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 .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 10/2/2025 has been entered. Claims 1 – 6 and 8 – 20 are pending; claim 7 has been cancelled.
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 – 6 and 8 – 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Step 1: Is the claimed invention a statutory category of invention?
Claims 1, 9 and 16 are directed to a media and a method of adjusting a textual content to a defined reading level (Step 1, Yes).
Step 2A, Prong 1: Does the claim recite an abstract idea?
The limitation of steps: … receiving a textual content having an initial reading level; receiving a desired reading level that is different from the initial reading level; assigning a part of speech tag to each word in the textual content; identifying one or more candidate replacement words that are a synonym for the words in the textual content, the one or more candidate replacement words limited to the part of speech tag assigned to each word, wherein identifying the candidate replacement words comprises selecting only synonyms that both share the same part of speech as the original word and maintain semantic compatibility with the textual content; determining a reading complexity for the one or more candidate replacement words; generating a candidate replacement text by replacing one or more words in the textual content with one or more replacement words, from the one or more candidate replacement words, wherein the one or more replacement words are inflected to a correct grammatical form based on the part of speech tag assigned to the corresponding one or more candidate replacement words; determining an updated reading level for the candidate replacement text; and based on a determination that the updated reading level is within a threshold of the desired reading level, outputting the candidate replacement text (Claim 1). determining a current reading level for a student; receiving a textual content having an initial reading level; determining a desired reading level for the student that is different from the initial reading level; assigning a part of speech to each word in the textual content; assigning a confidence score to each word in the textual content indicating a strength of classification of the assigned part of speech; identifying one or more candidate replacement words that are a synonym for the word in the textual content, the one or more candidate replacement words limited to the part of speech; excluding at least one of the one or more candidate replacement words for consideration as a target replacement based on the confidence score and based on a complexity score associated with each candidate replacement word, wherein excluding candidate replacement words based on the complexity score comprises retaining only candidate words having a grade-level readability score within a predefined target range; determining a reading complexity for the one or more candidate replacement words; generating a candidate replacement text by replacing the word in the textual content with a replacement word from the one or more candidate replacement words; and outputting the candidate replacement text to the student (Claim 9). receiving a textual content having an initial reading level; determining a desired reading level for a student that is different from the initial reading level; identifying a plurality of candidate replacement words that are a synonym for a word in the textual content; determining a reading complexity for each of the plurality of candidate replacement words; generating a first candidate replacement text by replacing the word in the textual content with a first replacement word from the plurality of candidate replacement words; determining that the first candidate replacement text is not grammatically correct based on inflectional consistency between the replacement word and surrounding words in the textual content, wherein determining grammatical correctness based on inflectional consistency comprises rejecting replacement texts that disrupt morphological agreement of tense or plurality across words in the sentence; determining that the second candidate replacement text is grammatically correct; and outputting the second candidate replacement text to the student (Claim 16) as drafted, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. This type of mental process can be practically performed in the human mind, for instance by a human teach mentally performs each of the claimed steps. Thus, the claim recites a mental process (Step 2A, Prong 1: yes).
Step 2A, Prong 2: Does the claim recite additional elements that integrate the judicial exception into a practical application?
Per the 2019 Revised Patent Subject Matter Eligibility Guidance, if a claim as a whole integrates the recited judicial exception into a practical application of that exception, a claim is not "directed to" a judicial exception. Alternatively, a claim that does not integrate a recited judicial exception into a practical application is directed to the exception. Evaluating whether a claim integrates an abstract idea into a practical application is performed by a) identifying whether there are any additional elements recited in the claim beyond the abstract idea, and b) evaluating those additional elements individual and in combination to determine whether they integrate the abstract idea into a practical application, using one or more of the considerations laid out by the Supreme Court and the Federal Circuit. Exemplary considerations indicative that an additional element (or combination of elements) may have or has not been integrated into a practical application are set forth in the 2019 PEG.
With respect to the instant claims, claim 1 recites the additional elements of: a computing device; one or more computer storage media. It is particularly noted that the use of a computing device "as a tool" to perform an abstract method and steps that only amount to extra solution activity are indicated in the 2019 PEG as examples that an additional element has not been integrated into a practical application. Claims 9 and 16 do not explicitly disclose any statutory product in the claims. Even in combination, the recited additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits, such as an improvement to a computing system, on practicing the abstract idea (STEP 2A, Prong 2: NO).
Step 2B: Does the claim recite additional elements that amount to significantly more than the judicial exception?
Claim 1 recites the additional elements of: a computing device; one or more computer storage media and Claims 9 and 16 do not explicitly disclose any statutory product in the claims set forth above for Step 2A, Prong 2. Regarding these limitations: Applicant's specification only describes these features in a highly generic manner by stating that " Various functions described herein as being performed by one or more entities may be carried out by hardware, firmware, and/or software. For instance, some functions may be carried out by a processor executing instructions stored in memory" in the Applicant’s specification, para. [0034]. Claims 9 and 16 do not require any statutory product to be tied to the claimed method. There is no indication in the Specification that Applicants have achieved an advancement or improvement computing technology. Dependent claims 2 – 6, 8, 10 – 15 and 17 – 20 inherit the deficiencies of their respective parent claims through their dependencies and do not recite additional limitations sufficient to direct the claims to more than the claimed abstract idea, and are thus rejected for the same reasons.
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 1-4 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2015/0310002 A1) in view of Amundsen et al. (US 9,336,204 B1).
