DETAILED ACTION
This office action is responsive to communication(s) filed on 9/26/2025.
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 .
Claims’ Status
Claims 1-5 and 7-13 are pending.
Claim 1 is independent.
Claims 1-5 and 7-12 are being examined.
Claim 6 is previously canceled.
Claim 13 is withdrawn as being drawn to a non-elected invention.
Claim 1 is newly amended.
Claim Rejections - 35 USC § 103
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-2 and 7-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta; Prabhakar (hereinafter Gupta – US 10936813 B1) in view of Dubs; Justin Tyler et al. (hereinafter Dubs – US 20100251105 A1) and Xu; Yixing et al. (hereinafter Xu – US 20220327363 A1).
Independent Claim 1:
Gupta teaches A computer-implemented method for generating and displaying a recommendation for modification of user input, the method comprising
receiving, […] a[n] […] application executing on a computing device, user input representing a first word entered by a user of the computing device, (at 502, process receives digital content, e.g., text data, upon submission by a user or automatically an in real time as the user types [entered by a user], col 18:3-8 and fig. 5. The digital content may include sentences of text and may contain multiple tokens, i.e., words, col 3:13-34. The digital content may include a word [a first word] of the multiple words that may have a spelling error, e.g., cols 17:59-18:2 and 18:22-33 and fig. 5:506. The user enters text and interacts with a spell checker via a user’s computing device 106 [“entered by a user of the computing device”], col 5:32-54. The computing device implements the method by using software [an application], cols 11:58-12:14)
the first word including at least one character; (the tokens are identified by identifying text/characters string that are separated by whitespace, col 3:13-34. As such, it is interpreted that each token has at least one text/character, because a text/character string has to contain at least one character. E.g., a set of tokens entered by the user may be “he drinks coffee”, which each of the words contains more than one character [at least one character], see col 2:43-3:12)
determining, by the […] application, that the user has completed entering the word; (the tokens are identified by identifying text/characters string that are separated by whitespace, col 3:13-34. As such, it is understood that the computer determines that the user has completed entering the word, at least based on identifying the presence of whitespaces following a word)
[…];
accessing, by [an] application, at least one word entered by the user prior to the entering of the first word; (a suggestion component may take into consideration [accessing] a context of non-word spelling error [first word], which may include accessing words surrounding the spelling error, e.g., preceding words in the digital content [at least one word entered by the user prior to the entering of the first word], in order to determine candidate suggestions, e.g., see cols 7:51-8:24 and 19:10-52 and fig. 5:510,512)
determining, by the […] application, [an] edit distance between the first word and each of a plurality of candidate modifications, based on analyzing the first word, […] based on the at least one word entered prior to the entering of the first word, (the suggestions are determined/preselected based on the context of a non-word spelling error, e.g., preceding words, as explained above, and also the candidate suggestions are limited only to those candidates that have a small edit distance away from the spelling of the non-word spelling error [an edit distance between the first word and each of a plurality of candidate modifications], cols 18:58-19:52. Herein, the edit distance is interpreted as being determined at least because the candidate suggestions are limited to candidates identified as having a small edit distance, cols 18:58-19:52 and fig. 5:510. At least because the context of the non-word spelling error is also considered, it is interpreted that the edit distance calculation is based on analyzing the first word […] and the at least one word entered prior to the entering of the first word)
the plurality of candidate modifications selected from a dictionary in a language (candidate suggestions are obtained from and located by searching within a corpus, which may represent a dictionary that is associated with or corresponds to a language, cols 6:3-16 and 7:11-36. Also see fig. 6 and cols 20:36-21:20.)
matching a language of the first word; (the digital content, which includes the spelling error [first word], is analyzed to identify a corresponding [matching] language associated with the digital content, and the language is used in selecting the corpus [i.e., dictionary], cols. 6:3-16 and 18:34-45. Also see fig. 6 and cols 20:36-21:20)
identifying, by the […] application, a subset of the plurality of candidate modifications, each of the subset associated with a confidence score that satisfies a threshold level of confidence, […]; (the spell checker ranks and prioritizes candidate suggestions based on a cumulative score for individual suggestions, which represent the likelihood that the candidate suggestions pertains to an intended word of the non-word spelling error [confidence score], and identifies for presentations only those candidate suggestions [subset] having a cumulative score greater than or equal to a threshold amount [satisfies a threshold level of confidence], cols 3:56-4:32)
and modifying, by the […] application, [a] graphical user interface to include a display of at least one of the identified subset associated with the confidence score that satisfies a threshold level of confidence. (the spell checker presents [] only those candidate suggestions having a cumulative score greater than or equal to a threshold amount, col 4:9-32, i.e., modifying, by the […] application, [a] graphical user interface to include a display [displaying] of at least one of the identified subset associated with the confidence score that satisfies a threshold level of confidence. Herein, that since the user interacts with the computing device via objects such as menus/pop-up boxes, the user interface of the computing device is interpreted as being a graphical user interface)
Gupta does not appear to expressly teach
that the receiving of the user input representing a first word entered by a user of the computing device is received by a graphical user interface provided by a virtual keyboard application executing on a computing device.
