DETAILED ACTION
This communication is in response to the Application filed on 06/16/2023. Claims 1-20 are pending and have been examined.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 1/7/2026 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Response to Arguments
With respect to the 35 U.S.C. 101 rejections for claims 1-20, the applicant asserts that as amended, claim 1 includes generating a second prompt including an instruction tasking the foundation model service with generating the adjusted reading passage and a data structure comprising the reading passage, the assessed grade level, and the target grade level. (Specification [0069]). Additionally, to tie the process closer to the user interface, claim 1 now includes enabling display of the adjusted reading passage in the user interface of the application with the at least one trouble word distinguished visually from other words of the adjusted reading passage. (Specification [0102] and FIG. 5D). Further, within the user interface, enabling display of a graphical input element with which to receive user input requesting another adjusted reading passage having a different level of difficulty. (Specification [0104] and FIG. 5D). Therefore, per Prong 2 of Step 2A of the Alice framework, the amended claim as a whole integrates the alleged mental process into a practical application that is not practically performable by the human mind.
Examiner respectfully disagrees, the user interface elements which the claim amendments primarily focus on merely represent extra-solution steps of data aggregations and presentment. Things such as showing trouble words, a user selecting trouble words, and an option to request another passage are all steps in gathering information that is necessary to perform the method. As for the foundation model service generating a passage using the trouble words and an assessed grade level, this process can be completed by the human mind. Using the teacher example from the 101 rejection below, a teacher can use the reading level of a student and trouble words they selected to write a passage for the student to read.
With respect to the 35 U.S.C. 103 rejections for claims 1-20, the applicant asserts that the amendments to the claims have overcome the current prior art. These arguments are considered moot in view of an updated prior art search necessitated by the amendments to the claims. Updated prior art rejections are detailed below.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claims 1, 10, and 16 recite One or more [computer readable storage media]; one or more [processors] operatively coupled with the one or more computer readable storage media; and an application comprising program instructions stored on the one or more computer readable storage media that, when executed by the one or more processors, direct the computing apparatus to at least: enable display, in a [user interface] of the application, of trouble words associated with a reader;
receive, in the user interface of the application, a selection of at least a trouble words associated with a reader; generate a prompt with which to elicit, from a [foundation model service], a reply that includes a reading passage tailored to a reading ability of a reader, wherein the prompt includes instructions for generating the reading passage using the trouble words and according to a desired readability based on one or more indicators of the reading ability of the reader; assess the readability of the reading passage using one or more readability analysis algorithms to generate an assessed grade level of the reading passage; generate a second prompt with which to elicit, from the foundation model service, a second reply that includes an adjusted request reading passage, wherein the second prompt includes an instruction tasking the foundation model service with generating the adjusted reading passage and a data structure comprising the reading passage, the assessed grade level, and the target grade level; and enable display of the adjusted reading passage in the [user interface] of the application with the trouble word distinguished visually from other words of the adjusted reading passage; and enable display, in the user interface, of a graphical input element with which to receive user input requesting another adjusted reading passage having a different level of difficulty.