Re claim 1:
1. One or more computer storage media comprising computer-executable instructions that when executed by a computing device cause the computing device to perform a method of adjusting a textual content to a defined reading level (Yu, Abstract; [0017], “selectively and dynamically determining the content of comprehension guides to be presented for corresponding words of an electronic book … the meaning of the word as used is ascertained and a lower difficulty same language equivalent or translation of the word is determined”), comprising:
receiving a desired reading level that is different from the initial reading level (Yu, [0043], “this word is above but still near the user's reading level”; [0040], “if the user 102 provides an indication in the user's guide profile that the user 102 is able to read at a ninth-grade reading level, then the selection module 138 may compare this reading level to the classification content 132 of the words of the text 126 of the electronic book 124(N). In this example, the module 138 may render comprehension guides for some or all of the words of the text 126 that are above a ninth-grade reading level”; [0048], “FIG. 2 illustrates displaying relatively few comprehension guides for a relatively higher reading level, in some instances the techniques may display more and/or different comprehension guides for a relatively higher difficulty level”);
assigning a part of speech tag to each word in the textual content (Yu, [0097], “number of meanings or senses of the word or phrase and/or a number of syllables in the word”; [0075], “the word sense determination module 404 may determine the part of speech (e.g., noun, verb, adjective, etc.) within which the instance of the word falls”; [0111], “such constraints may relate to the length of the guide (e.g. size of the terms, images, etc. of the guide), a reading level differential, a number of syllables, etc”; [0031], “the classification content 132 may specify a reading level (e.g., first-grade reading level, second-grade reading level, etc.) of each word … the classification content may indicate a relative frequency of appearance of the words in one or more bodies of writings”; [0100], “number of occurrences of each word throughout the eBook”; each word is tagged with reading level);
identifying one or more candidate replacement words that are a synonym for the word in the textual content, the one or more candidate replacement words limited to the part of speech tag assigned to each word (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”; [0031], “the classification content 132 may specify a reading level (e.g., first-grade reading level, second-grade reading level, etc.) of each word … the classification content may indicate a relative frequency of appearance of the words in one or more bodies of writings”; [0100], “number of occurrences of each word throughout the eBook”; each word is tagged with reading level), wherein identifying the candidate replacement words comprises selecting only synonyms that both share the same part of speech as the original word and maintain semantic compatibility with the textual content (Yu teaches a method providing a word replacement based on the meaning in a language, word order (syntax) or verb form. Specifically, the word sense determination module operates to determine the use or sense of the words of the text (Yu, [0072]). The word sense determination module may use functionality that analyzes surrounding words to determine the use or sense of words … The word sense determination module determination module 404 may determine the part of speech (e.g., noun, verb, adjective, etc.) within which the instance of the word falls (Yu, [0075]). Comprehension guides can present synonyms and/or definitions of the corresponding words and/or phrases of the text (Yu, [0019]) and the corresponding words or phrase is selected based on its’ meaning);
determining a reading complexity for the one or more candidate replacement words (Yu, [0088]; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase “full of.””; [0090]; [0020], “the complex word “Greek fire” may be “incendiary weapon” which includes the terms, incendiary and weapon”);
generating a candidate replacement text by replacing the word in the textual content with a replacement word from the one or more candidate replacement words (Yu, [0088] – [0091]);
determining an updated reading level for the candidate replacement text; and outputting the candidate replacement text (Yu, [0034], “the selection module 138 may determine a reading level at which to render the electronic book 124(N) based on the user's guide profile”; [0049], “the device 104(1) renders each comprehension guide 110(1)-(3) above the corresponding word 108(1)-(3)”; [0055], “increase or decrease the reading level of the guide profiles 202(1) and 202(2) for the purpose of showing more or fewer comprehension guides”).
Yu does not explicitly disclose
assigning a part of speech tag to each word in the textual content;
… generating a candidate replacement text by replacing one or more words in the textual content with one or more replacement words, from the one or more candidate replacement words, wherein the one or more replacement words are inflected to a correct grammatical form based on the part of speech tag assigned to the corresponding one or more candidate replacement words;
Amundsen et al. (US 9,336,204 B1) teaches delivering electronic literary content in a way that adjusts the reading level of the content for the reader while maintaining the overall story context (Amundsen, Abstract).
