DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
1. Regarding the claim objection, Applicant has amended claim 17 to address the minor informality. Accordingly, the objection is withdrawn.
2. Regarding the rejection under 35 U.S.C. § 101, Applicant has amended claim 15 to specifically recite “computer-readable storage medium”, which has a special definition provided in Applicant’s specification (para. 0023) that this term is not to be construed as being storage in the form of transitory signals per se. Therefore, the rejection of claims 15-20 as being rejected under 35 U.S.C. 101 for being directed to non-statutory subject matter is withdrawn.
However, Applicant’s arguments regarding the rejection of claims 1-20 under 35 U.S.C. 101 for being directed to an abstract idea without significantly more is not persuasive.
Applicant first argues on pgs. 10-12 that the amended claims integrate any alleged abstract ideas into a practical application. Specifically, Applicant argues that amended claim 1 integrates features which reflect an improvement to a technology or technical field, such as the recited generation of the explanation according to a likelihood of the explanation being accepted. The Examiner respectfully disagrees. The claims do not integrate the judicial exception into a practical application as the only additional elements present in the claim amount to mere instructions to implement the judicial exception using a generic computer (a network, a discourse explanation program, and a client device are all generic computer components). Furthermore, the recitation of the explanation being generated according to a likelihood of the explanation being accepted by the group falls under the category of mental process at the level it is being currently recited, and does not amount to a technical improvement. A person can write down an explanation that they believe is likely to be understood by the group, which reads on this limitation. For these reasons, the claims do not integrate the judicial exception into a practical application under Step 2A Prong 2.
Hence, Applicant’s arguments are not persuasive.
3. Regarding the rejection under 35 U.S.C. § 102 and 103, Applicant's arguments filed 05/26/2026 have been fully considered but they are not persuasive.
Applicant argues on pg. 13 and similarly on pg. 15 that the cited prior art does not teach the emphasized portions of the amended limitation of “generating an explanation for the piece of content according to the explanation level and a likelihood of the explanation being accepted by the group, wherein the explanation comprises natural language text, audio, images, videos not present in the piece of content”.
Regarding the first emphasized limitation (“generating an explanation for the piece of content according to…a likelihood of the explanation being accepted by the group”), Applicant’s arguments have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Regarding the second emphasized limitation (“wherein the explanation comprises natural language text, audio, images, videos not present in the piece of content”), Applicant’s arguments have been considered but are not found persuasive. Under the BRI of this limitation, Van Hickman discloses this feature. Van Hickman disclose, as part of the generation of the explanation, replacing one or more target words in the textual content with replacement words (see para. 0118). In other words, the replacement text generated contains natural language text (replacement words) which were not in the original textual content. This reads on the BRI of wherein the explanation comprises natural language text, audio, images, videos not present in the piece of content.” Hence, Applicant’s arguments are not persuasive.
Claim Objections
4. Claims 1, 8, and 15 are objected to because of the following informalities:
Amended claims 1, 8, and 15 now recite “wherein the explanation comprises natural language text, audio, images, videos not present in the piece of content”. There should be an “or” or an “and” before “videos” to indicate if one or all of the elements are present in the explanation. For purposes of examination, this limitation is being interpreted with “or” (i.e. the explanation need only have one of natural language text, audio, image, or video).