identifying, by […] the virtual keyboard application, a touchpoint within the graphical user interface associated with the at least one character;
that the determined edit distance is “a keyboard-weighted” edit distance that is also based on a location of the touchpoint within the graphical user interface
or that the identifying of the subset is “based on the determined keyboard-weighted edit distance”.
However, Dubs teaches/suggests
that the receiving of the user input representing a first word entered by a user of the computing device is received by a graphical user interface provided by a virtual keyboard application executing on a computing device. (virtual keyboard keys 300 of fig. 3, which are displayed by a touch screen 105 of fig. 1 [a graphical user interface], ¶ 36. The user inputs data into the computer 100 via the virtual keyboard, ¶ 33 and figs. 1 and 3. The computer is herein interpreted as including a virtual keyboard application executing on a computing device in order to allow the input, see ¶¶ 32 and 52 and figs. 1, 3 and 6)
identifying, by the virtual keyboard application, a touchpoint within the graphical user interface associated with the at least one character; (a touch location module 720 logs [“identifies”] each user touch location 315 on the keyboard for a plurality of modified first strings and calculates a user adjustment vector from the plurality of logged user touch locations 315, ¶ 60 and figs. 3 and 7-8. A substitution cost module calculates a substitution cost between a first character of a first string and a second character of a second string. A spatial vector module calculates a spatial vector between the first character and the second character from a location of a first key representing the first character on a keyboard and a location of a second key representing the second character on the keyboard. The spatial vector module modifies the substitution cost if the spatial vector is less than a spatial threshold, ¶ 10)
that the determined edit distance is “a keyboard-weighted” edit distance that is also based on a location of the touchpoint within the graphical user interface (a method, apparatus, and system that modifies substitution cost based on keyboard layout [keyboard-weighted] when calculating edit distances, ¶ 7, the method/apparatus/system may modify a first string to a second string in response to the edit distance, such modification includes modifying the substitution cost used in the edit distance calculation to account for the relative positions of the keys 300 [keyboard-weighted] representing first and second characters in the first and second string. As a result, a mis-keyed character resulting from a user unintentionally touching an area removed from a desired key is given a lower substitution cost value and so more consideration as a possible replacement in the edit distance calculation, ¶¶ 69 and 73-74 and fig. 8).
and that the identifying of the subset is “based on the determined keyboard-weighted edit distance”. (plurality of potential replacement strings are suggested based on edit distances, ¶¶ 5 and 74)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Gupta to include that the receiving of the user input representing a first word entered by a user of the computing device is received by a graphical user interface provided by a virtual keyboard application executing on a computing device, identifying, by the virtual keyboard application, a touchpoint within the graphical user interface associated with the at least one character, that the determined edit distance is “a keyboard-weighted” edit distance that is also based on a location of the touchpoint within the graphical user interface and that the identifying of the subset is “based on the determined keyboard-weighted edit distance”, as taught/suggested by Dubs.
One would have been motivated to make such a combination in order to improve the reliability of the method by increasing the probability of correctly identifying common typing mistakes, Dubs ¶ 6.
Gupta further teaches that the spell checker analyzes context of the misspelling error, at least in part, for the purpose of “word prediction”, cols 1:60-2:21.
Gupta does not appear to expressly teach, but Xu teaches/suggests
that the identifying is “a neural network component of” the virtual keyboard application, “the neural network comprising at least two connected neural network layers and a feature vector” (a neural network training method that helps avoid the overfitting phenomenon that occurs when the network process text, Abstract, wherein the layers of the network may be fully connected, ¶ 144, and wherein the training includes using a feature vector that is obtained through a first processing step is input into a second layer to continue to train the neural network, ¶ 47.).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to further modify the method of Gupta to include that the identifying is “a neural network component of” the virtual keyboard application, “the neural network comprising at least two connected neural network layers and a feature vector”, as taught/suggested by Xu.