The limitations in these claims, as drafted, are a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. An example of how this could be performed by the human mind is a teacher providing a student with a passage to practice reading. The teacher could first give the student a list of trouble words that the student can pick to have in the passage. A teacher could create a prompt tailored to the ability of the reader by handwriting one for them using the knowledge they have of the student’s reading ability. The student could specify words they had been struggling with and request a specific reading grade level for the teacher to create the passage with. The teacher could assess the grade level of the passage by comparing it to different example passages corresponding to different age groups, grade levels, or reading scores as well as to the students desired reading level. The teacher could then show the student the passage they made and ask them if they’d like it to be made easier/more difficult. Finally, the teacher could show the final passage they created, with trouble words highlighted, to the student by handing them a piece of paper they wrote it down on. The final passage could also include a checkbox that the student could check if they want another passage. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, claims 1, 10, and 16 recite a foundation model service and a user interface. The foundation model is merely being used to apply the mental process via a generic computing device. The foundation model is detailed further in paragraph 21 of the specification as various potential implementations of large-scale AI models. The use of such is well known and standard within the art and is merely being applied to this invention. The user interface is considered pre-solution activity as it is merely a data gathering step used before the method begins. Claims 1 and 16 specifically lists additional components a computer-readable storage media and a processor. The computer readable storage media is detailed on paragraph 114 of the specification with a generic description of the component. The processor is detailed on paragraph 113 of the specification with a generic description of the component. Both of these components would be considered generic computer components being used to perform a limitation of the human mind. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim 2 recites wherein the graphical input element comprises a first button to make the adjusted reading passage more difficult and a second button to make the adjusted reading passage less difficult.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of adjusting text to be more or less difficult upon another person’s request. The buttons merely function a part of the user interface performing the data gathering step in the method. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 3 recites wherein selection of the graphical input element causes the program instructions to direct the computing apparatus to generate a third prompt with which to elicit, from the foundation model service, a third reply that includes another adjusted reading passage, and to enable display of the another adjusted reading passage in the user interface.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. From the independent claim example, this would be a checkbox by the passage the teacher created that the student could check in order to get another passage created. The graphical input element merely functions as part of the user interface performing the data gathering step in the method. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claim 4 recites wherein the program instructions further direct the computing apparatus to enable display, in the user interface, of a configuration pane comprising: a number of attempts for completing a reading assignment based on the adjust reading passage, a time limit for completing the reading assignment, and an option to capture audio data of the reader reading the adjusted reading passage.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. A human can physically write down the number of attempts at a reading, the time limit to complete the reading, and a check box signifying if they want their reading to be recorded. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claims 5, 12, and 18 recite wherein to enable display of the adjusted reading passage in the user interface, the program instructions direct the computing apparatus to enable display of the adjusted reading passage after a maximum number of adjustments to the reading passage.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This would be the teacher setting a limit on how many times a student can request changes be made to the reading passage. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 6, 13, and 20 recite wherein the program instructions further direct the computing apparatus to receive, in the user interface, a selection of a topic for the reading passage, and wherein the prompt includes the topic.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. It’s quite common for readings to be assigned an estimated grade level, the teacher could use examples of readings at different grade levels to compare the passage they created to and then assign a grade level accordingly. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 7, 14, and 19 recites wherein to assess the readability of the reading passage, the program instructions direct the computing apparatus to: employ the one or more readability analysis algorithms to generate readability scores for the reading passage based on linguistic and structure features of the text; aggregate the readability scores to generate the assessed grade level of the reading passage.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind can judge the difficulty of a reading passage as well as perform algorithms that score the difficulty. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claims 8 and 15 recites wherein the one or more indicators of readability comprise one or more of: age, grade level, and level of difficulty.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The teacher could rank the reading passage based on these various metrics. As stated previously, the teacher could have various example writing that correspond to specific age ranges, grade levels, and difficulty ratings, then compare the written passage to them to score it. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
Claim 9 recites wherein the program instructions further direct the computing apparatus to: receive user input comprising a language of the reading passage; and translate the reading passage into the language.
The limitation in this claim, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The teacher could ask the student what language they’d like the passage in, hear the student’s response, and then translate the passage manually using their own knowledge or a translation book/resource. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claim does not recite any additional elements that were not in the independent claim. Accordingly, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claim is not patent eligible.
Claims 11 and 17 recites wherein the graphical input element comprises a first button to make the adjusted reading passage more difficult and a second button to make the adjusted reading passage less difficult and wherein the method further comprises, upon selection of the graphical input element, generating a third prompt with which to elicit a third reply from the foundation model service that includes another adjusted reading passage, and enabling display of the another adjusted reading passage in the user interface.
The limitation in these claims, as drafted, is a process that, under broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. The human mind is capable of adjusting text to be more or less difficult upon another person’s request. A human could also create a checkbox by the created passage that the student could check in order to get another passage created. The buttons and graphical input element merely function as part of the user interface performing the data gathering step in the method. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. The claims do not recite any additional elements that were not in the independent claims. Accordingly, the claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims are not patent eligible.
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.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1, 3-5, 7-8, 10, 12, 14-16, and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication 20230142574 A1 (Blau-McCandliss et al.) in view of US Patent Publication 20230169268 A1 (Van Hickman), Korea Patent Publication KR 102146433 B1 (Jun), US Patent Publication US 20220406214 A1 (Kang).