identifying one or more candidate replacement words that are a synonym for the word in the textual content, the one or more candidate replacement words limited to the part of speech tag assigned to each word, wherein identifying the candidate replacement words comprises selecting only synonyms that both share the same part of speech as the original word and maintain semantic compatibility with the textual content;
Amundsen further teaches
receiving a textual content having an initial reading level (Amundsen, col. 1, lines 45 – 53, “adjusted to the reading level of a beginning reader”);
assigning a part of speech tag to each word in the textual content (Amundsen, col. 5, lines 16 – 25, “delivery of a new version of the content may correspond to the reader selecting words, phrases, or entire passages for modification”; col. 6, lines 14 – 23, “words and phrases and their alternatives are each tagged with a corresponding reading level”; col. 2, lines 27 – 45, “words in a dictionary may be tagged with a corresponding reading level. A reading level might also be defined by selecting particular rules relating to grammar, syntax, sentence structure, etc.”);
identifying one or more candidate replacement words that are a synonym for the words in the textual content, the one or more candidate replacement words limited to the part of speech tag assigned to each word (Amundsen, col. 6, lines 14 – 24, “modification of the content may involve replacement of particular words or phrases with more or less sophisticated synonyms or synonymous phrases”; col. 5, line 56 – col. 6, line 13, “Suitable modifications (e.g., word or phrase replacements) corresponding to the second reading level are then identified with reference to the one or more resources (304)”; col. 7, lines 27 – 36, “word or phrase replacement, grammar and syntax simplification”), wherein identifying the candidate replacement words comprises selecting only synonyms that both share the same part of speech as the original word and maintain semantic compatibility with the textual content (Amundsen, col. 6, lines 1 – 20, “Suitable modifications ( e.g., word or phrase replacements) corresponding to the second reading level are then identified with reference to the one or more 10 resources (304). The modifications are then made such that a new version of the content is created that corresponds to the second reading level (306) … modification of the content may involve 15 replacement of particular words or phrases with more or less sophisticated synonyms or synonymous phrases”; cols. 8 - 9, claim 4 and claim 12, “to identify associations among the first words and the first sentence structures … modifying the first version of the literary content by replacing the first words … modifying the first version of the literary content comprises one or more of replacing words with synonyms”; replacing a word or phrase with a synonym (or a phrase) in sentence while maintain the meaning of the sentence. The synonym (or synonym phrase) in inherently compatible with the literacy content or sentence with its’ similar word meanings);
determining a reading complexity for the one or more candidate replacement words (Amundsen, col. 2, lines 27 – 45, “particular words in a dictionary may be tagged with a corresponding reading level. A reading level might also be defined by selecting particular rules relating to grammar, syntax, sentence structure, etc.”; col. 5, line 56 – col. 6, line 13, “Suitable modifications (e.g., word or phrase replacements) corresponding to the second reading level are then identified with reference to the one or more resources (304)”);
generating a candidate replacement text by replacing one or more words in the textual content with one or more replacement words, from the one or more candidate replacement words, wherein the one or more replacement words are inflected to a correct grammatical form based on the part of speech tag assigned to the corresponding one or more candidate replacement words (Amundsen, col. 1, lines 33 – 44, “The adjustments involve modification of the literary content and may take a variety of forms such as, for example, replacing particular words with synonymous words or phrases, modifying grammar and/or syntax, adding or eliminating portions of the content”; col. 6, lines 14 – 23, “words and phrases and their alternatives are each tagged with a corresponding reading level”; col. 2, lines 27 – 45, “particular words in a dictionary may be tagged with a corresponding reading level. A reading level might also be defined by selecting particular rules relating to grammar, syntax, sentence structure, etc.”; 6, lines 14 – 24, “modification of the content may involve replacement of particular words or phrases with more or less sophisticated synonyms or synonymous phrases”; col. 5, line 56 – col. 6, line 13, “Suitable modifications (e.g., word or phrase replacements) corresponding to the second reading level are then identified with reference to the one or more resources (304)”; col. 7, lines 27 – 36, “word or phrase replacement, grammar and syntax simplification”).
Therefore, in view of Amundsen, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the computer program/ method described in Yu, by providing the initial reading level as taught by Amundsen, since a child might have a favorite book written at the reading level of a beginning reader. As the child gets older, she may still wish to read the book but, because the reading level is so low, the book no longer provides a sufficient challenge to help the child improve her reading skills. However, if the reading level of the book is adjusted to that of a more advanced reader, the child may continue to read her favorite story while being challenged by a reading level that will help her to continue to improve her reading skills (Amundsen, col. 1, lines 54 - 62). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the computer program/ method described in Yu, by modifying grammar and syntax as taught by Amundsen, since the grammar and syntax can be modified based on the reading level; literary genre; historical period; writing style or voice (Amundsen, col. 6, lines 14 – 32; col. 7, lines 27 - 36). It would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the computer program/ method described in Yu, by tagging each word as taught by Amundsen, since word may be tagged with a corresponding reading level. A reading level might also be defined by selecting particular rules relating to grammar, syntax, sentence structure, etc (Amundsen, col. 2, lines 27 - 45).
Re claim 2:
2. The media of claim 1, wherein the part of speech tag is selected from a group consisting of a noun, adjective, and verb (Yu, [0090]; [0120]).
Re claim 3:
3. The media of claim 1, wherein the replacement word is selected based on the desired reading level and the reading complexity of the replacement word (Yu, [0088]; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase “full of.””; [0090]; [0020], “the complex word “Greek fire” may be “incendiary weapon” which includes the terms, incendiary and weapon”).
Re claim 4:
4. The media of claim 1, wherein the method further comprises determining an amount of words in the textual content to replace based on a difference between the initial reading level and the desired reading level (Yu, [0097], “ranking or score for words based on non-contextual factors such as frequency of occurrence of the word in a large corpus, word length, number of meanings or senses of the word or phrase and/or a number of syllables in the word”; [0092], “constraints may include the length of the guide, a maximum number of words, a maximum reading level differential or any other limit that the user of the system wishes”; [0101], “such as a subset of words for which to display corresponding comprehension guides for a particular number of instances of the words”).
Re claim 8:
8. The media of claim 1, wherein the method further comprises, prior to the outputting, determining that the candidate replacement text is grammatical (Yu, [0091], “the guide selection module 504 may utilize a lexical database to determine suitable comprehension guides. For example, an English lexical
database may include nouns, verbs, adjectives and adverbs grouped into sets of cognitive synonym”; Yu uses proper lexical (nouns, verb, adjective … grammar) to determine a synonym; Amundsen, col. 1, lines 33 – 44, “The adjustments involve modification of the literary content and may take a variety of forms such as, for example, replacing particular words with synonymous words or phrases, modifying grammar and/or syntax, adding or eliminating portions of the content”; col. 6, lines 14 – 23, “words and phrases and their alternatives are each tagged with a corresponding reading level”; col. 2, lines 27 – 45, “particular words in a dictionary may be tagged with a corresponding reading level. A reading level might also be defined by selecting particular rules relating to grammar, syntax, sentence structure, etc.”; 6, lines 14 – 24; col. 5, line 56 – col. 6, line 13; col. 7, lines 27 – 36, “word or phrase replacement, grammar and syntax simplification”).
