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 Double Patenting rejections for claims 1-20, the applicant asserts the amended claims now include substantial new limitations that distinguish them from the co-pending application.
The applicants’ arguments are considered persuasive. In view of amendments to the claims as well as amendments to the claims of co-pending application 18/336,407 the non-statutory double patenting rejections for claims 1-20 have been withdrawn from consideration.
If the applicant makes further amendments which cause the claims to once again align with those of 18/336,407, the examiner reserves the right to reimplement the statutory double patenting rejection.
With respect to the 35 U.S.C. 101 rejections for claims 1-20, the applicant asserts Claims 1, 10, and 16 are therefore amended to specify "receive, in a user interface of the application, a selection of one or more trouble words associated with a reader" as recited by amended claims 1, 10, and 16. These steps integrate the claimed process with a specific user interface in which "a user, such as a teacher, can request and receive a custom reading passage configured according to characteristics of a target audience (e.g., student reader) and which includes one or more trouble words, i.e., words identified as troublesome for the target audience learning to read." Specification, paragraph [0017]. The claims also specify "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 recited by amended claims 1, 10, and 16. As described in the specification, "an assessed readability for the reading passage is generated based on an aggregate readability score, such as a grade level estimation" using one or more reading models that analyze the reading passage. Specification, paragraph [0037].
Examiner respectfully disagrees, the amendments do not do enough to overcome the 101 rejections. Specifically, the inclusion of the user interface is implemented in such a manner that it is considered extra-solution activity. This is due to it only serving the initial step of data gathering and not being present throughout the method until the displaying step. Using it to display the information is considered post-solution activity as it is merely presenting the output of the method. As for the amendments regarding the grade level and target grade level, these are considered things that the human mind is capable of determining.
Applicant asserts the amended claims do not recite a mental process because they require foundation model services that host deep learning AI models. As explained in the specification, "Foundation model service 130 hosts foundation model 131 which is representative of a deep learning AI model, such as GPT-3®, GPT-3.5, ChatGPT®, or GPT-4, BERT, ERNIE, T5, XLNet, or other multimodal or unimodal model." Specification, paragraph [0055]. These foundation models cannot be performed mentally by a teacher. The application service assesses the readability of the reading passage and "generates a follow-on prompt tasking the foundation model service with adjusting or tuning the reading passage so its readability is closer to the desired readability" with "the follow-on prompt [including] previous versions of the reading passage along with corresponding readability assessments of the previous versions to guide the foundation model service in narrowing in on the desired readability." Specification, paragraph [0018]. Upon receiving an adjusted reading passage, "the adjusted reading passage is again assessed for readability" and if still beyond the margin of error, "another prompt is generated tasking the foundation model service with again adjusting the reading passage so its readability within acceptable bounds of the desired reading passage." Specification, paragraph [0019]. A teacher cannot mentally perform these complex interactions with AI foundation models that generate and iteratively adjust reading passages based on automated readability assessments.
The claims as a whole do not rely on human judgment but instead require a computing apparatus to perform specific automated operations using foundation model services. As described in the specification, "the follow-on prompts may include the original and subsequent adjusted versions of the reading passage along with the assessed readability scores or grade level estimates to guide the foundation model service in adjusting the reading passage to achieve the desired readability" with "the prompt [including] a data structure containing the original and adjusted versions of the reading passage along with the readability scores of the versions, the data structure being a JSON object, corresponding data vectors, or a two-dimensional data array." Specification, paragraph [0038]. The specification further explains that "the cycle of adjusting the reading passage is repeated until the desired readability is achieved or until a maximum number of adjustment cycles has occurred, at which point the adjustment cycle is exited and the reading passage is configured for display" to avoid "excess latency degrading the user experience as well as costs associated with excessive foundation model usage." Specification, paragraph [0020]. The application service "may limit the number of adjustments to be performed on a reading passage" because "repeated adjustment cycles may increase latency and potentially degrade the user experience." Specification, paragraph [0039]. These technical interactions between automated readability analysis systems and AI foundation models cannot be performed in the human mind.