Regarding Claims 1, 10, and 16, Blau-McCandliss et al. teaches; A computing apparatus comprising: one or more computer readable storage media; one or more processors operatively coupled with the one or more computer readable storage media; and an application comprising program instructions stored on the one or more computer readable storage media that, when executed by the one or more processors, direct the computing apparatus to at least:
(The data storage 1116 (e.g., a data storage device) includes the machine-readable medium 1122 (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions 1124 embodying any one or more of the methodologies or functions described herein. The instructions 1124 may also reside, completely or at least partially, within the main memory 1104, within the static memory 1106, within the processor 1102 (e.g., within the processor's cache memory), or any suitable combination thereof, before or during execution thereof by the machine 1100.) (Paragraph 77).
Claim 10 states A method of operating an application on a computing device, comprising:
(FIG. 5-8 are flowcharts illustrating operations of the device 130 in performing a method 500 of generating custom text based on the skill profile 320, according to some example embodiments.) (Paragraph 34).
Claim 16 states One or more computer readable storage media having an application comprising program instructions stored thereon that, when executed by one or more processors operatively coupled with the one or more computer readable storage media, direct a computing device to at least:
(The data storage 1116 (e.g., a data storage device) includes the machine-readable medium 1122 (e.g., a tangible and non-transitory machine-readable storage medium) on which are stored the instructions 1124 embodying any one or more of the methodologies or functions described herein.) (Paragraph 77).
receive, in the user interface of the application, (a selection of at least a trouble word) (taught by Jun) of the trouble words;
(The user 132 may be able to interact with the diagnostic app, and such interaction with the diagnostic app may cause the diagnostic app to provide output to the user profile accessor 410. The output of the diagnostic app may be, include, or otherwise specify one or more words that are difficult for the user 132, easy for the user 132, or any suitable combination thereof.) (Paragraph 42).
In Blau-McCandliss it is shown how a user can manually specify trouble words by directly interacting with the app.
generate a prompt with which to elicit, from a foundation model service,
(The machine generates custom text that includes the set of words by inputting the set of words into a learning machine (e.g., a generator module or other AI module)) (Paragraph 13).
The prompt is generated from an AI module which is equivalent to a foundation model service.
a reply that includes a reading passage tailored to a reading ability of a reader,
(A machine (e.g., a mobile device or other computing machine) is specially configured (e.g., by suitable hardware modules, software modules, or any suitable combination thereof) to behave or otherwise function as a custom text generator. The machine accesses a skill profile of a user. The skill profile specifies a set of one or more skills (e.g., language skills, such as literacy skills) that correspond to the user (e.g., skills in which the user is weak, strong, or any suitable combination thereof).) (Paragraph 13).
The system generates text based on the user’s reading ability.
wherein the prompt includes instructions for generating the reading passage using the trouble words [and according to a desired readability](addressed by secondary reference) based on one or more indicators of the reading ability of the reader;
(The machine determines (e.g., selects or updates) a set of words that correspond to the user based on the set of language skills (e.g., literacy skills) specified by the skill profile. The machine generates custom text that includes the set of words by inputting the set of words into a learning machine (e.g., a generator module or other AI module) that is trained based on a reference set of documents to generate custom text based on one or more inputted words.) (Paragraph 13).
(The user 132 may be able to interact with the diagnostic app, and such interaction with the diagnostic app may cause the diagnostic app to provide output to the user profile accessor 410. The output of the diagnostic app may be, include, or otherwise specify one or more words that are difficult for the user 132, easy for the user 132, or any suitable combination thereof.) (Paragraph 42).
The information regarding the trouble words and reading ability is taken from the users’ skill profile then used to generate the passage. It is further described how a user can manually specify these trouble words to the system.
generate a second prompt with which to elicit, from the foundation model service, a second reply that includes an adjusted reading passage;
(performing a linguistic analysis of audio data generated from the user reading aloud the first generated document; and wherein: the accessing of the skill profile of the user includes updating the skill profile of the user based on the linguistic analysis of the audio data generated from the user reading aloud the first generated document; and the generated second document is generated by the trained learning machine based on the skill profile updated based on the linguistic analysis) (Paragraph 101).