One would have been motivated to make such a combination, by modifying the virtual keyboard application, in order to improve the accuracy of word prediction candidates afforded by the method, Xu ¶ 47 and Gupta cols 1:60-2:21. It is noted that Gupta is involved in making predictions from data, and Xu’s uses neural network to make predictions more accurately. Although Gupta doesn’t directly mention the use of neural networks and Xu doesn’t directly mention text predictions specifically for a keyboard application, Applicant Admitted Prior Art (AAPA) teaches that it was well-known in the art to use neural networks to make text predictions, and that the accuracy of these predictions increases over time, see Conclusion section. This teaching is considered AAPA because the applicant did not traverse the examiner’s assertion of official notice in a previous office action (MPEP § 2144.03.C).
Claim 2:
The rejection of claim 1 is incorporated. Gupta further teaches
further comprising selecting the plurality of candidate modifications from a dictionary including words in a dialect of a language. (the differences of local language dialects are also considered in determining scores/ranking for candidate modifications, cols 16:51-17:4)
Claim 7:
The rejection of claim 1 is incorporated. Gupta further teaches
before determining the edit distance, identifying a language in which the user entered the first word. (as explained above, the digital content, which includes the spelling error [first word], is analyzed to identify a corresponding [matching] language associated with the digital content, and the language is used in selecting the corpus [i.e., dictionary], cols. 6:3-16 and 18:34-45. Also see fig. 6 and cols 20:36-21:20. The weighted scores used to calculate the edit distance are based on the language, col 20:6-20 and fig. 5:520, as such the identification of a language occurs before determining the edit distance)
Claim 8:
The rejection of claim 1 is incorporated. Gupta further teaches
further comprising: before determining the edit distance, determining whether the first word matches a word in the dictionary in the language matching the language of the first word; (after a word has been identified as misspelt, edit distances are determined for selecting candidate suggestions, col 3:35-55. However, before determining the edit distance, in order to identify words as being misspelt, the words are compared to a corpus [dictionary], and when not found in the dictionary, the words are identified/considered as being misspelt, cols 18:58-19:9 and fig. 5:10, i.e., determining whether the first word matches a word in the dictionary. But even before using the corpus, a language is determined based on the digital content [words/tokens], cols. 6:3-16 and 18:34-45, i.e., the dictionary in the language matching the language of the first word. )
and determining that the first word is not in the dictionary. (in order to identify a word as being misspelt, the words are compared to a corpus [dictionary], and when not found in the dictionary, the words are identified/considered as being misspelt, cols 18:58-19:9 and fig. 5:10)
Claim 9:
The rejection of claim 1 is incorporated. Gupta further teaches
before determining the edit distance: identifying a language in which the user typed the first word; (a language is determined from analyzing the digital content [containing the first word], cols. 6:3-16 and 18:34-45, i.e., identifying a language in which the user typed the first word. The identifying of language is done before [before determining the edit distance] and in order to identify a respective corpus [dictionary] from which a list of candidate suggestions is subsequently identified as having a small edit distance [determining the edit distance], cols 3:35-55, 6:3-16, 18:34-45, 18:58-19:9 and fig. 5:10.)
identifying a dictionary that is in the identified language from a plurality of dictionaries stored on the computing device; (a language is determined from analyzing the digital content, cols. 6:3-16 and 18:34-45. The language is identified from a plurality of dictionaries stored on the computing device, col 16:3-27 and fig. 4. The identifying of language is done before and in order to identify a respective corpus [identifying a dictionary that is in the identified language] from which a list of candidate suggestions is subsequently identified as having a small edit distance, cols 3:35-55, 6:3-16, 18:34-45, 18:58-19:9 and fig. 5:10.)
determining whether the first word matched a word in the identified dictionary; (in order to identify words as being misspelt, the words in the digital content [containing the first word] are compared to a corpus [dictionary], i.e., determining whether the first word matched a word in the identified dictionary)
and determining that the first word is not in the identified dictionary. (when the word(s) in the digital content are not found in the dictionary [determining that the first word is not in the identified dictionary], the words are identified/considered as being misspelt, cols 18:58-19:9 and fig. 5:10)
Claim 10:
The rejection of claim 1 is incorporated. Gupta further teaches
further comprising receiving user input including an instruction to replace the first word with the at least one of the identified subset. (based on a user selecting one of the candidate suggestions, the selected candidate is inserted into the digital content in the place of the misspelt word [“replacing” it], col 9:41-63)
Claim(s) 3-4 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US 10936813 B1) in view of Dubs (US 20100251105 A1) and Xu (US 20220327363 A1), as applied to claim 1 above, and further in view of Ryu; Won-ho et al. (hereinafter Ryu – US 20170249017 A1).