Claim 5 is are rejected under 35 U.S.C. 103 as being unpatentable over Yu and Amundsen as applied to claim 1 above, and further in view of Alison (“How To Analyze Running Records (And Get a Ton of Valuable Information About Your Beginning Readers!)” by Alison, https://learningattheprimarypond.com/blog/how-to-analyze-running-records/, retrieved from Internet Wayback machine, 11/19/2017).
Re claim 5:
Yu does not explicitly disclose MSV score. Alison teaches a method for analyzing running records for a reader. Alison further teaches: 5. The media of claim 1, wherein the desired reading level is determined based on a meaning, structural, and visual cues (MSV) score of an intended reader (Alison, pg. 5, “Cueing Systems (M-S-V)”; pg. 7, “… Appealing for help … Rerunning (going back and rereading during tricky parts … Self correcting”). Therefore, in view of Alison, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the computer program / method described in Yu, by providing the MSV and reading habit (i.e., self correcting) as taught by Alison, in order to monitor a reader’s word reading habits and general reading behaviors (Alison, pg. 7). A reader is self-monitoring to make sure that what he/she reads looks right, sounds right, and makes sense … self-corrects is likely doing a really good job of monitoring (Alison, pg. 10).
Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Yu and Amundsen as applied to claim 1 above, and further in view of Wasowicz et al. (US 2002/0164563 A1).
Re claim 6:
Yu teaches determining the desired reading level using the reading competency score and threshold increase (Yu, [0034], “a low reading level may be indicative of a lower difficulty threshold for selection of words for comprehension guides and a high reading level may be indicative of a higher difficulty threshold for selection of words for comprehension guides”; [0043], “Furthermore, the module 138 may also identify words having classified reading levels that are a threshold amount above the user's reading level”). Yu does not explicitly disclose audio recording of the student.
Wasowicz teaches a diagnostic system and method for evaluating reading skill of the student (Wasowicz, Abstract). Wasowicz teaches 6. The media of claim 1, wherein the method further comprises:
outputting a text for display; receiving audio data of the text being read orally by a student (Wasowicz, fig. 23; [0127], “the module may present a spoken word and a picture of the item in step 280 and query the user about which item in a sequence of items”; [0130]);
converting the audio data to converted text (Wasowicz, fig. 23; [0111]);
identifying an error in the converted text by detecting a difference between the converted text the text (Wasowicz, [0111], “speak the name of each item into a microphone that is interpreted by the speech recognition software in the client computer, transmitted to the server and compared to a correct response by the speech recognition software in the server so that the scorer may determine whether or not the child correctly identified each item”; [0126]; [0013], [0127], “If the response is incorrect, the module may determine the number of consecutive errors for the particular ending sound in step 288, compare the calculated number to a predetermined number in step 290 and display a next word”);
classifying, with a machine classifier, the error into an error category (Wasowicz, fig. 29, 740; [0056], “describe the different sounds units types, syllable types and phoneme types that may be tested using the diagnostic system since these types of sound units, syllables and phonemes are similar to the types of sound units, syllables and phonemes used in the training tools”; [0098], “indexes are then incremented as described below to analyze each incorrect response for each subtest wherein each incorrect response is compared to each error measure to determine the type of error”; [0099]);
generating a reading competency score using the error category (Wasowicz, [0097], “example, two of the incorrect responses indicate the same type of error (for example, an open syllable rime error) and one indicates a different type of error (for example, a r-controlled vowel rime). In this manner, the data about the particular incorrect responses by the user stored in the database are mapped into the types of errors that are shown by the particular incorrect answer”; [0098], “indexes are then incremented as described below to analyze each incorrect response for each subtest wherein each incorrect response is compared to each error measure to determine the type of error”; [0099]).
Therefore, in view of Wasowicz, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the media and method described in Yu, by assessing the verbal ability of the student as taught by Wasowicz, in order to provide diagnostic system and method for evaluating one or more phonological awareness, phonological processing and reading skills of an individual to detect phonological awareness, phonological processing and reading skill deficiencies in the individual so that the risk of developing a reading deficiency is reduced and existing reading deficiencies are remediated. The system may use graphical games to test the individual's ability in a plurality of different phonological awareness, phonological processing and reading skills. The system may use speech recognition technology to interact with the games. The system may include a module for providing motivation to a user of the system being tested (Wasowicz, Abstract).
Claims 9-13 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2015/0310002 A1) in view of Satterfield et al. (US 2020/0342164 A1).