Blau-McCandliss et al. includes a system creating a second passage based on the readability of the first passage.
and enable display of the adjusted reading passage in a user interface of the application
(In the operation 840, the custom text generator 430 causes the second custom text generated in the operation 830 to be presented (e.g., to the user 132). For example, the custom text generator 430 may display or cause display of the second custom text by a display screen of the device) (Paragraph 60).
Blau-McCandliss et al. specifies displaying the adjusted passage to the user
Blau-McCandliss et al. does not explicitly teach: enable display, in a user interface of the application, of trouble words associated with a reader; receive, in the user interface of the application, a selection of at least a trouble word of the trouble words; wherein the prompt includes instructions for generating the reading passage using the trouble words and according to a desired readability based on one or more indicators of the reading ability of the reader; and wherein the reading ability comprises a target grade level; assess the readability of the reading passage with respect to the desired readability, resulting an assessed readability; wherein the second prompt includes the assessed grade level and the target grade level; enable display of the adjusted reading passage in the user interface of the application with the trouble word distinguished visually from other words of the adjusted reading passage; enable display, in the user interface, of a graphical input element with which to receive user input requesting another adjusted reading passage having a different level of difficulty.
However, Jun teaches: enable display, in a user interface of the application, of trouble words associated with a reader;
(The associative memory unit 330, when any one of the at least one English word corpus icon is selected, a Korean sentence starting with an English word previously mapped to the selected English word corpus icon and stored, and at least one included in the Korean sentence At least one English word corresponding to the Korean word of may be displayed on the user terminal 100 in an associative memory format. Continuing to cite the above example, assume that A1 is selected from the corpus of A1, A2, A3... in the user terminal 100, and the word Airplane included in A1 is randomly selected. Of course, if it is not in the initial stage, that is, if the user has learned the word Airplane, it can be selected except for the rest, or if the test is not passed even though the word Airplane is learned, it can be reselected. At this time, since the Korean mapped to the Airplane is an airplane, a Korean sentence with the airplane as the first word can be extracted.) (Page 6, Paragraph 3)
Jun teaches a language learning platform in which the user selects a word that they are trying to learn and then the system outputs sentences including that term
receive, in the user interface of the application, a selection of at least a trouble word of the trouble words;
(The associative memory unit 330, when any one of the at least one English word corpus icon is selected, a Korean sentence starting with an English word previously mapped to the selected English word corpus icon and stored, and at least one included in the Korean sentence At least one English word corresponding to the Korean word of may be displayed on the user terminal 100 in an associative memory format. Continuing to cite the above example, assume that A1 is selected from the corpus of A1, A2, A3... in the user terminal 100, and the word Airplane included in A1 is randomly selected.) (Page 6, Paragraph 3)
The user selects keywords using the terminal.
While Blau-McCandliss et al. teaches identifying trouble words it gets them from a user profile rather than a direct user input selection. The Jun reference teaches selecting a trouble word from a user interface and producing text that includes said word. It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the reading ability-based text generation method as taught by Blau-McCandliss et al. to prompt the user to select trouble words as taught by Jun. This would have been an obvious improvement to allow a user selectively emphasize the vocabulary in their learning (Jun, Page 2, Paragraph 3)
Blau-McCandliss et al. in view of Jun does not explicitly teach: wherein the prompt includes instructions for generating the reading passage using the trouble words and according to a desired readability based on one or more indicators of the reading ability of the reader; and wherein the reading ability comprises a target grade level; assess the readability of the reading passage with respect to the desired readability, resulting an assessed readability; wherein the second prompt includes the assessed grade level and the target grade level; enable display of the adjusted reading passage in the user interface of the application with the trouble word distinguished visually from other words of the adjusted reading passage; enable display, in the user interface, of a graphical input element with which to receive user input requesting another adjusted reading passage having a different level of difficulty.
However, Van Hickman teaches: wherein the prompt includes instructions for generating the reading passage using [at least a trouble word and](addressed by Blau-McCandliss et al.) according to a desired readability based on one or more indicators of the reading ability of the reader;
(The desired reading level may be provided through a user interface that provides selectable reading levels. For example, the selectable reading levels for a text could be 7.1, 7.2, 7.3, 7.4, and so on.) (Paragraph 27).