Examiner respectfully disagrees, the foundational model service is not being interpreted as something the human mind can perform, but rather as an additional generic component being used to perform an activity the human mind can perform. It is considered generic as it can be implemented in many different way using general purpose models that are not specific to the method. Overall, if you were to remove the AI component from the claims the method is one which can be performed by the human mind and merely applying that method via a computer/AI is not considered patentable.
Applicant asserts the amended claims also specify that "the second prompt includes the assessed grade level and the target grade level" as recited by amended claims 1, 10, and 16. This technical feature enables structured data exchange between the application service and foundation model service, including readability scores and grade level estimates in specific data formats as described in paragraph [0038] of the specification. This automated data exchange between computing services represents a specific technological implementation that goes beyond abstract mental processes. Claims do not recite a mental process when they do not contain limitations that can practically be performed in the human mind, for instance when the human mind is not equipped to perform the claim limitations. SRIInt'l, Inc. v. Cisco Systems, Inc., 930 F.3d 1295, 1304 (Fed. Cir. 2019).
Examiner respectfully disagrees, the human mind is capable of determining what grade level another person is reading at or what grade level a specific passage aligns with. The human mind is capable of using the current grade level of a passage and a target grade level in order to modify the passage to be easier/more difficult. Also, the human mind is capable of creating a prompt that could be submitted to an AI model such as ChatGPT.
With respect to the 35 U.S.C. 103 rejections for claims 1-20, the applicant asserts Claims 1, 10, and 16 are amended herein to now recite, in part, "receive, in a user interface of the application, a selection of one or more trouble words associated with a 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," and "wherein the second prompt includes the assessed grade level and the target grade level" as recited by amended claims 1, 10, and 16. As discussed during the interview, the combination of Blau-McCandliss and Van Hickman fails to teach or reasonably suggest at least these aspects of claims 1, 10, and 16.
Examiner respectfully disagrees, the claim amendments presented differ substantially from the ones presented in the interview. In regards to the amendments to claims 1, 10, and 16 it is believed that the current prior art still teaches the claims in their entirety. Further information on these rejections can be found below.
Applicant asserts the combination of Blau-McCandliss and Van Hickman fails to teach or suggest "assess the readability of the reading passage using one or more readability analysis algorithms to generate an assessed grade level of the reading passage" and "generate a second prompt with which to elicit, from the foundation model service, a second reply that includes a request to adjust the reading passage based on the assessed grade level, wherein the second prompt includes the assessed grade level and the target grade level" as recited by amended claims 1, 10, and 16.
Blau-McCandliss teaches generating a second custom text based on a linguistic analysis of audio data generated from the user reading aloud a first custom text, where the skill profile is updated based on this linguistic analysis of the audio data. Blau-McCandliss, paragraph [0056]. Specifically, Blau-McCandliss performs "a linguistic analysis of audio data" that is "generated by (e.g., recorded by) the device 130 as a result of the user 132 reading aloud a first custom text that has been presented to the user 132 by the device 130." Blau-McCandliss, paragraph [0056]. The skill profile is then "updated to include a skill corresponding to a detected deficiency or proficiency in the ability of the user 132 to pronounce a word or a sub-component of a word." Blau-McCandliss, paragraph [0057].
This approach in Blau-McCandliss is fundamentally different from the claimed invention. In Blau-McCandliss, the adjustment to generate a second text occurs only after the user has read the first text aloud and audio data has been captured and analyzed. The system waits for user performance feedback before making any adjustments.
In contrast, claims 1, 10, and 16 as amended require assessing the readability of the reading passage using readability analysis algorithms to generate an assessed grade level before the user even sees the reading passage. The amended claims specifically require that "the second prompt includes the assessed grade level and the target grade level" as recited by claims 1, 10, and 16. This technical feature enables structured data exchange between the application service and foundation model service, including readability scores and grade level estimates in specific data formats. Neither Blau-McCandliss nor Van Hickman teaches including both the assessed grade level and target grade level in a prompt to a foundation model service.
Van Hickman teaches assessing readability after text modification, where "an updated reading level for the candidate replacement text may be determined using the same system that generated the initial reading level" and "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." Van Hickman, paragraph [0032]. However, Van Hickman operates on existing textual content by substituting words with synonyms, rather than generating entirely new reading passages through prompts to a foundation model service as required by the amended claims.