Re claim 9
Yu teaches 9. A method of adjusting a textual content to a defined reading level (Yu, Abstract; [0017], “selectively and dynamically determining the content of comprehension guides to be presented for corresponding words of an electronic book … the meaning of the word as used is ascertained and a lower difficulty same language equivalent or translation of the word is determined”) comprising:
determining a current reading level for a student (Yu, [0043], “this word is above but still near the user's reading level”; [0040], “if the user 102 provides an indication in the user's guide profile that the user 102 is able to read at a ninth-grade reading level”; [0048], “higher difficulty level”);
receiving a textual content having an initial reading level (Yu, [0018], “electronic books can include all forms of textual information such as books, magazines, newspapers, newsletters, periodicals, journals, reference materials, telephone books, textbooks”; [0060], “create or otherwise prepare e-books in a format similar”; [0022], “a measure of the difficulty of the words of the book”; [0030], “one or more words in the electronic book, a difficulty level of the words specified in terms of frequency of use, reading level, age, or the like”);
determining a desired reading level for the student that is different from the initial reading level (Yu, [0043], “this word is above but still near the user's reading level”; [0040], “if the user 102 provides an indication in the user's guide profile that the user 102 is able to read at a ninth-grade reading level, then the selection module 138 may compare this reading level to the classification content 132 of the words of the text 126 of the electronic book 124(N). In this example, the module 138 may render comprehension guides for some or all of the words of the text 126 that are above a ninth-grade reading level”; [0048], “FIG. 2 illustrates displaying relatively few comprehension guides for a relatively higher reading level, in some instances the techniques may display more and/or different comprehension guides for a relatively higher difficulty level”);
assigning a part of speech to each word in the textual content (Yu, [0097], “number of meanings or senses of the word or phrase and/or a number of syllables in the word”; [0075], “the word sense determination module 404 may determine the part of speech (e.g., noun, verb, adjective, etc.) within which the instance of the word falls”; [0111], “such constraints may relate to the length of the guide (e.g. size of the terms, images, etc. of the guide), a reading level differential, a number of syllables, etc”);
identifying one or more candidate replacement words that are a synonym for the word in the textual content, the one or more candidate replacement words limited to the part of speech (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”);
excluding at least one of the one or more candidate replacement words for consideration as a target replacement based on a complexity score associated with each candidate replacement word, wherein excluding candidate replacement words based on the complexity score comprises retaining only candidate words having a grade-level readability score within a predefined target range (Yu, [0016], “comprehension guides for words within the electronic book based on a reading level of the reader”; [0030], “for one or more words in the electronic book, a difficulty level of the words specified in terms … reading level”; [0031], “classification content 132 may specify a reading level (e.g., first-grade reading level, second-grade reading level, etc.) of each word”; [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced”; Yu teaches a word can be replaced based on the reading level of the reader (i.e., first, second, third reading level)).
determining a reading complexity for the one or more candidate replacement words (Yu, [0088]; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase “full of.””; [0090]; [0020], “the complex word “Greek fire” may be “incendiary weapon” which includes the terms, incendiary and weapon”);
generating a candidate replacement text by replacing the word in the textual content with a replacement word from the one or more candidate replacement words (Yu, [0088] – [0091]); and
outputting the candidate replacement text to the student (Yu, [0034], “the selection module 138 may determine a reading level at which to render the electronic book 124(N) based on the user's guide profile”; [0049], “the device 104(1) renders each comprehension guide 110(1)-(3) above the corresponding word 108(1)-(3)”; [0055], “increase or decrease the reading level of the guide profiles 202(1) and 202(2) for the purpose of showing more or fewer comprehension guides”).
Yu does not explicitly disclose
assigning a part of speech to each word in the textual content;
assigning a confidence score to each word in the textual content indicating a strength of classification of the assigned part of speech;
….
excluding at least one of the one or more candidate replacement words for consideration as a target replacement based on the confidence score;
Satterfield et al. (US 2020/0342164 A1) teaches an electronic input document presented on a display of a client is examined to identify a text unit in the electronic input document and contextual information about the input document (Satterfield, Abstract).
assigning a part of speech to each word in the textual content (Satterfield, [0065], “for each text”; fig. 8, 804 - “Determine a set of annotations for the text unit and the input document responsive to the contextual information”; [0046], “Annotations may pertain to documents as a whole, and may also pertain to particular text units within the documents”);
assigning a confidence score to each word in the textual content indicating a strength of classification of the assigned part of speech (Satterfield, [0066], “confidence score”);
excluding at least one of the one or more candidate replacement words for consideration as a target replacement based on the confidence score (Satterfield, [0057], “The text identification module 314 may also determine a minimum threshold for the confidence scores, and discard candidate texts that have confidence scores below the minimum threshold for use as suggested text”; [0063], “the document analysis module 130 may identify text units associated with candidate texts having confidence scores above a threshold amount”; [0066]).
Therefore, in view of Satterfield, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Yu, by providing the confidence score, since the text identification module determine a minimum threshold for the confidence scores, and discard candidate texts that have confidence scores below the minimum threshold for use as suggested text (Satterfield, [0057]).
Re claim 10:
10. The method of claim 9, further comprising determining an updated reading level for the candidate replacement text (Yu, [0034], “the selection module 138 may determine a reading level at which to render the electronic book 124(N) based on the user's guide profile”; [0049], “the device 104(1) renders each comprehension guide 110(1)-(3) above the corresponding word 108(1)-(3)”; [0055], “increase or decrease the reading level of the guide profiles 202(1) and 202(2) for the purpose of showing more or fewer comprehension guides”).
Re claim 11:
11. The method of claim 9, further comprising, prior to the outputting, determining an updated reading level for the candidate replacement text and determining that the updated reading level is within a threshold of the desired reading level (Yu, [0034], “a low reading level may be indicative of a lower difficulty threshold for selection of words for comprehension guides and a high reading level may be indicative of a higher difficulty threshold for selection of words for comprehension guides”; [0043], “Furthermore, the module 138 may also identify words having classified reading levels that are a threshold amount above the user's reading level”).
Re claim 12:
12. The method of claim 9, further comprising, prior to the outputting, determining that the candidate replacement text is grammatical (Yu, [0091], “the guide selection module 504 may utilize a lexical database to determine suitable comprehension guides. For example, an English lexical
database may include nouns, verbs, adjectives and adverbs grouped into sets of cognitive synonym”; Yu uses proper lexical (nouns, verb, adjective … grammar) to determine a synonym).