Claim 3:
The rejection of claim 1 is incorporated. Gupta does not appear to expressly teach
further comprising selecting the plurality of candidate modifications from a dictionary including a subset of words contained in a second dictionary and associated with a population group having a threshold level of probability of using the subset of words.
However, Ryu teaches/suggests
further comprising selecting the plurality of candidate modifications from a dictionary including a subset of words contained in a second dictionary and associated with a population group having a threshold level of probability of using the subset of words (generating/displaying word recommendation based on a demographic language model (dictionaries) that considers demographic properties such as age group and gender [population group], ¶ 13, which is used to assigned priorities to candidate recommendations and recommend based on these priorities, in order to increase the probability that words intended by those users, Abstract, ¶¶ 83 and 131-134, among others).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Gupta further comprising selecting the plurality of candidate modifications from a dictionary including a subset of words contained in a second dictionary and associated with a population group having a threshold level of probability of using the subset of words, as taught/suggested by Ryu.
One would have been motivated to make such a combination in order to increase the text input effectiveness of the method, Ryu ¶ 10.
Claim 4:
The rejection of claim 1 is incorporated. Gupta does not appear to expressly teach
further comprising selecting the plurality of candidate modifications from a dictionary including words in a slang version of a language.
However, Ryu teaches/suggests
further comprising selecting the plurality of candidate modifications from a dictionary including words in a slang version of a language (generating/displaying word recommendation based on a demographic language model (dictionaries) that considers demographic properties such as age group and gender [population group], and including their associated slang language, ¶¶ 13, 83 and 86, which is used to assigned priorities to candidate recommendations and recommend based on these priorities, in order to increase the probability that words intended by those users, Abstract, ¶¶ 83 and 131-134, among others.)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Gupta further comprising selecting the plurality of candidate modifications from a dictionary including words in a slang version of a language , as taught/suggested by Ryu.
One would have been motivated to make such a combination in order to increase the text input effectiveness of the method, Ryu ¶ 10.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US 10936813 B1) in view of Dubs (US 20100251105 A1) and Xu (US 20220327363 A1), as applied to claim 1 above, and further in view of Zhai; Shumin et al. (hereinafter Zhai – US 20160299685 A1).
Claim 5:
The rejection of claim 1 is incorporated. Gupta does not appear to expressly teach, but Zhai teaches:
wherein identifying the subset of the plurality of candidate modifications further comprises determining, by the neural network component of the virtual keyboard application, a probability of a candidate modification in the subset of the plurality of candidate modifications having a threshold level of accuracy. (a keyboard module that uses a neural network to determine probabilities related to characters and/or strings based on features of the user input, and uses these probabilities to perform operations including auto-correction/suggestion, and to determine the characters/strings that mostly were intended by the user, and wherein the strings that satisfy a probability threshold are output for display, ¶¶ 14 and 129.)
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Gupta wherein identifying the subset of the plurality of candidate modifications further comprises determining, by the neural network component of the virtual keyboard application, a probability of a candidate modification in the subset of the plurality of candidate modifications having a threshold level of accuracy, as taught/suggested by Zhai.
One would have been motivated to make such a combination in order to improve the efficiency, flexibility and accuracy of the method, ¶¶ 1-2 and 69.
Claim(s) 11-12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta (US 10936813 B1) in view of Dubs (US 20100251105 A1) and Xu (US 20220327363 A1), as applied to claim 1 above, and further in view of Harrity; Paul A. (hereinafter Harrity – US 7207004 B1).
Claim 11:
The rejection of claim 1 is incorporated. Gupta does not appear to expressly teach
further comprising receiving user input including an instruction not to replace the first word with the at least one of the identified subset.
However, Harrity teaches/suggests the concept(s) of
further comprising receiving user input including an instruction not to replace the first word with the at least one of the identified subset (a spell checker window displays selectable items includes suggestions, wherein the items include “Add”, “Ignore”, “Ignore All”, and “Cancel”, which are instruction not to replace the error [first word] with any of the suggestions, cols 6:35-7:35 and figs. 9A-9B, i.e., “receiving user input including an instruction not to replace the first word with the at least one of the identified subset”).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Gupta further comprising receiving user input including an instruction not to replace the first word with the at least one of the identified subset, as taught/suggested by Harrity.
One would have been motivated to make such a combination in order to improve the usability/functionalities provided by the method, by allowing the user to supplement a standard spell-check dictionary and provide addition control to the user by displaying additional decision options, Harrity cols 1:64-2:3, 6:35-7:35 and figs. 9A-9B.