(Once the input reading level and the desired reading level are determined, the technology described herein adjusts the text to match the desired reading level. Conceptually, the initial reading level is used to determine how many words need to be substituted to adjust the textual input to the desired reading level.) (Paragraph 28).
Van Hickman has the user specify a desired reading level. This information along with an initial reading level of an input passage are used to create a new passage for the user.
and wherein the reading ability comprises a target grade level;
(The complexity may be retrieved from one or more sources, such as, but not limited to Fog readability formula, simplified Flesch-Kincaid grade level readability formula, Sander's consonant acquisition chart, word frequency, Flesch-Kincaid grade level Readability Formula, and the like.) (Paragraph 116).
(The desired reading level may be provided through a user interface that provides selectable reading levels. For example, the selectable reading levels for a text could be 7.1, 7.2, 7.3, 7.4, and so on. These example-reading levels increase the 7th grade reading level by 10% of the 8th grade reading level incrementally.) (Paragraph 27).
Van Hickman assesses the readability using an estimated grade level. Furthermore, the user also inputs an estimated grade level for their desired reading level.
assess the readability of the reading passage using one or more readability analysis algorithms to generate an assessed grade level of the reading passage;
(As an additional quality check, an updated reading level for the candidate replacement text may be determined using the same system that generated the initial reading level. If the updated reading level is within a threshold of the desired reading level, then the candidate replacement text may be output to the user.) (Paragraph 32).
(The complexity may be retrieved from one or more sources, such as, but not limited to Fog readability formula, simplified Flesch-Kincaid grade level readability formula, Sander's consonant acquisition chart, word frequency, Flesch-Kincaid grade level Readability Formula, and the like.) (Paragraph 116).
The passage created from the initial text and inputted desired reading level has its readability assessed after creation. The readability can be assessed using a readability analysis algorithm.
wherein the second prompt includes an instruction tasking the foundation model service with generating the adjusted reading passage and a data structure comprising the reading passage, the assessed grade level and the target grade level;
(Once the input reading level and the desired reading level are determined, the technology described herein adjusts the text to match the desired reading level. Conceptually, the initial reading level is used to determine how many words need to be substituted to adjust the textual input to the desired reading level. The initial reading level and desired reading level can also be inputs to determining the complexity of the synonyms eventually selected.) (Paragraph 28).
Van Hickman adjusts the reading passage according to how the desired grade level compares to the assessed grade level.
While Blau-McCandliss et al. teaches most of the limitations of these claims, their invention creates a desired readability using a skill profile. The Van Hickman reference teaches that the desired readability could alternatively be obtained via a user input and using a grade level as a metric. It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the reading ability-based text generation method as taught by Blau-McCandliss et al. in view of Jun to prompt the user for a desired readability as taught by Van Hickman. This would have been an obvious improvement allow a user to manually finely tune the difficulty level of their reading (Van Hickman, Paragraph 4)
Blau-McCandliss et al. in view of Jun does not explicitly teach: enable display of the adjusted reading passage in the user interface of the application with the trouble word distinguished visually from other words of the adjusted reading passage; enable display, in the user interface, of a graphical input element with which to receive user input requesting another adjusted reading passage having a different level of difficulty.
However, Van Hickman teaches: enable display of the adjusted reading passage in the user interface of the application with the trouble word distinguished visually from other words of the adjusted reading passage;
(The reading fluency question may further include a reading guide message for a passage or word phrases. The reading guide message is, for example, a message that guides the standard pronunciation of a specific word (e.g., “‘kotbang-gwi’ meaning snort is pronounced as [koppang-gwi or kodppang-gwi].”), a message that guides a user to read a specific word (e.g., “Pay attention to the pronunciation of ‘padakpadak’ meaning flapflap and read it clearly.”), and the like.) (Paragraph 32)
Kang teaches a language learning system which emphasizes trouble words in a reading by including a message to draw attention to it. This can be seen in Figs. 3a and 3b
enable display, in the user interface, of a graphical input element with which to receive user input requesting another adjusted reading passage having a different level of difficulty.
