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 . This action is made non-final.
This action is in response to the application and claims filed March 20, 2024. Claims 1-20 are pending in the case and have been examined. Claims 1-20 are rejected.
Claim Objections
Claim 19 is objected to because of the following informalities: It is dependent on claim 1, but seems to have meant to be dependent from claim 18. 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.
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
To determine if a claim is directed to patent ineligible subject matter, the Court has guided the Office to apply the Alice/Mayo test, which requires:
Step 1: Determining if the claim falls within a statutory category.
Step 2A: Determining if the claim is directed to a patent ineligible judicial exception consisting of a law of nature, a natural phenomenon, or abstract idea; and Step 2A is a two prong inquiry. MPEP 2106.04(II)(A). Under the first prong, examiners evaluate whether a law of nature, natural phenomenon, or abstract idea is set forth or described in the claim. Abstract ideas include mathematical concepts, certain methods of organizing human activity, and mental processes. MPEP 2104.04(a)(2). The second prong is an inquiry into whether the claim integrates a judicial exception into a practical application. MPEP 2106.04(d).
Step 2B: If the claim is directed to a judicial exception, determining if the claim recites limitations or elements that amount to significantly more than the judicial exception. (See MPEP 2106).
Claims 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1: Claims 1-10 are directed to a method (a process), Claims 11-17 is directed to a computer storage media (a manufacture), and Claims 18-20 is directed to a method (a process). Therefore, Claims XXX are directed to a process, machine or manufacture or composition of matter.
Regarding claim 1
Step 2A Prong 1
Claim 1 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “educational system”, “datasets”, “textual content”, “prompt” and “large language model”) [see MPEP 2106.04(a)(2)(III)].
“identifying a plurality of small datasets that contain data pertaining to the student and the peers” (e.g., a human can select relevant information from sources)
“generating one or more dataset queries” (e.g., requesting/retrieving relevant information)
“dynamically generating textual content sections from the data pertaining to the student and the peers” (e.g., organizing and describing information)
“building a prompt that includes the dynamically generated textual content sections and predefined textual content that describes the educational system” (e.g., formatting the information and instructions for analysis)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional elements of “educational system”, “datasets”, “textual content”, “prompt” and “large language model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). The Examiner notes that this is used throughout the claim limitations, and is rejected thusly for each claim which recites the same language.
Regarding the “receiving, from an educator, a request for an analysis of a student, the request identifying the student and a plurality of peers of the student” this additional element is recited at a high level of generality and amounts to extra-solution activity of receiving data, i.e. pre-solution activity of data gathering for use in the claimed process (see MPEP 2106.05(g)).
Regarding the “submitting the prompt to a large language model to cause the large language model to generate the analysis from the prompt” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “presenting the analysis to the educator” this additional element is recited at a high level of generality and amounts to extra-solution activity of presenting data from a model, i.e. post-solution activity of data outputting for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional elements of “educational system”, “datasets”, “textual content”, “prompt” and “large language model” which are recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Regarding the “receiving, from an educator, a request for an analysis of a student, the request identifying the student and a plurality of peers of the student” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of obtaining data to input for a model, i.e., pre-solution activity of data gathering. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Regarding the “submitting the prompt to a large language model to cause the large language model to generate the analysis from the prompt” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Regarding the “presenting the analysis to the educator” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of outputting data from a model, i.e., post-solution activity of data outputting. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 2
Step 2A Prong 1
Claim 2 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein the request specifies a time period and the one or more dataset queries are configured to cause the data pertaining to the student and the peers to be limited to the time period” this additional element is recited at a high level of generality and amounts to extra-solution activity of limiting the data to a specific time length, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein the modifying the policy by automatically changing the at least one current value from the set of current values is done by the agent without an involvement of a user” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of limiting the data to a specific time length, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 3
Step 2A Prong 1
Claim 3 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein the plurality of small datasets are maintained by the educational system” which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein the plurality of small datasets are maintained by the educational system” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 4
Step 2A Prong 1
Claim 4 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein at least one of the small datasets is maintained by another educational system” which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein at least one of the small datasets is maintained by another educational system” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 5
