to the similar device of Bardige et al., a system that dynamically assigns problems to student to enable efficient learning, to yield the predictable result of yielding a more robust system for question assignment. One of ordinary skill in the art would be motived to incorporate the known techniques of Williams et al. with the similar device of Bardige et al. as the methodology of Williams et al. for quiz question generation would allow for an accurate assessment of a user’s current skill level and in turn better allow the system to assign appropriately difficult questions to the user.
Regarding claim 11, Platt et al. teaches:
The information processing apparatus according to claim 2, wherein the processor is configured to, in response to setting the incorrectly-answered question as the deriving target question, treat an average of correctness determination results of determination as to whether the similar users and the target user have correctly answered the incorrectly-answered question, as the correct answer probability of the target user for the incorrectly-answered question (Para. [0071]-[0072], shows that the probability of a user correctly answering a question, i.e. the deriving target question, is based on the percentage of the cohort of similar users that have answered the target question correctly, i.e. the correctness determination result).
Regarding claim 12, Bardige et al. in view of Platt et al. fails to explicitly teach
The information processing apparatus according to claim 3, wherein the question-giving history information further includes information on whether each of the plurality of questions has been given to each of the users, wherein the processor is configured to: set, among the plurality of questions, at least one of a not-yet-given question that has not been given to the target user and an incorrectly-answered question that the target user has incorrectly answered, as the deriving target question, and receive a proportion of the not-yet-given question and a proportion of the incorrectly-answered question in the question to be given to the target user, and wherein the process of determining includes, based on correct answer probabilities of the target user correctly answering questions as the deriving target question, with the correct answer probabilities each being the derived correct answer probability, determining the question to be given to the target user such that the number of not-yet-given questions and the number of incorrectly-answered questions account for the respective proportions.
Williams et al. teaches:
The information processing apparatus according to claim 3, wherein the question-giving history information further includes information on whether each of the plurality of questions has been given to each of the users, wherein the processor is configured to: set, among the plurality of questions, at least one of a not-yet-given question that has not been given to the target user and an incorrectly-answered question that the target user has incorrectly answered, as the deriving target question, and receive a proportion of the not-yet-given question and a proportion of the incorrectly-answered question in the question to be given to the target user, and wherein the process of determining includes, based on correct answer probabilities of the target user correctly answering questions as the deriving target question, with the correct answer probabilities each being the derived correct answer probability, determining the question to be given to the target user such that the number of not-yet-given questions and the number of incorrectly-answered questions account for the respective proportions (Para. [0190]-[0200], shows a system that will select a set proportion of questions that have not be seen by the user, i.e. not-yet-given questions, in addition to a set proportion of questions that the user has previously answered improperly, i.e. incorrectly-answered questions).
Regarding claims 14 and 18, they are mirrored claims to claim 2 and are rejected in like manner.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Bardige et al. (Document ID US 8545232 B1; 2013-10-01) in view of Platt et al. (Document ID US 20170372215 A1; 2017-12-28), Nagantani et al. (Document ID US 2020020226 A1; 2020-06-25), and Angelopoulos et al. (Document ID US 20190189025 A1; 2019-06-20) and in further view of Carbonell et al. (Document ID US 20140222806 A1; 2014-08-07).
Regarding claim 8, Bardige et al. in view of Platt et al. and Angelopoulos et al. fails to explicitly teach:
The information processing apparatus according to claim 6, wherein the processor is configured to identify, among the users except the target user, a reference number or a reference proportion of users selected in descending order of the cosine similarity as the similar users.
Carbonell et al. teaches:
The information processing apparatus according to claim 6, wherein the processor is configured to identify, among the users except the target user, a reference number or a reference proportion of users selected in descending order of the cosine similarity as the similar users (Para. [0037]-[0039], shows that k number of people, i.e. a reference number of users, may be selected from the top of pre-order compiled reference vector of each user, i.e. the similar users being selected in descending order of cosine similarity).