Claim 12:
The rejection of claim 1 is incorporated. Gupta does not appear to expressly teach
further comprising receiving user input including an instruction to add the first word to the dictionary.
However, Harrity teaches/suggests the concept(s) of
further comprising receiving user input including an instruction to add the first word to the dictionary (a spell checker window displays selectable items includes suggestions, wherein the items include “Add” which are instruction to add flagged error [first word] to a dictionary, cols 6:35-7:35 and figs. 9A-9B, i.e., “receiving user input including an instruction to add the first word to the dictionary” ).
Accordingly, it would have been obvious to a person having ordinary skill in the art, before the effective filing date of the claimed invention, to modify the method of Gupta further comprising receiving user input including an instruction to add the first word to the dictionary, as taught/suggested by Harrity.
One would have been motivated to make such a combination in order to improve the usability/functionalities provided by the method, by allowing the user to supplement a standard spell-check dictionary and provide addition control to the user by displaying additional decision options, Harrity cols 1:64-2:3, 6:35-7:35 and figs. 9A-9B.
Response to Arguments
Applicant's argument(s) has/have been fully considered but are unpersuasive.
First, in summary, the applicant alleges that “since the pending claims do not require a spatial measurement”, the combination of Gupta and Dubs fails to suggest the limitations of the claim, Remarks Pages 5-6. E.g., the applicant states that “Dubs fails to cure this deficiency [of Gupta] because the system in Dubs requires a calculation of a physical distance between keys on either a physical keyboard or in a user interface representing a keyboard in order to function” (Remarks Page 5) and “Dubs cannot operate without determining the initial calculation of the physical distance between keys. Since Dubs teaches only the use of the touch location to modify the spatial vector and since Dubs teaches that the spatial vector representing the physical distance between keys must be used to modify a substitution cost, Dubs cannot teach or suggest a method that does not require the physical measurement of a distance between keys represented by a spatial vector” (Remarks Page 6).
The examiner respectfully disagrees because:
A reference can serve as a primary or secondary source for a claim limitation even if it contains extra details because the prior art is considered as a whole, and the examiner only needs to find support for each limitation within the collective art, as indicated by MPEP § 2144, which discusses motivation to combine references for obviousness and MPEP § 2131, showing how multiple references can support anticipation, focusing on what's taught, not all the extras. Here, the examiner has mapped each teaching to the prior art, as whole, and simply because the claimed invention does not require a spatial/physical measurement, as in the system of Dubs, this doesn’t mean that Dubs cannot be used to teach the limitations of the claims.
The applicant has fails to provide evidence that a system which require spatial measurements cannot also require or be modified to require producing a plurality of candidate modifications based on “how likely it was that one or more letters were pressed” (this is apparently a paraphrase of the actually claimed limitation of “identifying…based on the determined keyboard-weighted edit distance”)
Furthermore, it is noted that the features upon which applicant relies (i.e., that the identifying is of “how likely it was that one or more letters were pressed”, and that the claimed method is one cannot “require the physical measurement of a distance between keys represented by a spatial vector”) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993).
Second, the applicant alleges patentability of dependent claims based on the arguments above. Remarks Page 6.
The examiner respectfully disagrees for the reason(s) mentioned above.
Conclusion
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Below is a list of these references, including why they are pertinent:
Grevstad; Simon et al. (US 20210364130 A1), pertinent to representative claim 1 for disclosing the self-improving nature of neural networks, ¶ 39.
Popescu; Octavian et al. (US 20170286403 A1), pertinent to representative claim 1 for disclosing that conventional techniques have used Recurrent Neural Networks (RNN) with success for predicting word similarity, ¶ 4.
Shillingford; Brendan et al. (US 20210110831 A1), pertinent to representative claim 1 for disclosing that conventional systems use a VSR neural network to directly predict characters or words, ¶ 22
Osborne; Joseph et al. US 20180267952 A1, pertinent at least to claim 1 for disclosing predicting text using a neural network language model, ¶ 29 and fig. 2.
Greenberg; Alexa et al. US 20180173692 A1, pertinent at least to claim 1 for disclosing keyboard module that help predict characterized or implies a text, and/or auto generate phrases from the received text, by using neural networks, ¶ 41 and fig. 1.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GABRIEL S MERCADO whose telephone number is (408)918-7537. The examiner can normally be reached Mon-Fri 8am-5pm (Eastern Time).
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, Kieu Vu can be reached on (571) 272-4057. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Gabriel Mercado/ Examiner, Art Unit 2171