Step 2A Prong 1
Claim 5 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein the plurality of datasets are selected from: an emotion dataset;
a rewards dataset; a parental engagement dataset; a structured assignment dataset; a virtual interaction dataset; or an educator notes dataset” these additional elements are recited at a high level of generality and amounts to extra-solution activity of specifying what types of data sources are to be used, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein the plurality of datasets are selected from: an emotion dataset; a rewards dataset; a parental engagement dataset; a structured assignment dataset; a virtual interaction dataset; or an educator notes dataset” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of limiting the data to a specific data source, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 6
Step 2A Prong 1
Claim 6 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein the plurality of datasets are selected based on functionality that the student’s school uses within the educational system” this additional element is recited at a high level of generality and amounts to extra-solution activity of limiting which datasets to be used based on the schools enabled functionality, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein the plurality of datasets are selected based on functionality that the student’s school uses within the educational system” limitation, this additional element is recited at a high-level of generality and amounts to extra-solution activity of limiting which datasets to be used based on the schools enabled functionality, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 7
Step 2A Prong 1
Claim 7 recites the following mental processes, that in each case under the broadest reasonable interpretation, covers performance of the limitation in the mind (including observation, evaluation, judgement, opinion) or with the aid of pencil and paper but for recitation of generic computer components (e.g., “educational system”, “datasets”, “textual content”, “prompt” and “large language model”) [see MPEP 2106.04(a)(2)(III)].
“wherein the predefined textual content that describes the educational system is selected based on the functionality that the student’s school uses within the educational system” (e.g., selecting predefined informational content based on a condition)
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
In accordance with Step 2A, Prong 2, the claim does not include any additional elements and the judicial exception is not integrated into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Regarding claim 8
Step 2A Prong 1
Claim 8 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein the plurality of datasets include each of: an emotion dataset;
a rewards dataset; a parental engagement dataset; a structured assignment dataset; a virtual interaction dataset; and an educator notes dataset” these additional elements are recited at a high level of generality and amounts to extra-solution activity of specifying what types of data sources are to be used, i.e. pre-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein the plurality of datasets include each of: an emotion dataset;
a rewards dataset; a parental engagement dataset; a structured assignment dataset; a virtual interaction dataset; and an educator notes dataset” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of limiting the data to a specific data source, i.e., pre-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 9
Step 2A Prong 1
Claim 9 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “wherein the dynamically generated textual content sections include anonymized data pertaining to the student and the peers” which is recited at a high-level of generality such that they amount to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). It is merely stating the desired result of anonymized data, but does not recite how the anonymization is performed.
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “wherein the dynamically generated textual content sections include anonymized data pertaining to the student and the peers” which is recited at a high-level of generality such that it amounts to no more than mere instructions to apply the exception using a generic computer component (See MPEP 2106.05(f)).
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claim 10
Step 2A Prong 1
Claim 10 does not recite an abstract idea, but is directed to the abstract idea identified in its parents claim(s).
Accordingly, at Step 2A, prong one, the claim recites an abstract idea.
Step 2A Prong 2
The judicial exception is not integrated into a practical application. In particular, the claim recites the additional element of “parsing and formatting the analysis prior to presenting the analysis to the educator” these additional elements are recited at a high level of generality and amounts to extra-solution activity of processing the output for presentation, i.e. post-solution activity of selecting a particular data source or type of data to be manipulated for use in the claimed process (see MPEP 2106.05(g)).
Accordingly, at Step 2A, prong two, the additional elements individually or in combination do not integrate the judicial exception into a practical application.
Step 2B
In accordance with Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more that the judicial exception. As discussed above, the additional element of “parsing and formatting the analysis prior to presenting the analysis to the educator” limitation, these additional elements are recited at a high-level of generality and amounts to extra-solution activity of processing output for presentation, i.e., post-solution activity of selecting a particular data source or type of data to be manipulated. The courts have found limitations directed to obtaining information electronically, recited at a high-level of generality, to be well-understood, routine, and conventional (see MPEP 2106.05(d)(II), “receiving or transmitting data over a network”, "electronic record keeping," and "storing and retrieving information in memory").