The combination of Blau-McCandliss and Van Hickman fails to teach the specific technical approach of assessing readability using readability analysis algorithms to generate an assessed grade level before user interaction, and then including both the assessed grade level and target grade level in a second prompt to adjust the reading passage. This represents a fundamentally different technical approach from the user-performance-based adjustments taught by Blau-McCandliss.
The combination of Blau-McCandliss, Van Hickman, and Patel fails to teach or suggest the limitations of claim 9 for the same reasons discussed above regarding the base claim 1. Patel merely teaches translation functionality where "the translation 164 accessibility module allows a user to select a language that the user prefers" and "the web page may display text in the language that is selected by the user." The addition of Patel does not cure the deficiencies in the combination of Blau-McCandliss and Van Hickman discussed above.
Examiner respectfully disagrees, the current claim language does not explicitly state that assessment of readability cannot be done within the method by displaying the passage to the user. Blau-McCandliss still displays an updated passage based on an assessment of the users reading ability as seen in Paragraph 101. Van Hickman is then incorporated to show that the metric of the reading ability can be a grade level (Paragraphs 27 and 116) and that a target grade level and measured grade level can be combined when creating a new passage at a different difficulty (Paragraphs 28 and 32). In combination these references teach the claim limitations.
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:
receive, in a [user interface] of the application, a selection of one or more 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 one or more 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 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 a request to adjust the reading passage based on the assessed grade level, wherein the second prompt includes the assessed grade level and the target grade level; and enable display of the adjusted reading passage in the [user interface] of the application.
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. A student could provide the teacher with a list of trouble words by writing them down on a piece of paper. 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 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 to the student by handing them a piece of paper they wrote it down on. 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 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 request to adjust the reading passage comprises a request to make the reading passage more difficult when the assessed grade level is below the target grade level.
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. This would be the teacher having their own grade level associated with the passage they wrote using an algorithm by doing any calculations mentally. They could then listen to the student request a higher grade level and then rewrite the passage to be more difficult. The rewriting of this passage could be based the student providing their current grade level and desired reading grade level. 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 the request to adjust the reading passage comprises a request to make the reading passage less difficult when the assessed grade level is above the target grade level.
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. This would be the teacher having their own grade level associated with the passage they wrote, listening to the student requesting a lower grade level, and then rewriting the passage to be less difficult. 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 request to adjust the reading passage comprises a request to make the reading passage more difficult when the assessed grade level is below the target grade level.
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. This would be the teacher having their own grade level associated with the passage they wrote, listening to the student requesting a higher grade level, and then rewriting the passage to be more difficult. 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 and 14 recites wherein the assessed grade level is based on an aggregation of scores of the reading passage generated based on one or more scoring metrics.
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 base the grade level off various different scoring metrics such as average word length, length of the passage, or how many uncommon words are in the 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 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/or 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 request to adjust the reading passage comprises one of: a request to make the reading passage more difficult when the assessed grade level is below the target grade level and a request to make the reading passage less difficult when the assessed grade level is above the target grade level.
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 having their own grade level associated with the passage they wrote, listening to the student requesting a lower or higher grade level, and then rewriting the passage to be more or less difficult. 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 19 recites wherein the assessed readability comprises an estimated grade level based on an aggregation of scores of the reading passage generated based on one or more scoring metrics.
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. 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. The teacher could base the grade level off various different scoring metrics such as average word length, length of the passage, or how many uncommon words are in the 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 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 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 1-5, 7-8, 10-12, and 14-19 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).
Regarding Claims 1, 10, and 16, Blau-McCandliss et al. teaches; receive, in a user interface of the application, a selection of one or more trouble words associated with a reader;
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 prompt is generated from an AI module which is equivalent to 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).
a reply that includes a reading passage tailored to a reading ability of a reader,
The system generates text based on the users reading ability (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).
wherein the prompt includes instructions for generating the reading passage using one or more 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 information regarding the trouble words and reading ability is taken from the users’ skill profile then used to generate the passage (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). It is further described how a user can manually specify these trouble words to the system (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).
generate a second prompt with which to elicit, from the foundation model service, a second reply that includes a request to adjust the reading passage based on the [grade level] (addressed by Van Hickman);
Blau-McCandliss et al. includes a system creating a second passage based on the readability of the first 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).
and enable display of the adjusted reading passage in a user interface of the application.