Re claim 13:
13. The method of claim 9, further comprising determining an amount of words in the textual content to replace based on a difference between the initial reading level and the desired reading level (Yu, [0097], “ranking or score for words based on non-contextual factors such as frequency of occurrence of the word in a large corpus, word length, number of meanings or senses of the word or phrase and/or a number of syllables in the word”; [0092], “constraints may include the length of the guide, a maximum number of words, a maximum reading level differential or any other limit that the user of the system wishes”; [0101], “such as a subset of words for which to display corresponding comprehension guides for a particular number of instances of the words”).
Re claim 15:
15. The method of claim 9, wherein the part of speech is selected from a group consisting of a noun, adjective, and verb (Yu, [0090]; [0120]).
Claim 14 is rejected under 35 U.S.C. 103 as being unpatentable over Yu and Satterfield as applied to claim 9 above, and further in view of Alison (“How To Analyze Running Records (And Get a Ton of Valuable Information About Your Beginning Readers!)” by Alison, https://learningattheprimarypond.com/blog/how-to-analyze-running-records/, retrieved from Internet Wayback machine, 11/19/2017).
Re claim 14:
Yu does not explicitly disclose MSV score. Alison teaches a method for analyzing running records for a reader. Alison further teaches: 14. The method of claim 9, wherein the current reading level for the student is based on a MSV score (Alison, pg. 5, “Cueing Systems (M-S-V)”; pg. 7, “… Appealing for help … Rerunning (going back and rereading during tricky parts … Self correcting”). Therefore, in view of Alison, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the computer program / method described in Yu, by providing the MSV and reading habit (i.e., self correcting) as taught by Alison, in order to monitor a reader’s word reading habits and general reading behaviors (Alison, pg. 7). A reader is self-monitoring to make sure that what he/she reads looks right, sounds right, and makes sense … self-corrects is likely doing a really good job of monitoring (Alison, pg. 10).
Claims 16-19 are rejected under 35 U.S.C. 103 as being unpatentable over Yu et al. (US 2015/0310002 A1) in view of Choi (US 2016/0196257 A1).
Re claim 16:
Yu teaches 16. A method of adjusting a textual content to a defined reading level (Yu, Abstract; [0017], “selectively and dynamically determining the content of comprehension guides to be presented for corresponding words of an electronic book … the meaning of the word as used is ascertained and a lower difficulty same language equivalent or translation of the word is determined”) comprising:
receiving a textual content having an initial reading level (Yu, [0043], “this word is above but still near the user's reading level”; [0040], “if the user 102 provides an indication in the user's guide profile that the user 102 is able to read at a ninth-grade reading level, then the selection module 138 may compare this reading level to the classification content 132 of the words of the text 126 of the electronic book 124(N). In this example, the module 138 may render comprehension guides for some or all of the words of the text 126 that are above a ninth-grade reading level”; [0048], “FIG. 2 illustrates displaying relatively few comprehension guides for a relatively higher reading level, in some instances the techniques may display more and/or different comprehension guides for a relatively higher difficulty level”);
determining a desired reading level for a student that is different from the initial reading level (Yu, [0043], “this word is above but still near the user's reading level”; [0040], “if the user 102 provides an indication in the user's guide profile that the user 102 is able to read at a ninth-grade reading level, then the selection module 138 may compare this reading level to the classification content 132 of the words of the text 126 of the electronic book 124(N). In this example, the module 138 may render comprehension guides for some or all of the words of the text 126 that are above a ninth-grade reading level”; [0048], “FIG. 2 illustrates displaying relatively few comprehension guides for a relatively higher reading level, in some instances the techniques may display more and/or different comprehension guides for a relatively higher difficulty level”);
identifying a plurality of candidate replacement words that are a synonym for a word in the textual content (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”);
determining a reading complexity for each of the plurality of candidate replacement words (Yu, [0088]; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase “full of.””; [0090]; [0020], “the complex word “Greek fire” may be “incendiary weapon” which includes the terms, incendiary and weapon”; [0031], “the classification content 132 may specify a reading level (e.g., first-grade reading level, second-grade reading level, etc.) of each word … the classification content may indicate a relative frequency of appearance of the words in one or more bodies of writings”; [0100], “number of occurrences of each word throughout the eBook”; each word is tagged with reading level);
generating a first candidate replacement text by replacing the word in the textual content with a first replacement word from the plurality of candidate replacement words (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase "full of."”; [0090]);
determining that the first candidate replacement text (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase "full of."”; [0090]);
generating a second candidate replacement text by replacing the word in the textual content with a second replacement word from the plurality of candidate replacement words (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase "full of."”; [0090]);
determining that the second candidate replacement text (Yu, [0088], “the guide selection module 406 may look for other words which may have a lower difficulty level than the word being replaced. The difficulty level for the words may be determined in a manner similar to that discussed above for the general word ranking module 402, the contextual ranking module 406 and/or the user-based ranking module 408”; [0090], “determining a synonym for the word "zucchini,"”; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase "full of."”; [0090]); and
outputting the second candidate replacement text to the student (Yu, [0034], “the selection module 138 may determine a reading level at which to render the electronic book 124(N) based on the user's guide profile”; [0049], “the device 104(1) renders each comprehension guide 110(1)-(3) above the corresponding word 108(1)-(3)”; [0055], “increase or decrease the reading level of the guide profiles 202(1) and 202(2) for the purpose of showing more or fewer comprehension guides”).