Accordingly, at Step 2B, the additional element individually or in combination does not amount to significantly more than the judicial exception.
Regarding claims 11-17, which recite substantially the same limitations as claims 1, 3-7, and 9, respectively and are rejected for the same reasons as described above.
Regarding claims 18, 19, and 20, which recite substantially the same limitations as claims 1, 5, and 8, respectively and are rejected for the same reasons as described above.
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.
Claim(s) 1-7, and 9-19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ben-Elazar et al. (US 20230050034 A1, referred to as Ben-Elazar), in view of Mondlock et al. (US 12039263 B, referred to as Mondlock).
Regarding claim 1, Ben-Elazar teaches a method, implemented by an educational system, for anonymously generating an analysis of a student from small datasets, the method comprising:
receiving, from an educator, a request for an analysis of a student, the request identifying the student and a plurality of peers of the student ([0099], [0108-0112]: Describes that an education software platform that presents GUI notifications/data insights to a user, such as a teacher or administrator, and allows the user to select a GUI element or feature form the notification to request additional contextual information about the data insight. The requested data insight may pertain to a specific student user, and may include a classification/analysis of that student’s activity for a current week and specific assignment.; [0076], 0080], and [0111]: The educational frame of reference may be an educational class comprising a plurality of student users, and that an individual student’s activity may be evaluated relative to a class average or other student users in the same class.);
identifying a plurality of small datasets that contain data pertaining to the student and the peers ([0014]: Describes an educational software platform that manages multiple types or student related educational data, including course registration, online classes, document storage, assignment/document submission and grading, transcript management, student assessment evaluation, student schedules, attendance, extracurricular activities, user feedback including student specific surveys, and other student related data needs.; [0017-0020]: Describes accessing user activity data pertaining to interactions by student users with a plurality of applications/services of the educational software platform, including event count data for interactions with specific applications/services or features. The user activity data may further include additional data types, such as categorical representations of user feedback, student surveys representing social and emotional state, proper classification determinations, historical user activity data, and generated data insights.; [0026-0028]: Describes that the educational frame of reference may be an educational class comprising a plurality of student users, such that the data pertains to the target student and peer students in the same educational class.; [0042]: Describes that knowledge repositories storing activity data logs, collected signal data, telemetry data. Corresponding to identifying multiple educational datasets/data sources containing data pertaining to the student and the student’s peers.);
dynamically generating textual content sections from the data pertaining to the student and the peers([0067-0073]: Describes classification levels of user activity are output to a data insight generation application/service to generate data insights pertaining to exemplary classifications, and that data insights may be generated from contextual evaluation of the classifications. It generates data insights that include contextual information, trends/patterns or user activity, and talking points/rationale that explain why a data insight was generated, where the talking points may include specific data types of user activity data and/or correlations or patterns between different data types.; [0077-0078]: Describes GUI notifications including the data insights may be dynamically generated, including dynamically generating the format and layout of the GUI notification based on the exemplary classifications and the type of data insight being generated.; [0080-0083]: Describes generating textual data insights for a specific student user, including data insights indicating the student’s relative to a prior assignment, the student’s activity relative to a class average, the student’s activity relative to a weekly emotional survey, and other student users who provided a similar response. Corresponding to dynamically generating textual content sections, such as data insights and talking points, from educational data pertaining to the student and peer students.);
Although Ben-Elazar teaches data pertaining to the student and the peers, it does not teach generating one or more dataset queries to retrieve the data.
generating one or more dataset queries to retrieve the data pertaining to the student and the peers from the plurality of small datasets (Col. 6, lines 8-31: Describes that a suer may submit a query through a GUI and may manually choose document sources, asset sources, and/or experts in answering the query. It retrieves or scrapes text or data from external data sources, including accessing documents/assets using SQL queries or other suitable methods.; Col. 7, lines 8-35: Describes selecting keywords from a query, searching metadata of document collections, asset collections, and/or expert collections, and using the identified intent to select which sources will be used to answer the query.);
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined the student/peer educational data of Ben-Elazar with the query generation of Mondlock. Doing so would have allowed the system to generate more complete and contextually relevant student insights.
