Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicant's amendment filed on May 6, 2026 has been entered. Claims 1 – 2, 6 – 10, 13 – 15, 17 and 19 – 20 have been amended. Claims 4 and 11 have been canceled. No claims have been added. Claims 1 – 3, 5 – 10 and 12 – 20 are still pending in this application, with claims 1, 8 and 15 being independent.
Response to Arguments
Applicant's arguments filed May 6, 2026 have been fully considered but they are not persuasive.
Regarding Rejection under 35 USC § 103
The Applicant alleges: “
Without acquiescing as to the validity of the prior rejections, Applicant respectfully submits that the cited references do not disclose, teach, or suggest features recited in the amended claims. For example, the amended independent Claim 1 recites, inter alia:
detecting, by the processor, background noise in the portion of the received audio input that is classified as the user speech; and
in response to determining that the detected background noise exceeds a predetermined threshold, rejecting, by the processor, the portion of the received audio input that is classified as the user speech.
Support can also be found in at least the previously presented Claims 4 and 11, as well as at least paragraphs [0172]-[0178] of the specification as originally filed. Additional support can be found in other portions of the present application.
The Office Action argued:
[r]egarding claim 11 (depends on claim 8), Braho discloses the SRD wherein the at least one memory and the computer program code are further configured to, with the at least one processor cause the SRD to at least reject a portion of the received audio input that is classified as user speech in an instance which detected background noise exceeds a predetermined threshold of background noise ([0161 - 01621, "an acceptance or rejection threshold").
See Office Action, pages 12-13. Applicant respectfully traverses the rejections. Paragraphs [0161]- [0162] of Braho state:
[a]t 912, a threshold adjust module may selectively adjust threshold(s) used by an accept/reject module for accepting or rejecting a word of a hypothesis from the decoder. The threshold adjustment may advantageously be based at least in part on classifications of the frames corresponding to the word, for instance distinctions between being classified as speech versus a non-transient background noise versus a transient noise event. The threshold adjust may additionally, or alternatively, employ an expected result. Threshold adjustment, including use of expected results, is generally described in U.S. Pat. No. 7,865,362. However, the present implementation may advantageously selectively operate the threshold adjustment module using the classifications. Alternatively or additionally, the confidence score of a word in the hypothesis can be adjusted based at least in part on the classifications of the frames corresponding to the word. In this case, it may not be
necessary to adjust an acceptance or rejection threshold.
At 914, the accept/reject hypothesis module determines whether to accept or reject the hypothesis, for example by comparing a confidence score of a word in the hypothesis to a threshold. At 916, the accept/reject hypothesis module determines whether the hypothesis is accepted. Notable acts 914 and 916 may be combined. Hypothesis acceptance and rejection are generally described in U.S. Pat. No. 7,865,362. If the hypothesis is accepted, the processor-based recognition device outputs recognized text and/or metadata at 918. Otherwise, the processor- based recognition device may reprocess the set of fragments, provide an indication that the input was not recognized, simply attempt to process the next set of fragments.
(Emphasis added). To the extent that Braho was deemed to allegedly describe "comparing a confidence score of a word in the hypothesis to a threshold," paragraph [0094] of Braho states:
[t]he decoder 444 provides hypotheses 462 along with associated
confidence scores or values to the accept/reject module 446. In general, a hypothesis may contain one or more words, numbers or phrases. The
accept/reject module 446 may be implemented in logic executed by a processor or other integrated circuit device executing instructions, or alternatively may be implemented by a dedicated circuitry (e.g., dedicated digital circuit). The accept/reject module 446 computationally evaluates the hypotheses 462, accepting some and rejecting others. The confidence scores or values generated by the decoder 444 may be used by the accept/reject module 446 in evaluating the hypotheses. For example, a hypothesis or a portion thereof may be accepted if one or more confidence values or factors is or are above a defined acceptance threshold.
Also for example, a hypothesis or a portion thereof may be ignored or rejected if one or more confidence values or factors is or are not above the defined acceptance threshold, or below a defined rejection threshold. Such an outcome may cause the system to prompt the user to repeat the speech input.
(Emphasis added). As emphasized above, Braho does not consider background noise in the "hypothesis," and therefore cannot disclose, teach, or suggest "detecting, by the processor, background noise in the portion of the received audio input that is classified as the user speech; and in response to determining that the detected background noise exceeds a predetermined threshold, rejecting, by the processor, the portion of the received audio input that is classified as the user speech" recited in the amended Claim 1.