Blau-McCandliss et al. specifies displaying the adjusted passage to the user (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).
Claims 1 and 16 specifically, further state 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: These components are included in the system as seen in Fig. 11 and described to operate together (The term “machine-readable medium” shall also be taken to include any medium, or combination of multiple media, that is capable of carrying (e.g., storing or communicating) the instructions 1124 for execution by the machine 1100, such that the instructions 1124, when executed by one or more processors of the machine 1100 (e.g., processor 1102), cause the machine 1100 to perform any one or more of the methodologies described herein,) (Paragraph 79).
Blau-McCandliss et al. does not explicitly teach: wherein the prompt includes instructions for generating the reading passage using [one or more trouble words and](addressed by Blau-McCandliss) 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;
However, Van Hickman teaches: wherein the prompt includes instructions for generating the reading passage using [one or more trouble words 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;
Van Hickman has the user specify a desired reading level (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). This information along with an initial reading level of an input passage is used to create a new passage for the user (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).
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 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. 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)
Regarding Claim 2, Blau-McCandliss et al. in view of Van Hickman teaches the apparatus of claim 1;
Furthermore, Blau-McCandliss et al. teaches wherein the request to adjust the reading passage comprises a request to make the reading passage more difficult when the assessed grade level is below the target grade level.
In Blau-McCandliss et al. the adjustment to the reading passage is made based on the linguistic analysis of the user reading the first passage. This adjustment can detect a proficiency in the pronunciation of words and adjust the skill profile accordingly when generating the next passage. Detecting proficiencies is equivalent to being below the desired readability as it means the user is already excelling at the reading level. (In the operation 810, the user profile accessor 410 updates the skill profile 320 of the user 132 based on the linguistic analysis (e.g., some or all of the output thereof) performed in the operation 802. For example, the skill profile 320 may be updated to include a skill corresponding to a detected deficiency or proficiency in the ability of the user 132 to pronounce a word or a sub-component of a word.) (Paragraph 57). A user can input words that are easy for them into the system as a form requesting that the generated passages difficulty be comprised around it (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… The output of the diagnostic app may accordingly be a basis for generating the skill profile 320 of the user 132.) (Paragraph 42).
Furthermore, Van Hickman, as in the independent claim, teaches using a grade level as the metric for readability
(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).
Regarding Claim 3, Blau-McCandliss et al. in view of Van Hickman teaches the apparatus of claim 1;
Furthermore, Blau-McCandliss et al. teaches wherein the request to adjust the reading passage comprises a request to make the reading passage less difficult when the assessed grade level is above the target grade level.
In Blau-McCandliss et al. the adjustment to the reading passage is made based on the linguistic analysis of the user reading the first passage. This adjustment can detect a deficiency in the pronunciation of words and adjust the skill profile accordingly when generating the next passage. Detecting deficiencies is equivalent to being above the desired readability as it means the user is struggling at the reading level. (In the operation 810, the user profile accessor 410 updates the skill profile 320 of the user 132 based on the linguistic analysis (e.g., some or all of the output thereof) performed in the operation 802. For example, the skill profile 320 may be updated to include a skill corresponding to a detected deficiency or proficiency in the ability of the user 132 to pronounce a word or a sub-component of a word.) (Paragraph 57). A user can input words that are difficult for them into the system as a form requesting that the generated passages difficulty be comprised around it (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… The output of the diagnostic app may accordingly be a basis for generating the skill profile 320 of the user 132.) (Paragraph 42).
Furthermore, Van Hickman, as in the independent claim, teaches using a grade level as the metric for readability
(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).
Regarding Claim 4, Blau-McCandliss et al. in view of Van Hickman teaches the apparatus of claim 3;
Furthermore, Blau-McCandliss et al. teaches wherein the request to adjust the reading passage comprises a request to make the reading passage more difficult when the assessed grade level is below the target grade level.