Ben-Elazar in view of Mondlock teaches, building a prompt (Mondlock Col. 7, lines 48 cont. Col. 8 lines 1-5: Describes a query module which generates an augmented user query by supplementing a rephrased user query with information obtained from document collections, asset collections, and/or other suitable sources “to generate a prompt”. It appends contents of retrieved document when generating the augmented user query, summarizing text chunks into output text suitable for submission to an LLM service, implementing prompt engineering to supplement the augmented user query, and adding text instructing the LLM service to answer the user query with the supplemental information instead of relying upon pretrained data.) that includes the dynamically generated textual content sections(As discussed above Ben-Elazar [0067-0073], and [0077-0083]: Describes dynamically generating textual content sections in the form of data insights, contextual information, trends/patterns of user activity, talking points, and rationale based on student and peer educational data.;) and predefined textual content (Mondlock Col. 8, lines 37-61: Describes submitting a prompt to the LLM service that includes the user query, relevant information, and a request for the LLM to answer the user query using the provided relevant information.) that describes the educational system (Ben-Elazar [0014]: Describes an educational software platform context and functionality, including management of student related educational data.);
submitting the prompt to a large language model to cause the large language model to generate the analysis from the prompt (Mondlock Col 4, lines 53 cont. Col 5 lines 1-12: Describes that an LLM service may receive a prompt and generate a natural language response.; Col. 8, lines 50-61: Describes that the LLM interface module may transmit prompts to and receive answers form the LLM service, including transmitting a prompt that includes the user query and relevant information and receiving an answer.; Col. 15, lines 56 cont. Col. 16, liens 1-21: Describes submitting a prompt to the LLM service comprising the user query, relevant information, and a request for the LLM to answer the user query using the provided relevant information, where the prompt causes the LLM service to output answer. Corresponding to submitting the prompt to a large language model to cause the large language model to generate an answer/analysis form the prompt.); and
presenting the analysis to the educator (Ben-Elazar [0003], [0008], and [0012]: Describes that derived classifications are used to generate data insights for display via an application/service of a domain specific software data platform. The generated predictive insights are framed so that other users, such as teachers or parents, can understand why a prediction is made.; [0082]: Describes that exemplary classifications may provide an early warning notification as to levels of user activity so that other users, such as teachers, parents, guardians, or other students, can intervene and set a student user up for success in an educational class.; Mondlock Col. 6, lines 8-23: Describes presenting/outputting the generated answer, including that a user may access a server to submit a query and receive an answer to the query, a GUI may include an output text field for displaying the answer, and the system outputs the answer.).
Regarding claim 2, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the request specifies a time period and the one or more dataset queries are configured to cause the data pertaining to the student and the peers to be limited to the time period ([0012], and [0017]: Describes generating a classification prediction indicating whether students in an educational class are predicted, over a predetermined time period, to have a low or high activity level based on contextual analysis of multiple types of user driven events. The user activity data may be framed according to a temporal frame of reference, where the temporal frame of reference is an identified period of time for which to generate the predictive classification, such as a current week/one week period, days, weeks, months, or years.; [0027]: Describes determining whether user activity for a specific time frame, such as a week in an educational class, semester, or year, will be high or low.; [0080-0083]: Describes generating student specific data insights for a current week, including insights for the student “Shay Daniel” and comparisons to a class average or other students during the current week).
Regarding claim 3, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the plurality of small datasets are maintained by the educational system ([0014]: Describes an educational software platform that manages student related educational data, including course registration, online classes, document storage, assignment/document submission and grading, transcript management, student assessment evaluation, student schedules, attendance, extracurricular activities, user feedback including student specific surveys and other student related data needs.; [0017-0018]:Describes accessing user activity data pertaining to interactions by student users with a plurality of applications/services associated with the educational software platform, including event count data for interactions with specific applications/services and features of the educational software platform.;[0042]: Describes that the system includes knowledge repositories that maintain/store activity data logs, collected signal data, telemetry data including past and present usage of a specific user and/or group of users, application/service data, and other data used to support the disclosed processing.).