Other cited references do not cure the deficiencies of Braho. For example, to the extent that El Dokor was deemed to allegedly describe "[t]he voice recognition module 204 generates and outputs voice data representing words in the audio input," the "voice recognition module 204" does not detect background noise in the portion of received audio input classified as the user speech, and therefore cannot disclose, teach, or suggest "in response to determining that the detected background noise exceeds a predetermined threshold, rejecting, by the processor, the portion of the received audio input that is classified as the user speech" recited in the amended Claim 1. To the extent that Liu was deemed to allegedly describe "a deep neural support vector machine configured to classify the acoustic information into a plurality of phones," the "deep neural support vector machine" does not detect background noise in the portion of received audio input classified as the user speech, and therefore cannot disclose, teach, or suggest "in response to determining that the detected background noise exceeds a predetermined threshold, rejecting, by the processor, the portion of the received audio input that is classified as the user speech" recited in the amended Claim 1. Other cited references similarly fail to disclose, teach, or suggest features recited in the amended Claim 1.
Accordingly, for at least the various reasons set forth above, Applicant respectfully submits that the cited references fail to disclose, teach, or suggest each and every element of independent Claim 1. Applicant respectfully requests that the rejection of independent Claim 1 be withdrawn, and that Claim 1 be allowed.
For at least the similar reasons, Applicant respectfully submits that independent Claims 8 and 15 are also not disclosed, taught, or suggested by cited references. Applicant respectfully requests that Claims 8 and 15 be allowed.
The other claims are dependent from one of the independent claims discussed above, and are patentable for at least the same reasons. Applicant, therefore, respectfully submits that the rejections herewith are overcome and requests that the rejections be withdrawn. Because each dependent claim is also deemed to define an additional aspect of the invention, however, the individual reconsideration of the patentability of each on its own merits is respectfully requested.
Examiner’s response:
The Examiner respectfully disagrees.
Braho discloses, in Paragraphs [0161], that:
[0161] At 912, a threshold adjust module may selectively adjust threshold(s) used by an accept/reject module for accepting or rejecting a word of a hypothesis from the decoder. The threshold adjustment may advantageously be based at least in part on classifications of the frames corresponding to the word, for instance distinctions between being classified as speech versus a non-transient background noise versus a transient noise event. The threshold adjust may additionally, or alternatively, employ an expected result. Threshold adjustment, including use of expected results, is generally described in U.S. Pat. No. 7,865,362. However, the present implementation may advantageously selectively operate the threshold adjustment module using the classifications. Alternatively or additionally, the confidence score of a word in the hypothesis can be adjusted based at least in part on the classifications of the frames corresponding to the word. In this case, it may not be necessary to adjust an acceptance or rejection threshold. (Emphasis added).
Braho discloses the claimed limitation of “detect background noise in the portion of the received audio input classified as the user speech ([0161], selectively adjust threshold(s) … distinctions between being classified as speech versus a non-transient background noise versus a transient noise event); and in response to determining that the detected background noise exceeds a predetermined threshold, reject the portion of the received audio input that is classified as the user speech ([0161], selectively adjust threshold(s) used by an accept/reject module for accepting or rejecting a word of a hypothesis from the decoder … distinctions between being classified as speech versus a non-transient background noise versus a transient noise event).”
Therefore, the 103 rejection is still maintained.
Regarding Double Patenting Rejection
The Applicant’s argument is persuasive. The double patenting rejection is lifted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 6, 7, 13 – 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 6 recites the limitation " wherein the machine learning model is trained based on a training corpus comprising at least one word and phrase related to the warehouse picking operations” (emphasis added). There is insufficient antecedent basis for this limitation in the claim.
Claim 7, it is dependent upon claim 6, thus, it is rejected accordingly.
Claims 13 – 14, they are corresponding to claims 6 – 7, thus, they are rejected accordingly.
Claim 15 recites the limitation "rejecting, by the processor, the portion of the received audio input that is classified as the user speech in response to the detected background noise exceeding a predetermined threshold of the detected background noise” (emphasis added). There is insufficient antecedent basis for this limitation in the claim.
Claims 16 – 20, they are dependent upon claim 15, thus, they are rejected accordingly.
Claim 20 recites the limitation " wherein the machine learning model is trained based on a training corpus comprising at least one word and phrase related to the warehouse picking operations” (emphasis added). There is insufficient antecedent basis for this limitation in the claim.
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, 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, 5 – 8 and 12 – 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho et al. (US Patent Application Publication 2014/0278391), hereinafter referred as Braho, in view of El Dokor et al. (US Patent Application Publication 2014/0277936), hereinafter referred as El Dokor, and in further view of Liu et al. (US Patent Application Publication 2016/0307565), hereinafter referred as Liu.