Yu does not explicitly disclose determining that the first candidate replacement text is not grammatically correct … determining that the second candidate replacement text is grammatically correct;
Choi (US 2016/0196257 A1) teaches a grammar correcting method is provided, the method including receiving a sentence generated based on speech recognition, receiving information associated with a speech recognition result of the sentence and correcting grammar in the sentence based on the information associated with the speech recognition results of the sentence. Choi teaches generating a first candidate replacement text by replacing the word in the textual content with a first replacement word from the plurality of candidate replacement words; determining that the first candidate replacement text is not grammatically correct based on inflectional consistency between the replacement word and surrounding words in the textual content, wherein determining grammatical correctness based on inflectional consistency comprises rejecting replacement texts that disrupt morphological agreement of tense or plurality across words in the sentence; generating a second candidate replacement text by replacing the word in the textual content with a second replacement word from the plurality of candidate replacement words; determining that the second candidate replacement text is grammatically correct (Choi, [0050]; [0059]; [0064]; [0071], “The verifier 114 may detect candidate words associated with a grammatical error by verifying grammar in the speech recognized sentence 720. For example, the verifier 11 may detect “good” as a first candidate word associated with a grammatical error and detect “need” as a second candidate word associated with a grammatical error”; [0010]; [0076], “a determination in which the plurality of candidate words are not grammatically associated with each other, a single word associated with the grammatical error is corrected. For example, when a sentence of "she love her cat" is recognized, a word associated with the grammatical error may be a single word of "love." In such an example, "love" is corrected to "loves."”; [0050], “the verifier 114 may detect the singular noun and the plural verb based on the plurality of candidate words associated with the grammatical error”; fig. 6B; fig. 9). Therefore, in view of Choi, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method described in Yu, by checking grammar error as taught by Choi, since it was known in the art to provide sentence with proper grammar to the student (Choi, [0064], “the verifier 114 may detect candidate words associated with a grammatical error by verifying grammar in the speech recognized sentence 620”).
Re claim 17:
17. The method of claim 16, wherein the method further comprises, prior to the outputting, determining an updated reading level for the second candidate replacement text and determining that the updated reading level is within a threshold of the desired reading level (Yu, [0034], “a low reading level may be indicative of a lower difficulty threshold for selection of words for comprehension guides and a high reading level may be indicative of a higher difficulty threshold for selection of words for comprehension guides”; [0043], “Furthermore, the module 138 may also identify words having classified reading levels that are a threshold amount above the user's reading level”).
Re claim 18:
18. The method of claim 16, wherein the second replacement word is selected based on the desired reading level and the reading complexity of the second replacement word (Yu, [0088]; [0089], “definition of 'joyful" given above, the alternative phrase "characterized by" is more difficult than the alternative phrase “full of.””; [0090]; [0020], “the complex word “Greek fire” may be “incendiary weapon” which includes the terms, incendiary and weapon”).
Re claim 19:
19. The method of claim 16, wherein the method further comprises determining the desired reading level using a reading competency score for the student and threshold increase (Yu, [0034], “the selection module 138 may determine a reading level at which to render the electronic book 124(N) based on the user's guide profile”; [0049], “the device 104(1) renders each comprehension guide 110(1)-(3) above the corresponding word 108(1)-(3)”; [0055], “increase or decrease the reading level of the guide profiles 202(1) and 202(2) for the purpose of showing more or fewer comprehension guides”; [0024]; [0029]; [0035]).
Claim 20 is rejected under 35 U.S.C. 103 as being unpatentable over Yu and Choi as applied to claims 19 above, and further in view of Alison (“How To Analyze Running Records (And Get a Ton of Valuable Information About Your Beginning Readers!)” by Alison, https://learningattheprimarypond.com/blog/how-to-analyze-running-records/, retrieved from Internet Wayback machine, 11/19/2017).
Re claim 20:
Yu does not explicitly disclose MSV score. Alison teaches a method for analyzing running records for a reader. Alison further teaches: 20. The method of claim 19, wherein the reading competency score for the student is based on a MSV score (Alison, pg. 5, “Cueing Systems (M-S-V)”; pg. 7, “… Appealing for help … Rerunning (going back and rereading during tricky parts … Self correcting”). Therefore, in view of Alison, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to modify the computer program / method described in Yu, by providing the MSV and reading habit (i.e., self correcting) as taught by Alison, in order to monitor a reader’s word reading habits and general reading behaviors (Alison, pg. 7). A reader is self-monitoring to make sure that what he/she reads looks right, sounds right, and makes sense … self-corrects is likely doing a really good job of monitoring (Alison, pg. 10).
Response to Arguments
Applicant's arguments filed 10/2/2025 have been fully considered but they are not persuasive.
Applicant argues:
Yu describes modifying reading passages based on user feedback to adjust comprehension difficulty but does not disclose synonym selection that is constrained by part-of-speech equivalence. Yu's focus is on leveraging outcome-based feedback to guide text modification, not on grammatical classification or synonym filtering based on part of speech.
Amundsen similarly discloses simplifying literary works to different reading levels, but it does not disclose the additional requirement of ensuring both grammatical and semantic consistency at the word level. While Amundsen may involve substitution of simpler words or phrases, such substitution is not limited to synonyms sharing a grammatical category, nor is it guided by a semantic compatibility check with the surrounding text.
The newly added limitation introduces a two-pronged filtering constraint: first, by requiring synonym replacements to share the same grammatical classification, and second, by requiring that the replacement maintain semantic compatibility with the textual content. This combination ensures both syntactic correctness and contextual fidelity in the modified text. Neither Yu nor Amundsen teaches or suggests such a restriction, nor would their combined teachings render it obvious. Accordingly, the combination of Yu and Amundsen fails to disclose or suggest the subject matter of amended claim 1, and withdrawal of the §103 rejection is respectfully requested.