(For allowing the user to listen to the user's recorded voice, the user interface activates the listen button at B. When the user's reading fluency score is equal to or greater than the progress criterion, the user interface activates a proceed button at C to turn to the page on which the subsequent reading fluency question is displayed.) (Paragraph 77).
Kang provides a button to the user which upon being pressed triggers a new reading passage to be displayed. This can be seen in Fig. 5b.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the reading ability-based text generation method as taught by Blau-McCandliss et al. in view of Jun and Van Hickman to emphasize keywords and include a button for displaying a new passage as taught by Kang. This would have been an obvious improvement as added display options provide immediate feedback to the user on their reading progress (Kang, Paragraph 4)
Regarding Claim 3, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the apparatus of claim 1;
Furthermore, Kang teaches wherein selection of the graphical input element causes the program instructions to direct the computing apparatus to generate a third prompt with which to elicit, from the foundation model service, a third reply that includes another adjusted reading passage, and to enable display of the another adjusted reading passage in the user interface.
(For allowing the user to listen to the user's recorded voice, the user interface activates the listen button at B. When the user's reading fluency score is equal to or greater than the progress criterion, the user interface activates a proceed button at C to turn to the page on which the subsequent reading fluency question is displayed.) (Paragraph 77).
Kang teaches the GUI element for producing another reading passage to be displayed. This is in combination with Blau-McCandliss’s method of generating adjusted reading passages using an AI model.
Regarding Claim 4, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the apparatus of claim 3;
Furthermore, Kang teaches wherein the program instructions further direct the computing apparatus to enable display, in the user interface, of a configuration pane comprising: a number of attempts for completing a reading assignment based on the adjust reading passage, a time limit for completing the reading assignment, and an option to capture audio data of the reader reading the adjusted reading passage.
(To limit the size of voice data and efficiently calculate the reading fluency score, the user interface 100 may limit the user's recording time. For example, unless the user interface 100 receives a reading end input, e.g., a touch input of a record button, a voice input requesting the end, etc. within a preset time limit after recording starts, the user interface 100 may stop recording and prompt the user to re-record.) (Paragraph 34).
(The scoring model may extract as reading-aloud features, for example, all or part of the number of self-corrections, the number of hesitant reads, the number of repeated reads, and the number of sign ignorances. The scoring model may determine a user's reading speed and/or reading accuracy or calculate a reading fluency score based on the extracted misreading features and/or reading-aloud features.) (Paragraph 48).
(As shown in FIG. 3(b), when the user completes listening to the guiding voice, the user interface activates the record button B so that the user can record the voice.) (Paragraph 72).
(Since the reading fluency score is calculated based on the user's reading speed and reading accuracy, the user interface may display the user's reading speed (or reading time) and reading accuracy together.) (Paragraph 77).
Kang displays a reading speed for the user (Fig. 5B) and sets a maximum time for reading the passage. Kang also calculates a reading score based, in part, on how often the reader repeats it and displays this reading score to the user. Lastly, Kang displays a UI button for capturing audio while reading a passage as can be seen in Figs. 3-5
Regarding Claims 5, 12 and 18, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the method of claims 4, 11, and 17;
Furthermore, Blau-McCandliss et al. teaches wherein to enable display of the adjusted reading passage in the user interface, the program instructions direct the computing apparatus to enable display of the adjusted reading passage after a maximum number of adjustments to the reading passage.
(For example, the custom text generator 430 may display or cause display of the second custom text by a display screen of the device 130 (e.g., for viewing by the user 132, for the user 132 to read aloud, or both).) (Paragraph 60).
Fig. 8 of Blau-McCandliss et al. shows the round-trip process of creating an adjusted passage where it can be seen that a maximum number of adjustment (1) is performed before the text is presented to the user again.
Regarding Claims 7 and 14, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the method of claims 5 and 12;
Furthermore, Van Hickman teaches wherein to assess the readability of the reading passage, the program instructions direct the computing apparatus to: employ the one or more readability analysis algorithms to generate readability scores for the reading passage based on linguistic and structure features of the text;
(The complexity may be retrieved from one or more sources, such as, but not limited to Fog readability formula, simplified Flesch-Kincaid grade level readability formula, Sander's consonant acquisition chart, word frequency, Flesch-Kincaid grade level Readability Formula, and the like.) (Paragraph 116).