Appropriate correction is required.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
5. Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 8, and 15, “A computer-implemented method”, “A computer system”, and “A computer program product” are recited, with the method and system directed to one of the four statutory categories of invention (process, system) (Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
identifying a target skill level for a group of one or more users…: a person determines a skill level of users (e.g. determines a first user is an expert in a particular subject, whereas the second user is a novice)
identifying a complexity level and a range of explainability corresponding to a piece of content …: a person determines how complex a piece of content is (e.g. determines a scientific research paper has a high complexity) and a range of explainability
determining… an explanation level for the piece of content based on i) the complexity level of the piece of content ii) the target skill level, and iii) the range of explainability, wherein the range of explainability is determined through quantitative techniques and qualitative techniques: person determines a level of explanation to give based on complexity levels, target skill levels, and ranges of explainability (e.g. for a high complexity level research paper for a novice user, using a high explanation level)
generating an explanation for the piece of content according to the explanation level and a likelihood of the explanation being accepted by the group, wherein the explanation comprises natural language text, audio, images, videos not present in the piece of content: person writes down an explanation using pen and paper and determines videos, text, audio, and images not present in the content to use for explaining, based on explanation level (e.g. for the complex article being read by the novice user, writing down a simpler explanation with pictures)
providing… the explanation… a target user in the group: person shows written response to the user in the group
Claims 1, 8, and 15 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). The only additional element are “a processor-implemented method” (claim 1), “one or more processors, one or more computer-readable memories, one or more computer-readable tangible storage medium, and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising” (claim 8), “A computer program product, the computer program product comprising: one or more computer-readable tangible storage medium and program instructions stored on at least one of the one or more tangible storage medium, the program instructions executable by a processor capable of performing a method, the method comprising” (claim 15), “a group of users on a network” (claims 1, 8, 15), “content received though the network to a computer”, “determining, by a discourse explanation program on the computer” (claims 1, 8, and 15), and “providing, through the network…to a client device”, which are recited at a high level of generality and amount to mere instructions to implement the judicial exception using a generic computer. Mere instructions to implement the judicial exception using a generic computer do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract idea. Therefore, claims 1, 8, and 15 are directed to an abstract idea.
Claims 1, 8, and 15 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer, which does not amount to significantly more than the judicial exception as it does not provide an inventive concept. Therefore, claims 1, 8, and 15 are not patent eligible.
Regarding claim 2-7, 9-14, and 16-20, “The processor-implemented method”, “The computer system”, and “The computer program product” are recited, with the method and system directed to one of the four statutory categories of invention (process, machine) (Step 1: YES). However, the claims limitations, under their broadest reasonable interpretation, recite mental processes which fall into the category of abstract idea (Step 2A Prong 1: YES).
The following limitations, under their broadest reasonable interpretation, recite mental processes:
Claims 2, 9, and 16:
determining an explainability of the piece of content based on the complexity level of the piece of content: a person decides an explainability of the content based on its complexity (e.g. decides a scientific journal article has a low explainability)
Claims 2, 9, and 16 contain no additional elements.
Claims 3, 10, and 17:
wherein the content comprises a message in a chat service: a person reads chat messages in particular for determining explainability
Claims 3, 10, and 17 contain no additional limitations.
Claims 4, 11, and 18:
wherein identifying the complexity level is performed …: a person can read content and determine how complex it is (e.g. determine a science article has high complexity)
Claims 4, 11, and 18 contain the additional limitation of “using natural language processing”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 5, 12, and 19:
wherein the target skill level and the complexity level each correspond to a particular subject: a person interprets target skill level and complexity level relating to a particular subject (e.g., science, history, politics)
Claims 5, 12, and 19 contain no additional limitations.
Claims 6, 13, and 20:
wherein generating an explanation includes selecting an explanation from a preexisting…of explanations: a person creates a response by using prewritten explanations
Claims 6, 13, and 20 contain the additional limitation “from a preexisting repository”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 7 and 14:
wherein generating an explanation…: a person can write down an explanation relating to the content item
Claims 7 and 14 contain the additional limitation “includes natural language processing”, which amounts to mere instructions to implement the judicial exception using a generic computer.
Claims 2-7, 9-14, and 16-20 do not contain any additional elements which integrate the judicial exception into a practical application (Step 2A Prong 2: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer, which even when viewed in combination do not integrate the judicial exception into a practical application as they do not impose any meaningful limits on practicing the abstract idea. Therefore, claims 2-7, 9-14, and 16-20 are directed to abstract ideas.
Claims 2-7, 9-14, and 16-20 do not contain any additional elements which amount to significantly more than the judicial exception (Step 2B: NO). As discussed above, the only additional limitations amount to mere instructions to implement the judicial exception using a generic computer, which even when viewed in combination do not amount to significantly more than the judicial exception as they do not provide an inventive concept. Therefore, claims 2-7, 9-14, and 16-20 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.
6. Claims 1-2, 6, 8-9, 13, 15-16, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Van Hickman (US 2023/0169268 A1) in view of Kargiannakis et al. (US 2020/0265184 A1, hereinafter Kargiannakis).