In Blau-McCandliss et al. the adjustment to the reading passage is made based on the linguistic analysis of the user reading the first passage. This adjustment can detect a proficiency in the pronunciation of words and adjust the skill profile accordingly when generating the next passage. Detecting proficiencies is equivalent to being below the desired readability as it means the user is already excelling at the reading level. (In the operation 810, the user profile accessor 410 updates the skill profile 320 of the user 132 based on the linguistic analysis (e.g., some or all of the output thereof) performed in the operation 802. For example, the skill profile 320 may be updated to include a skill corresponding to a detected deficiency or proficiency in the ability of the user 132 to pronounce a word or a sub-component of a word.) (Paragraph 57). A user can input words that are easy for them into the system as a form requesting that the generated passages difficulty be comprised around it (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… The output of the diagnostic app may accordingly be a basis for generating the skill profile 320 of the user 132.) (Paragraph 42).
Furthermore, Van Hickman, as in the independent claim, teaches using a grade level as the metric for readability
(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).
Regarding Claims 5, 12 and 18, Blau-McCandliss et al. in view of Van Hickman 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.
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. (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).
Regarding Claims 7 and 14, Blau-McCandliss et al. in view of Van Hickman teaches the method of claims 5 and 12;
Furthermore, Van Hickman teaches wherein the assessed grade level is based on an aggregation of scores of the reading passage generated based on one or more scoring metrics.
Van Hickman states the scoring metrics used to assess grade level of the reading (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).
Regarding Claims 8 and 15, Blau-McCandliss et al. in view of Van Hickman 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.
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. (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).
Regarding Claims 11 and 17, Blau-McCandliss et al. in view of Van Hickman teaches the method of claims 10 and 16;
Furthermore, Blau-McCandliss et al. teaches wherein the request to adjust the reading passage comprises one of: a request to make the reading passage more difficult when the assessed grade level is below the target grade level and a request to make the reading passage less difficult when the assessed grade level is above the target grade level.
In Blau-McCandliss et al. the adjustment to the reading passage is made based on the linguistic analysis of the user reading the first passage. This adjustment can detect a deficiency and proficiency in the pronunciation of words and adjust the skill profile accordingly when generating the next passage. Detecting deficiencies is equivalent to being above the desired readability as it means the user is struggling at the provided reading level. Detecting proficiencies is equivalent to being below the desired readability as it means the user is already excelling at the provided reading level. (In the operation 810, the user profile accessor 410 updates the skill profile 320 of the user 132 based on the linguistic analysis (e.g., some or all of the output thereof) performed in the operation 802. For example, the skill profile 320 may be updated to include a skill corresponding to a detected deficiency or proficiency in the ability of the user 132 to pronounce a word or a sub-component of a word.) (Paragraph 57). A user can input words that are easy/difficult for them into the system as a form requesting that the generated passages difficulty be comprised around it (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… The output of the diagnostic app may accordingly be a basis for generating the skill profile 320 of the user 132.) (Paragraph 42).
Furthermore, Van Hickman, as in the independent claim, teaches using a grade level as the metric for readability
(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).
Regarding Claim 19, Blau-McCandliss et al. in view of Van Hickman 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.
Van Hickman assesses the readability using an estimated grade level and states the scoring metrics used to do so (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). Furthermore, the user inputs an estimated grade level for their desired reading level (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).
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) and further in view of US Patent Application Publication 20240378397 A1 (Liu).
Regarding Claim 6, 13, and 20, Blau-McCandliss in view of Van Hickman teaches the apparatus of claims 5, 12, and 18.
Blau-McCandliss et al. in view of Van Hickman 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 Van Hickman 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) and further in view of US Patent Application Publication 20200334411 A1 (Patel et al.).
Regarding Claim 9, Blau-McCandliss in view of Van Hickman teaches the apparatus of claim 1.
Blau-McCandliss et al. in view of Van Hickman 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;
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. (The translation 164 accessibility module allows a user to select a language that the user prefers.) (Paragraph 73)
and translate the reading passage into the language.
The system of Patel et al. then translates the text to the selected 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).
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 Van Hickman 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
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
01/23/2026