By definition: grammar classification (selecting only synonyms that both share the same part of speech) is the system of rules for how words are put together to form sentences and communicate meaning in a language, covering everything from word order (syntax) to verb forms, ensuring clarity and understanding. Semantic by definition: relating to meaning in language. The semantic compatibility can be interpreted as logical, meaningful, and contextual alignment of words, ensuring that subjects, verbs, and objects make sense together in the textual content. Hence, the two constraints: part of speech and semantic compatibility) overlap in scope or require similar criteria.
According to MPEP 2111 [R-5], during patent examination, the pending claims must be “given their broadest reasonable interpretation consistent with the specification.” The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005) expressly recognized that the USPTO employs the “broadest reasonable interpretation” standard. Yu teaches a method providing a word replacement based on the meaning in a language, word order (syntax) or verb form. Specifically, the word sense determination module operates to determine the use or sense of the words of the text (Yu, [0072]). The word sense determination module may use functionality that analyzes surrounding words to determine the use or sense of words … The word sense determination module determination module 404 may determine the part of speech (e.g., noun, verb, adjective, etc.) within which the instance of the word falls (Yu, [0075]). Comprehension guides can present synonyms and/or definitions of the corresponding words and/or phrases of the text (Yu, [0019]) and the corresponding words or phrase is selected based on its’ meaning.
Amundsen also teaches a replacement word is selected based on grammar, syntax and context in a sentence or a passage (Amundsen, col. 6, lines 14 – 32, “replacement of particular words or phrases with more or less sophisticated synonyms or synonymous phrases. This may be achieved using dictionaries and/or thesauruses (e.g., stored in data store 112 or a separate data store) in which words and phrases and their alternatives are each tagged with a corresponding reading level”; col. 2, lines 27 – 45, “this sophistication may be represented by a variety of characteristics of the literary content including, for example, vocabulary, grammar, syntax, sentence structure …”; col. 5, lines 26 – 42, “the user's interaction with content might indicate that a vocabulary and sentence structure corresponding to an intermediate-level reader is appropriate”).
Applicant argues:
Yu describes modifying text passages in response to user comprehension feedback, but Yu does not disclose excluding candidate synonyms using a combination of confidence and complexity scoring. Yu's disclosure focuses on adapting passages at the content level rather than applying multi-factor filtering to candidate replacement words.
Satterfield discloses techniques for identifying simpler alternatives to difficult words, but the disclosure is limited to identifying replacements and does not disclose scoring candidates based on confidence or grade-level readability, let alone excluding words that fall outside a predefined readability range. Satterfield' s approach is more general and does not introduce quantitative thresholds to control synonym substitution.
The Office disagrees. According to MPEP 2111 [R-5], during patent examination, the pending claims must be “given their broadest reasonable interpretation consistent with the specification.” The Federal Circuit’s en banc decision in Phillips v. AWH Corp., 415 F.3d 1303, 75 USPQ2d 1321 (Fed. Cir. 2005) expressly recognized that the USPTO employs the “broadest reasonable interpretation” standard. Yu teaches a comprehension guides for words that filter word based on the reading level of the reader. The reading level of the reader include the complexity of the word (for example, Yu, [0016], “comprehension guides for words within the electronic book based on a reading level of the reader”; [0030], “for one or more words in the electronic book, a difficulty level of the words specified in terms … reading level”; [0031], “classification content 132 may specify a reading level (e.g., first-grade reading level, second-grade reading level, etc.) of each word”). The words selected in the study guide may be filtered to remove words that, although frequently used within the electronic book 124(N), are one or more of: below the user's reading level; previously indicated by the user 102 as not to be given in a comprehension guide (e.g., the user 102 knows the word); have a low rankings or evaluations of the words or based on some other filter (Yu, [0029]). Yu teaches a word can be replaced based on the reading level of the reader (i.e., first, second, third reading level).
Applicant argues:
Yu describes modifying textual passages in response to user comprehension feedback, but Yu does not disclose or suggest evaluating grammatical correctness at the level of inflectional consistency. Yu's system is concerned with tailoring comprehension difficulty, not with enforcing morphological agreement across words in a sentence.
Sharifi similarly fails to disclose this feature. Sharifi is directed to synonym selection and vocabulary substitution, but it does not address grammatical correctness in terms of maintaining inflectional agreement such as tense or plurality. While Sharifi may discuss ensuring substitutions are contextually appropriate, it does not teach rejecting candidates that would disrupt morphological agreement in surrounding text.
The examiner submits that Choi teaches the newly added limitations: Choi (US 2016/0196257 A1) teaches a grammar correcting method is provided … determining that the first candidate replacement text is not grammatically correct based on inflectional consistency between the replacement word and surrounding words in the textual content, wherein determining grammatical correctness based on inflectional consistency comprises rejecting replacement texts that disrupt morphological agreement of tense or plurality across words in the sentence (Choi, [0076], “a determination in which the plurality of candidate words are not grammatically associated with each other, a single word associated with the grammatical error is corrected. For example, when a sentence of "she love her cat" is recognized, a word associated with the grammatical error may be a single word of "love." In such an example, "love" is corrected to "loves."”; [0050], “the verifier 114 may detect the singular noun and the plural verb based on the plurality of candidate words associated with the grammatical error”; fig. 6B; fig. 9). The word “love” has been modified based on the inflectional consistency and morphological agreement (i.e., tense, singular noun… etc.) of the noun “she” or a singular noun in the sentence.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JACK YIP whose telephone number is (571)270-5048. The examiner can normally be reached Monday thru Friday; 9:00 AM - 5:00 PM 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, XUAN THAI can be reached at (571) 272-7147. 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.
/JACK YIP/Primary Examiner, Art Unit 3715