Regarding claim 1, Van Hickman discloses A computer-implemented method (para. 0128), the method comprising: identifying a target skill level for a group of for one or more users (para. 0047 “The MSV score 258 may be associated with the user via a user profile stored in the reading database 260 or elsewhere. The MSV score is a type of proficiency score.”; para. 0112 “A desired reading level 1215 for the output text 1212 is received. The desired reading level 1215 may be based on a reading level determined for a reader. In one aspect, the reading level for a reader may be the MSV score 258.”) on a network (Fig. 1, network 110 connecting user devices 102a-n); identifying a complexity level and a range of explainability corresponding to a piece of content (para. 0110 “The input text 1210 may be a book, news article, blog post, short story, or any other textual content.”; para. 0044 “The input text 201 may be part of a reading assignment. The reading instruction system 200 can include a corpus of input texts for students to read. In one aspect, the corpus of input texts are stored in reading database 260. In aspects, the input texts can be documents, webpages, book excerpts, books, and the like.”; complexity level: para. 0111 “An initial reading level of the textual content may be determined by the reading level component 1207.”; range of explainability: para. 0026 “The desired reading level may be set one or two increments above the reading level equivalent to the MSV score.”) received through the network to a computer (para. 0039 “Data sources 104a and 104b through 104n may comprise data sources and/or data systems, which are configured to make data available to any of the various constituents of operating environment 100, or reading instruction system 200 described in connection to FIG. 2. For example, the data sources may comprise text to be read by a student, an audio file of storing a recording of a student reading, a video file storing a video of a student reading, a reading assessment, an error report, and other data items described herein. Data sources 104a and 104b through 104n may be discrete from user devices 102a and 102b through 102n and server 106 or may be incorporated and/or integrated into at least one of those components.”); determining, by a discourse explanation program on the computer (para. 0042 “Reading instruction system 200 includes reading interface 205, speech-to-text component 210, error identifier 220 (and its components 221, 222, 224, 226, 228, 230, 232, 234), and cueing system 250 (and its components 252, 254, 256). These components may be embodied as a set of compiled computer instructions or functions, program modules…”; see also para. 0145), an explanation level for the piece of content based on i) the complexity level of the piece of content ii) the target skill level (initial (i) and desired reading levels (ii) used to determine a complexity level: para. 0028 “The initial reading level and desired reading level can also be inputs to determining the complexity of the synonyms eventually selected…”; para. 0030 “In aspects, the complexity level may be retrieved from a data record with complexity levels assigned various words. The complexity level is used to select words to substitute with words in the input text. Initially, a heuristic may be used to select the complexity of candidate words and an amount of candidate words to substitute. The heuristic may map various candidate words to a reading level adjustment that will occur with each word, given an initial reading level of the text. The heuristic may also exclude candidate words with above a threshold complexity for use with certain desired reading levels. This prevents inappropriately complex words from being used as substitute words ”, see also para. 0117) and iii) the range of explainability, (range used for determining desired reading level, which affects explanation level: para. 0026 “The desired reading level may be set one or two increments above the reading level equivalent to the MSV score.”), wherein the range of explainability is determined through quantitative techniques and qualitative techniques (the range of explainability (i.e. the gap between an initial and desired reading level) is based on quantitative techniques (MSV calculation) and qualitative techniques (a mapping to a qualitative reading level schema): para. 0026 “The desired reading level may be set one or two increments above the reading level equivalent to the MSV score.”; para. 0064 “The MSV can be a grouping each cue score. In other words, the MSV score can have three attributes and three corresponding scores. The three attributes (MSV) could be combined in a weighted combination to determine a final MSV score. The MSV score could serve as an overall reading competency score. The MSV could be combined with other factors, such as the total number of errors, the reading level of the input text, and other factors to determine a separate competency score.”; para. 0112 “The MSV score 258 may be mapped to a reading level schema to enable use of the MSV score 258 for selecting the desired reading level…”; para. 0113 “The reading levels used by the technology described herein can use existing reading level measurements, such as, but not limited to, GUIDED READING LEVELS (GRL), ACCELERATED READER (AR) ATOS LEVEL, DEVELOPMENTAL READING ASSESSMENT (DRA), and LEXILE MEASUREMENT.”); generating an explanation for the piece of content according to the explanation level…wherein the explanation comprises natural language text, audio, images, videos not present in the piece of content (para. 0118 “Once the candidate words are selected based on the assigned complexity level, a candidate replacement text is generated by substituting one or more target words in the textual content with one or more candidate replacement words. Not all target words originally identified need to be replaced.”; the explanation comprises natural language text not present in the piece of content (i.e. substitute words not found in original text)); and providing, through the network, the explanation to a client device of a target user in the group (para. 0119 “As an additional quality check, an updated reading level for the candidate replacement text may be determined by the reading-level component 1207. 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. If outside the threshold, then the candidate replacement text may be adjusted by undoing a replacement, making a new replacement, or updating a replacement with more complex or less complex candidate word.”; Fig. 1, 102, 110).