Van Hickman states the scoring metrics used to assess grade level of the reading.
aggregate the readability scores to generate the assessed grade level of the reading passage.
(The complexity may be retrieved from one or more sources, such as, but not limited to Fog readability formula, simplified Flesch-Kincaid grade level readability formula, Sander's consonant acquisition chart, word frequency, Flesch-Kincaid grade level Readability Formula, and the like. In one aspect, complexity score from multiple sources are normalized and then combined through a weighting algorithm. Giving equal weight to various sources is possible or differential weights may be used.) (Paragraph 116).
The various scores are combined.
Regarding Claims 8 and 15, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the method of claims 1 and 10;
Furthermore, Van Hickman teaches wherein the one or more indicators of readability comprise one or more of: age, grade level, and level of difficulty.
(The complexity may be retrieved from one or more sources, such as, but not limited to Fog readability formula, simplified Flesch-Kincaid grade level readability formula, Sander's consonant acquisition chart, word frequency, Flesch-Kincaid grade level Readability Formula, and the like.) (Paragraph 116).
Van Hickman, as stated before, uses a grade level as an indicator of readability. Furthermore, by readability, consonant acquisition, and word frequency formulas it assesses a level of difficulty.
Regarding Claim 19, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the computer readable storage media of claim 18;
Furthermore, Van Hickman teaches wherein the assessed grade level based on an aggregation of scores of the reading passage generated based on one or more scoring metrics.
(The complexity may be retrieved from one or more sources, such as, but not limited to Fog readability formula, simplified Flesch-Kincaid grade level readability formula, Sander's consonant acquisition chart, word frequency, Flesch-Kincaid grade level Readability Formula, and the like.) (Paragraph 116).
(The desired reading level may be provided through a user interface that provides selectable reading levels. For example, the selectable reading levels for a text could be 7.1, 7.2, 7.3, 7.4, and so on. These example-reading levels increase the 7th grade reading level by 10% of the 8th grade reading level incrementally.) (Paragraph 27).
Van Hickman assesses the readability using an estimated grade level and states the scoring metrics used to do so. Furthermore, the user inputs an estimated grade level for their desired reading level.
Claims 2, 11, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 20230142574 A1 (Blau-McCandliss et al.) in view of US Patent Publication 20230169268 A1 (Van Hickman), Korea Patent Publication KR 102146433 B1 (Jun), US Patent Publication US 20220406214 A1 (Kang), and US Patent Publication US 20190114300 A1 (Miltsakaki).
Regarding Claim 2, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the apparatus of claim 1;
Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang does not teach wherein the graphical input element comprises a first button to make the adjusted reading passage more difficult and a second button to make the adjusted reading passage less difficult.
However, Miltsakaki teaches wherein the graphical input element comprises a first button to make the adjusted reading passage more difficult and a second button to make the adjusted reading passage less difficult.
(To that end, the user interface server 110 may display a number of selectable target reading level 18 options to the user 10. In some embodiments, the system 20 analyzes the input text 12, determines the original reading level 14, and offers selection of target reading level 18 that are less difficult than the original reading level 14. Additionally, or alternatively, the system 20 may select a pre-determined reading level 18 for the user 10 (e.g., based on a pre-defined user 10 selection, based on previous user 10 preferences, and/or on a questionnaire provided to determine the appropriate reading level of the user 10). In some embodiments, however, the system 20 provides all available reading levels 18 as selectable options.) (Paragraph 31)
Miltsakaki teaches a system for modifying the difficulty of reading passages where the user can select the level of difficulty they would like the passage adjusted to.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the reading ability-based text generation method as taught by Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang to UI buttons for changing the difficulty of the reading as taught by Miltsakaki. This would have been an obvious improvement to allow the user to change the reading level to what they see fit. (Miltsakaki, Paragraph 3).
Regarding Claims 11 and 17, Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang teaches the method of claims 10 and 16;
Furthermore, Miltsakaki teaches graphical input element comprises a first button to make the adjusted reading passage more difficult and a second button to make the adjusted reading passage less difficult and wherein the method further comprises, upon selection of the graphical input element, generating a third prompt with which to elicit a third reply from the foundation model service that includes another adjusted reading passage, and enabling display of the another adjusted reading passage in the user interface.