Regarding claim 8, Braho discloses a speech recognition device (SRD) for use in warehouse picking operations (Figs. 1 - 3) comprising:
a microphone (Fig. 1, #120a, 120b);
at least one processor (Fig. 2, #214); and
at least one memory (Fig. 2, #218, 220) including computer program code, the at least one memory and the computer program code configured to, with the at least one processor, cause the SRD to at least:
receive, at the microphone, an audio input (Fig. 5, 504, [0110]), wherein the audio input is related to a phrase uttered to an instruction to pick an item, and an identification of at least of a location and an object ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”);
classify a portion of the received audio input, wherein the portion of the received audio input is classified as user speech (Fig. 9, 909 - 910, [0157 - 0160]);
detect background noise in the portion of the received audio input classified as the user speech ([0161], selectively adjust threshold(s) … distinctions between being classified as speech versus a non-transient background noise versus a transient noise event); and
in response to determining that the detected background noise exceeds a predetermined threshold, reject the portion of the received audio input that is classified as the user speech ([0161], selectively adjust threshold(s) used by an accept/reject module for accepting or rejecting a word of a hypothesis from the decoder … distinctions between being classified as speech versus a non-transient background noise versus a transient noise event).
However, Braho fails to explicitly disclose the SRD wherein the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object.
However, in a similar field of endeavor El Dokor discloses a system of audio input and control ([0025]). In addition, El Dokor discloses the system wherein the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt ([0025]).
Therefore, 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 invention of Braho, and the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object. The motivation for doing this is that audio input can be confirmed without an error so that process can be more accurate.
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein classify portions of the received audio input is based on a machine learning model.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classify portions of the received audio input is based on a machine learning model ([0014]).
Therefore, 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 invention of Braho, and classify portions of the received audio input is based on a machine learning model. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claim 12 (depends on claim 8), Braho discloses the SRD further comprising a headset, wherein the microphone is mounted on the headset and is configured for use in a warehouse environment (Fig. 1, #120a and 120b).
Regarding claim 13 (depends on claim 8), Braho discloses the SRD wherein the classification is processing based on a corpus comprising at least one word and phrase related to the warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase ([0049 - 0052]).
There was some teaching, suggestion, or motivation in Braho and Liu or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, 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 invention of Braho, and the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claim 14 (depends on claim 13), Braho discloses the SRD wherein the
classification is processing based on a corpus and the audio input received during the warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”; Fig. 1, [0046 - 0053]).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein the classification is based on the machine learning model that is trained based on training corpus and the audio input.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on the machine learning model that is trained based on training corpus and the audio input ([0049 - 0052]).
There was some teaching, suggestion, or motivation in Braho and Liu or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, 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 invention of Braho, and the classification is based on the machine learning model that is trained based on training corpus and the audio input. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claims 1 and 5 – 7, they are corresponding to claims 8 and 12 – 14, respectively, thus, they are interpreted and rejected for a same reason set forth for claims 8 and 12 – 14.
Claim(s) 2 – 3, 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho in view of El Dokor, in further view of Liu, and Yen et al. (US Patent Application Publication 2009/0271187), hereinafter referred as Yen.
Regarding claim 9 (depends on claim 8), Braho discloses the SRD wherein the at least one memory and the computer program code are further configured to, with the at least one processor, cause the SRD to at least classify a portion of the received audio input as background noise and reject the other portion of the received audio input that is classified as the background noise ([0161 – 0162]).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein classify another portion of the received audio input is based on the machine learning model; and background noise is background speech.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classify portions of the received audio input is based on a machine learning model ([0014]).
In a similar field of endeavor Yen discloses a system of audio signal processing (abstract). In addition, Yen discloses the system wherein background noise is background speech ([0057]).
Therefore, 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 invention of Braho, and classify portions of the received audio input is based on a machine learning model; and background noise is background speech. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence, and a specific type of background noise can be rejected so that the Application of Braho can be extended.
Regarding claim 10 (depends on claim 8), Braho discloses the SRD wherein the
classification is to distinguish the user speech from background noise ([0161 – 0162]).
However, Braho in view of El Dokor fails to explicitly disclose the machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system and wherein the machine learning model is trained to distinguish user speech from background speech.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on a machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system and wherein the machine learning model is trained to distinguish sounds ([0014]).
In a similar field of endeavor Yen discloses a system of audio signal processing (abstract). In addition, Yen discloses the system wherein background noise is background speech ([0057]).