Van Hickman does not specifically disclose [generating an explanation for the piece of content according to the explanation level] and a likelihood of the explanation being accepted by the group…
Kargiannakis teaches [generating an explanation for the piece of content according to …] a likelihood of the explanation being accepted by the group (para. 0061 “Content conversion system 100 may transform text such that each of these dimensions of simplicity is within a certain tolerance of the target readability and/or comprehensibility level—to create an even feel to the document and maximize overall readability and comprehensibility…”; para. 0150 “Furthermore, a confidence level may be applied to an understanding of whether there is sufficient proof that this change is being recognized appropriately. For example, if a number of users reject a transformation, the confidence level reduces. Confidence may be based on a frequency of use, and vary based on user feedback. The value of a readability level and/or comprehensibility level associated with a particular transformation may also move concurrently with the movement of the readability levels and/or comprehensibility levels of those users accepting the transformation, and confidence increases.”; para. 0191 “The decision to send a content segment for transformation may be controlled by conversion controller 1102, and may be based, for example, on a confidence level.”).
Van Hickman and Kargiannakis are considered to be analogous to the claimed invention as they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van Hickman to incorporate the teachings of Kargiannakis in order to generate the explanation based on a likelihood of the explanation being accepted by the group. Doing so would be beneficial, as this would allow for comprehensible explanations to be generated based on confidences from user feedback, enabling transformations which users have historically associated as comprehensible being used (para. 0150).
Regarding claim 2, Van Hickman in view of Kargiannakis discloses wherein determining an explanation level further comprises: determining an explainability of the piece of content based on the complexity level of the piece of content (Van Hickman, explainability of content (determining level of complexity of replacement words for the content) is determined based on complexity level of the content (initial reading level): para. 0030 “The heuristic map various candidate words toa reading level adjustment that will occur with each word, given an initial reading level of the text…”; see also para. 0117).
Regarding claim 6, Van Hickman in view of Kargiannakis discloses wherein generating an explanation includes selecting an explanation from a preexisting repository of explanations (Van Hickman, generating an explanation with candidate replacement words which are preexisting words synonymous with words in the input text: para. 0115 “The candidate selector 1203 takes the list of target words from the speech component 1202 and identifies synonyms. The synonyms may be selected from multiple sources, such as BERT, Wordnet lexical base, Merriam Webster thesaurus, and like…”).
Regarding claim 8, claim 8 is a computer system claim with limitations similar to those in claim 1, and is thus rejected under similar rationale.
Additionally, Van Hickman discloses A computer system (Fig. 16), the computer system comprising: one or more processors (Fig. 16, 1614), one or more computer-readable memories (Fig. 16, 1612, para. 0150 “Memory 1612 includes computer storage media in the form of volatile and/or nonvolatile memory.”), one or more computer-readable tangible storage medium (Fig. 16, 1612, para. 0148 “Computer storage media includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Computer storage media does not comprise a propagated data signal.”), and program instructions stored on at least one of the one or more tangible storage medium for execution by at least one of the one or more processors via at least one of the one or more memories, wherein the computer system is capable of performing a method comprising (para. 0034 “For instance, some functions may be carried out by a processor executing instructions stored in memory.”; para. 0128 “For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media.”).
Regarding claim 9, claim 9 is rejected for analogous reasons to claim 2.
Regarding claim 13, claim 13 is rejected for analogous reasons to claim 6.
Regarding claim 15, claim 15 is a computer program product claim with limitations similar to those recited in claim 1, and is thus rejected under similar rationale.