It would be obvious, before the effective filing date of the claimed invention, for someone of ordinary skill to apply the known techniques of Carbonell et al., regarding the limiting of how many users get added to the cohort and which users get added to the cohort, to the similar device of Platt et al., a system that predicts of if a user will correctly answer a question based on the probability that a curated cohort of peers similar to the user have properly answered the aforementioned question, to yield the predictable result of yielding a more robust system for cohort identification. One of ordinary skill in the art would be motived to incorporate the known techniques of Carbonell et al. with the similar device of Platt et al. as limiting the number of users in the cohort simplifies the amount of processing done by the system and taking the users with the highest cosine similarity metric allows for the best predictions with the available data.
Summary
No claim is allowed
Claims 1-20 are rejected under 35 USC § 101
Claims 1-20 are rejected under 35 USC § 103
Response to Arguments
The Applicants arguments filed on January 2, 2026 related to claims 1-20 are fully considered, but are not fully persuasive.
Rejections under 35 USC § 101
Applicant respectfully argues that the amended claims clearly integrates the “alleged” abstract idea into a practical application (e.g. a system having a server and plural terminal devices connected to the server via a network, which operate cooperatively to output appropriate questions to a given terminal device operated by a target user). Additionally, Applicant respectively argues that the amended independent claims recite a specific technical implementation of a learning support system that achieves concrete improvements over conventional technology.
Examiner respectfully disagrees and does not find this argument persuasive. The amended claims do not integrate the abstract idea into a practical application because the claim limitations do not provide any of the following:
Improvements to the functioning of a computer, or to any other technology or technical field – see MPEP 2106.05(a);
Applying the judicial exception with, or by use of, a particular machine - see MPEP 2106.05(b);
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c); or
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e).
Furthermore, there are also several factors that reasonably explain that the Applicant’s claims are not indicative of significantly more, which include:
Adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f);
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h).
Here, the Applicant’s claims do not contain features that integrate the abstract idea into a practical application as described in the above ordered combination, and Applicant’s claims are merely claimed to use a computer as a tool to perform an abstract idea and to generally link the use of a judicial exception to a particular technological environment or field of use.
The limitations of the amended independent claims define a process, that under its broadest reasonable interpretation, covers performance of the limitation in the human mind but for the recitation of generic computer components. That is, other than the recitation of a “server”, “memory”, “processor”, “terminal devices”, and “non-transitory computer-readable storage medium”, nothing in the amended independent claims precludes the step from practically being performed in the human mind. For example, but for the recited language, the limitations above encompass observing the “question giving history” of multiple students; selecting a target student; determining which students have answered the same questions as the target student; determining which students out of the prior selected students have a comparable proficiency to the target students; determining the chances of the student answering a new question correctly; and presenting the question to the student depending on the prior determination. If a claim limitation, under its broadest reasonable interpretation, covers concepts performed in the human mind (including an observation, evaluation, judgment, opinion), then it falls within the “mental processes” grouping of abstract ideas. Hence, the limitations of independent claims 1, 13, and 17 are drawn to an abstract ides of “determining if a target user is likely to solve a question” which falls within the “mental processes” grouping of abstract ideas in terms of concepts performed in the human mind (including an observation, evaluation, judgment, opinion), as per MPEP 2106.04(a)(2) III.
The Applicant’s claimed elements of a “server”, “memory”, “processor”, “terminal devices”, and “non-transitory computer-readable storage medium” are merely claimed to generally link the use of a judicial exception (e.g., pre-solution activity of data gathering and post-solution activity of presenting data) to (1) a particular technological environment or (2) field of use, per MPEP §2106.05(h); and are applying the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, per MPEP §2106.05(f). In other words, the claimed abstract idea, determining if a target user is likely to solve a question, is not providing a practical application,
Respectfully, Examiner does not find the arguments above persuasive and maintains the rejection under 35 USC § 101.
Rejections under 35 USC § 103
Examiner respectfully agrees that Bardige et al. in view of Platt et al. fail to teach all of the limitations of the amended independent claims. However, in light of a new search, which was necessitated by the Applicant’s amendments, new art has been found that teach the newly added limitations, see the rejections under 35 USC § 103 above.
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 extension fee 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 date of this final action.
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/A.J.B./Examiner, Art Unit 3715
/KANG HU/Supervisory Patent Examiner, Art Unit 3715
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