(To that end, the user interface server 110 may display a number of selectable target reading level 18 options to the user 10. In some embodiments, the system 20 analyzes the input text 12, determines the original reading level 14, and offers selection of target reading level 18 that are less difficult than the original reading level 14. Additionally, or alternatively, the system 20 may select a pre-determined reading level 18 for the user 10 (e.g., based on a pre-defined user 10 selection, based on previous user 10 preferences, and/or on a questionnaire provided to determine the appropriate reading level of the user 10). In some embodiments, however, the system 20 provides all available reading levels 18 as selectable options.) (Paragraph 31)
(In accordance with yet another embodiment, a computer-implemented method for simplifying an input text receives an input text. The method generates an estimated reading level, from of a plurality of reading levels, for the input text. The method also generates a simplified version of the input text, based on a reading level that is less difficult than the estimated reading level, in a manner that preserves a meaning of the input text in the simplified version.) (Paragraph 10).
Miltsakaki teaches a system for modifying the difficulty of reading passages where the user can select the level of difficulty they would like the passage adjusted to.
Claims 6, 13, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 20230142574 A1 (Blau-McCandliss et al.) in view of US Patent Application Publication 20230169268 A1 (Van Hickman), Korea Patent Publication KR 102146433 B1 (Jun), US Patent Publication US 20220406214 A1 (Kang), and further in view of US Patent Application Publication 20240378397 A1 (Liu).
Regarding Claim 6, 13, and 20, Blau-McCandliss in view of Jun, Van Hickman, and Kang teaches the apparatus of claims 5, 12, and 18.
Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang does not explicitly teach: program instructions further direct the computing apparatus to receive, in the user interface, a selection of a topic for the reading passage, and wherein the prompt includes the topic.
However, Liu teaches a program instructions further direct the computing apparatus to receive, in the user interface, a selection of a topic for the reading passage, and wherein the prompt includes the topic.
(In FIG. 5A, the first selection page 100 displays multiple boxes for the user to fill in the topic as well as the variables used for subsequently generating the content text.) (Paragraph 63).
(Step S30: communicating with the NLP model through the communications module 30 for receiving a content text generated by the NLP model according to the topic, the at least one variable, the selected writing type, and the relevance data;) (Paragraph 32).
Liu presents a method of generating text in which the user can select the topic for the text and that information is provided to an NLP model to generate it.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the reading ability-based text generation method as taught by Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang to implement the selection of a topic as taught by Liu. This would have been an obvious improvement to make the generated text be relevant to whatever topic the user would like. (Liu, Paragraph 6-7).
Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over US Patent Application Publication 20230142574 A1 (Blau-McCandliss et al.) in view of US Patent Application Publication 20230169268 A1 (Van Hickman), Korea Patent Publication KR 102146433 B1 (Jun), US Patent Publication US 20220406214 A1 (Kang), and further in view of US Patent Application Publication 20200334411 A1 (Patel et al.).
Regarding Claim 9, Blau-McCandliss in view of Jun, Van Hickman, and Kang teaches the apparatus of claim 1.
Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang does not explicitly teach: wherein the program instructions further direct the computing apparatus to: receive user input comprising a language of the reading passage; and translate the reading passage into the language.
However, Patel et al. teaches a wherein the program instructions further direct the computing apparatus to: receive user input comprising a language of the reading passage;
(The translation 164 accessibility module allows a user to select a language that the user prefers.) (Paragraph 73)
Patel et al. teaches a system that improves accessibility of web pages with modifications. It includes a translation module which prompts the user to select a language for the text to be in.
and translate the reading passage into the language.
(The web page may display text in the language that is selected by the user. In an exemplary embodiment, the text of a web page is translated into multiple languages and stored in the web page.) (Paragraph 73).
The system of Patel et al. then translates the text to the selected language.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the reading ability-based text generation method as taught by Blau-McCandliss et al. in view of Jun, Van Hickman, and Kang to implement the language translation of text as taught by Patel et al. This would have been an obvious improvement to make the invention and the text provided more accessible to all people. (Patel et al. Paragraph 4).
Conclusion
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/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/DOUGLAS GODBOLD/Primary Examiner, Art Unit 2655
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