There was some teaching, suggestion, or motivation in Braho, Liu and Yen or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, 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 invention of Braho, and the machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system and wherein the machine learning model is trained to distinguish user speech from background speech. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence, and a specific type of background noise can be rejected so that the Application of Braho can be extended.
Regarding claims 2 – 3, they are corresponding to claims 9 – 10, respectively, thus, they are interpreted and rejected for a same reason set forth for claims 9 – 10.
Claim(s) 15 – 16 and 18 – 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho in view of Liu.
Regarding claim 15, Braho discloses a method of speech recognition for use in warehouse picking operations (abstract), the method comprising:
receiving, by a processor, an audio input (Fig. 5, 504, [0110]);
classifying, by the processor, a portion of the received audio input, wherein the portion of the received audio input is classified as user speech (Fig. 9, 909 - 910, [0157 - 0160]) related to the warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”);
rejecting, by the processor, the portion of the received audio input that is classified as the user speech in response to the detected background noise exceeding a predetermined threshold of the detected background noise ([0161], selectively adjust threshold(s) used by an accept/reject module for accepting or rejecting a word of a hypothesis from the decoder … distinctions between being classified as speech versus a non-transient background noise versus a transient noise event); and
processing, by the processor, a remaining portion of the received audio input that is classified as user speech (Fig. 5, 518, [0119], “This may advantageously limit the information being sent to be information which has been classified (i.e., determined to likely be) as speech rather than noise”) to generate at least one of words and phrases related to the warehouse picking operations (Fig. 5, 520 - 522, [0120 - 0122], “digitized audio to recognize speech”; [0162], “outputs recognized text”; [0007]).
However, Braho fails to explicitly disclose the method wherein classify portions of the received audio input is based on a machine learning model.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classify portions of the received audio input is based on a machine learning model ([0014]).
Therefore, 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 invention of Braho, and classify portions of the received audio input is based on a machine learning model. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Regarding claim 16 (depends on claim 15), Liu discloses the method wherein the machine learning model is at least one of a neural network system, a support vector machine, and an inductive logic system ([0014]).
Regarding claim 18 (depends on claim 15), Braho discloses the method further comprising a microphone, wherein the microphone is mounted on a headset and is configured for use in a warehouse environment (Fig. 1, #120a and 120b).
Regarding claim 19 (depends on claim 15), Braho discloses the method wherein the detected background noise comprises at least one of an operation of a vehicle in a warehouse and a movement of pallets in the warehouse ([0005], driving a vehicle).
Regarding claim 20 (depends on claim 15), Braho discloses the method wherein the classification is processing based on a corpus comprising at least one word and phrase related to the warehouse picking operations ([0007], “the user may receive voice instructions, …, and/or receive directions such as location information specifying locations for picking up or delivering goods”).
However, Braho in view of El Dokor fails to explicitly disclose the SRD wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase.
However, in a similar field of endeavor Liu discloses a system of audio input and control (abstract). In addition, Liu discloses the system wherein the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase ([0049 - 0052]).
There was some teaching, suggestion, or motivation in Braho and Liu or in the knowledge generally available to one of ordinary skill in the art, to modify the reference or to combine reference teachings teachings to achieve the claimed limitations; and there was reasonable expectation of success to achieve the claimed limitations (KSG scenario G).
Therefore, 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 invention of Braho, and the classification is based on the machine learning model that is trained based on a training corpus comprising at least one word and phrase. The motivation for doing this is that the device of Braho can be more powerful and advanced for artificial intelligence.
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Braho in view of Liu, and in further view of El Dokor.
Regarding claim 17 (depends on claim 15), Braho fails to explicitly disclose the method wherein the audio input is related to a confirmation phrase, and wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object.
However, in a similar field of endeavor El Dokor discloses a system of audio input and control ([0025]). In addition, El Dokor discloses the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt ([0025]).
Therefore, 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 invention of Braho, and the audio input is related to a confirmation phrase uttered in response to an instruction wherein the confirmation phrase is at least one of a confirmation of a recognition of a prompt, a confirmation of a completion of a task, and a confirmation of an identification of at least of a location and an object. The motivation for doing this is that audio input can be confirmed without an error so that process can be more accurate.
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to QIAN YANG whose telephone number is (571)270-7239. The examiner can normally be reached on Monday-Thursday 8am-6pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Andrew Bee can be reached on 571-270-5183. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/QIAN YANG/
Primary Examiner, Art Unit 2677
Prosecution Insights