Additionally, Van Hickman discloses A computer program product, the computer program product comprising: one or more computer-readable storage medium and program instructions stored on at least one of the one or more computer-readable storage medium, the program instructions executable by a processor capable of performing a method, the method comprising (Fig. 16, 1612, para. 0148 “Computer storage media includes RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices. Computer storage media does not comprise a propagated data signal.”; para. 0128 “For instance, various functions may be carried out by a processor executing instructions stored in memory. The methods may also be embodied as computer-usable instructions stored on computer storage media.”).
Regarding claim 16, claim 16 is rejected for analogous reasons to claim 2.
Regarding claim 20, claim 20 is rejected for analogous reasons to claim 6.
7. Claims 3, 10, and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Van Hickman in view of Kargiannakis and further in view of Shevchenko et al. (US 10,594,757 B1, hereinafter Shevchenko).
Regarding claim 3, Van Hickman in view of Kargiannakis does not specifically disclose wherein the content comprises a message in a chat service.
Shevchenko teaches wherein the content comprises a message in a chat service (AIA assists users with readability and vocabulary mismatch for chat messaging: Col. 56 Lines 63-67 and Col. 57 Lines 1-5 “In embodiments, the AIA may be used to improve clarity and effectiveness, such as through context and communication profiles. For instance, an alert of readability or vocabulary mismatch may be provided to a user based on the target audience (e.g., too many idioms in a text for non-native speakers or inclusion of complex language in a text for children), a suggestion for an improvement to or automatic rewrite of the text may be provided to adjust readability and vocabulary, a rewrite of an email may be provided to maximize a positive outcome, and the like.”; Col. 47 Lines 57-67 and Col. 48 Line 1“FIG. 2 illustrates an AIA communication system model, where a user generates a communication as an input to the MA, such as a written electronic text (e.g., email, text message, document, and the like), voice communication (e.g., voice input to a telecommunications system), and the like…generates an output in the form of a modified communication or feedback to the user that is directed at optimizing the effectiveness, clarity, and correctness of the communication…”).
Van Hickman, Kargiannakis and Shevchenko are considered to be analogous to the claimed invention as they both are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van Hickman in view of Kargiannakis to incorporate the teachings of Shevchenko in order to specifically have the content comprise a message in a chat service. Doing so would be beneficial, as this would allow for more clear and effective communication for chat communications between a user and a target audience (Col. 56 Lines 63-67 and Col. 57 Lines 1-20).
Regarding claim 10, claim 10 is rejected for analogous reasons to claim 3.
Regarding claim 17, claim 17 is rejected for analogous reasons to claim 3.
8. Claims 4, 7, 11, 14, and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Van Hickman in view of Kargiannakis and further in view of Miltsakaki (US 2019/0114300 A1).
Regarding claim 4, Van Hickman in view of Kargiannakis does not specifically disclose wherein identifying the complexity level is performed using natural language processing.
Miltsakaki teaches wherein identifying the complexity level is performed using natural language processing (para. 0045 “In some embodiments, machine learning (e.g., the reading level estimation engine 112 and/or the text simplification engine 116) accesses data relating to particular words (e.g., their frequency of use at particular reading levels R1-R4) and their corresponding reading level R1-R4 in the database 114.”; para. 0049 “The reading level estimation engine 112 thus can use the database 114 to help classify the reading level R1-R4 of newly inputted texts 12 based on the content of the text 12. As a simplified example, if the input text 12 contains a high prevalence of the words “victory” and “legal,” and a low prevalence of the words “legislative” and “conquest,” the reading level estimation engine 112 may determine that the text 12 has a high probability of being in the R2 reading level. Accordingly, the system 20 could assign the R2 reading level to the inputted text 12. At this point, the reading level estimation engine 112 has generated an estimated reading level for the input text 12.”).
Van Hickman, Kargiannakis and Miltsakaki are considered to be analogous to the claimed invention as they are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van Hickman in view of Kargiannakis to incorporate the teachings of Miltsakaki in order to specifically identify the complexity level using natural language processing. Doing so would be beneficial, as this would allow a model to access a large corpus of data and learn to accurately classify complexity levels (para. 0051).
Regarding claim 7, Van Hickman in view of Kargiannakis does not specifically disclose wherein generating an explanation includes natural language generation.