Regarding claim 4, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein at least one of the small datasets is maintained by another educational system ([0013-0014]: Describes how the platform may support both organizationally proprietary systems and third party software and systems, including those of independent software vendors, such that users create user activity data by interacting with a plurality of applications/services adapted for the education domain. Integration of educational platforms provides online learning resources.; [0041-0042]: Describes that the host applications/services of the distributed software platform may be configured to interface with other non-proprietary applications/services, such as third party applications/services, to extend functionality including data transformation and associated implementation. Knowledge resources may include data affiliated with a software applications platform as well as data obtained through interfacing with resources over a network connection, including third part applications/services.).
Regarding claim 5, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the plurality of datasets are selected from: an emotion dataset; a rewards dataset; a parental engagement dataset; a structured assignment dataset; a virtual interaction dataset; or an educator notes dataset ([0014]: Describes how the platform manages multiple types of student related educational data.; [0017-0019]: Describes how user activity data pertaining to interactions by student users with a plurality of applications/services associated with the educational software platform, including event count data for interactions with specific applications/services or features, which correspond to a virtual interaction dataset. User activity data may include categorical representations of user feedback, including student surveys representing social and emotional state, corresponding to an emotion dataset.[0080-0081]: Describes assignment related educational data and student specific insights concerning whether a student opened, viewed, or otherwise interacted with a specific assignment, which corresponds to a structured assignment dataset.).
Regarding claim 6, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the plurality of datasets are selected based on functionality that the student’s school uses within the educational system ([0014]: Describes an educational software platform which connects students, teachers, administrators, and parents through customized versions of software products that guide learning and manage associated educational data, and that the capabilities of the educational platform include Functionality such as registering students and courses, conducting online learning/classes, document storage and management, assignment/document submission and grading, student assessment evaluation, management of access to learning content, building student schedules, tracking student attendance, managing extracurricular activity data, user feedback including student specific surveys, and managing other student related data needs in a school.; [0017-0018]: Describes that user activity data pertains to interactions by student users with a plurality of applications/services associated with the educational software platform, including event count data indicating interactions with specific applications/services and/or specific features of the educational software platform.;. [0026-0028]: describes that user activity data is managed according to an educational frame of reference designation, such as an educational class, school, school district, or grade level.).
Regarding claim 7, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the predefined textual content that describes the educational system is selected based on the functionality that the student’s school uses within the educational system ([0017-0018]: Describes how user activity data pertains to interactions with specific applications/services and/or Specific features of the educational software platform, and that the educational frame of reference may be a class, schools, school district, or grade level.).
Regarding claim 9, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the dynamically generated textual content sections include anonymized data pertaining to the student and the peers ([0128-0129]: Describes that user activity data for a plurality of student users associated with an educational class is identified and scrubbed to remove student user identification, and that scrubbing the input feature data to remove user identification helps minimize bias when analyzing the user activity data.; [0067-0073], and [0154-0156]: Describes that classifications generated form the student-user activity data are output to a data insight generation application/service to generate data insights, including contextual information, trends/patterns of user activity, and talking points/rationale explaining why the data insight was generated.; The data insights/talking points are generated from student and peer data that has been scrubbed to remove student/user identification, corresponding to dynamically generated textual content sections that include anonymized data pertaining to the student and the peers.).
Regarding claim 10, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches further comprising: parsing and formatting the analysis prior to presenting the analysis to the educator ([0077-0078]: Describes generating a GUI notification comprising one or more data insights, where formatting of the GUI notification may be predetermined to populate data fields based on the type of notification being generated, such as a call out, GUI menu, graph, or dynamic timeline. The format and layout of the GUI notification may be dynamically generated based on analysis of the classifications and the type of data insight being generated, and that the notification and/or contextual data insights are rendered in a GUI for presentation via a host application/service endpoint.).
Regarding claims 11-17 which recite substantially the same limitations as claims 1, 3-7, and 9. Claims 11-17 further recite one or more computer storage media (Ben-Elazar [0097-0103]: Describes a processing system with storage media and other computer hardware and software to execute the educational platform.) to performed the method steps of claims 1, 3-7, and 9, respectively and are therefore rejected on the same premise.
Regarding claims 18, and 19, which recite substantially the same limitations as claims 1, and 6-7 and are therefore rejected on the same premise.
Claim(s) 8, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ben-Elazar et al. (US 20230050034 A1, referred to as Ben-Elazar), in view of Mondlock et al. (US 12039263 B, referred to as Mondlock), in view of Grimes et al. (US 20140272847 A1, referred to as Grimes), in view of Creamer et al. (US 20130330704 A1, referred to as Creamer).
Regarding claim 8, Ben-Elazar in view of Mondlock teaches, the method of claim 1.
Ben-Elazar teaches wherein the plurality of datasets include each of: an emotion dataset; a rewards dataset; a parental engagement dataset; a structured assignment dataset; a virtual interaction dataset; and an educator notes dataset ([0014]: Describes how the platform manages multiple types of student related educational data.; [0017-0019]: Describes how user activity data pertaining to interactions by student users with a plurality of applications/services associated with the educational software platform, including event count data for interactions with specific applications/services or features, which correspond to a virtual interaction dataset. User activity data may include categorical representations of user feedback, including student surveys representing social and emotional state, corresponding to an emotion dataset.[0080-0081]: Describes assignment related educational data and student specific insights concerning whether a student opened, viewed, or otherwise interacted with a specific assignment, which corresponds to a structured assignment dataset.)
Although Ben-Elazar teaches, an emotion dataset, a structured assignment dataset, and a virtual interaction dataset. It does not teach a rewards dataset.
Grimes teaches a rewards dataset ([0007-0008]: Describes a reward based improvement/incentive system that determines a learning state, learning activity status, and reward category for a user, and grants a reward based on the determined learning state, learning activity status, and reward category.; [0120-0130: That the reward based learning system may capture a user’s learning and reward preferences, maintain one or more reward based learning profiles for each user, track the users progress, and grant rewards such as customize rewards or electronic rewards. Corresponding to maintaining reward related data for students, i.e., a rewards data set)
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Ben-Elazar in view of Mondlock’s platform with the rewards dataset of Grimes. Doing so would allow the system to consider student motivation, learning progress, reward preferences, and awarded incentives when generating student insights.
Although Grimes teaches a rewards dataset. It do not teach a parental engagement dataset, and an educator notes dataset.
Creamer teaches a parental engagement dataset, and an educator notes dataset ([0008-0010]: Describes a student information system including a parent management module that allows a parent to manage alerts, customize attributes/preferences/parameters, and communicate with the faculty, and also describes a parent/student portal.; [0083]: Describes Displaying information about a students or parents online activity, including a graph or other representation of online activity during a period of time, corresponding to parental engagement data.; [0088-0090]: Describes that a teacher may use the student information system to add notes, such as teacher notes, and other content/materials to a course or assignment, and that the system may track student records and, notes, and changes made by teachers. Corresponding to a parental engagement and educator notes datasets.).
It would have been obvious to one of ordinary skill in the art at the time of the claimed invention to have combined Ben-Elazar in view of Mondlock, in view of Grime’s platform with the datasets of Creamer. Doing so would allow the system to consider additional student context from parent/guardian activity, parent-faculty communications, teacher notes, and student records when generating more complete and contextually relevant educational insights.
Regarding claim 20, which recite substantially the same limitations as claim 8 and are therefore rejected on the same premise.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. See attached PTO-892 for additional art including.
US 12008332 B1: LLM prompt automation
US 20220319181 A1: education specific student monitoring datasets
US 20200327252 A1: anonymization and privacy preserving student or sensitive data analytics
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/D.T.R./Examiner, Art Unit 2128
/OMAR F FERNANDEZ RIVAS/Supervisory Patent Examiner, Art Unit 2128
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