Miltsakaki teaches wherein generating an explanation includes natural language generation (para. 0045 “In some embodiments, machine learning (e.g., the reading level estimation engine 112 and/or the text simplification engine 116) accesses data relating to particular words (e.g., their frequency of use at particular reading levels R1-R4) and their corresponding reading level R1-R4 in the database 114.”; para. 0054 “At step 510, the text simplification engine 116 makes a decision as to whether the input text 12 needs to be simplified. For example, if the reading level 14 of the input text 12 is at a lower reading level than the selected target level 18, no text simplification takes place. One or more considerations can be taken into account such as the number of words, grammatical structure, the topic of discussion, etc. In a preferred embodiment, the text simplification engine 116 includes a Deep Neural Network. If the text 12 does not need to be simplified, the control passes to final stage 590. If the text 12 needs to be simplified, however, control passes to steps 520 and 530…”; para. 0064 “At step 590, the simplified sentence is produced and is presented to the user 10.”).
Van Hickman, Kargiannakis and Miltsakaki are considered to be analogous to the claimed invention as they are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van Hickman in view of Kargiannakis to incorporate the teachings of Miltsakaki in order to specifically generate the explanation using natural language processing. Doing so would be beneficial, as this would allow a model to be configured which uses feedback to improve the quality of future simplified texts (para. 0006).
Regarding claim 11, claim 11 is rejected for analogous reasons to claim 4.
Regarding claim 14, claim 14 is rejected for analogous reasons to claim 7.
Regarding claim 18, claim 18 is rejected for analogous reasons to claim 4.
9. Claims 5, 12, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Van Hickman in view of Kargiannakis and further in view of Dodelson et al. (US 2014/0193796 A1, hereinafter Dodelson).
Regarding claim 5, Van Hickman in view of Kargiannakis does not specifically disclose wherein the target skill level and the complexity level each correspond to a particular subject.
Dodelson teaches wherein the target skill level and the complexity level each correspond to a particular subject (target skill level (skill level of a particular user) and complexity level (complexity of a particular content) each correspond to a particular subject: para. 0050 “Upon receiving the entered information, at step 206, the system 100 develops a student profile associated with each user…At step 208, the system 100 assess the skill level of the user(s) in one or more subjects. To perform this step, the system 100 may, for example, deliver a set of question to the user(s) in different subject matters, such as literacy, reading comprehension, vocabulary, and mathematics, and assess a skill level in each subject area based on a predetermined skill-level scale…”; para. 0052 “The system 100 obtains the unmodified content from sources. The unmodified content includes, but is not limited to, textbook excerpts, periodical articles, news articles, literary excerpts, and the like. The unmodified content may come from any source, such as, for example, academic textbook, news sources, library databases, pre-developed lesson databases, and the like.”; para. 0055 “At step 216, the system 100 matches a specific version of the aligned content to a user using the user's pre-assessed skill level(s). The system 100 may modify further the matched aligned content version to increase comprehension of the aligned content version by the specific user. The 100 system matches a version of the aligned content to a user by matching specific areas of learning where the user exhibits a need for improvement, as assessed by the system 100 in step 208.”).
Van Hickman, Kargiannakis, and Dodelson are considered to be analogous to the claimed invention as they are in the same field of natural language processing. Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Van Hickman in view of Kargiannakis to incorporate the teachings of Dodelson in order to specifically have the target skill level and the complexity level each correspond to a particular subject. Doing so would be beneficial, as this would enable aligned content to a user to increase comprehension in a particular subject (para. 0055, para. 0057).
Regarding claim 12, claim 12 is rejected for analogous reasons to claim 5.
Regarding claim 19, claim 19 is rejected for analogous reasons to claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Aggarwal et al. (US 2024/0119220 A1): simplifying text via machine learning (Figs. 3-4)
Bastide et al. (US 2021/0157867 A1): generating summaries of context based on user’s skill (Fig. 2)
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to CODY DOUGLAS HUTCHESON whose telephone number is (703)756-1601. The examiner can normally be reached M-F 8:00AM-5:00PM EST.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Pierre-Louis Desir can be reached at (571)-272-7799. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/CODY DOUGLAS HUTCHESON/Examiner, Art Unit 2659
/BHAVESH M MEHTA/Supervisory Patent Examiner, Art Unit 2656
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