DETAILED ACTION
This action is in response to the Applicant’s response filed on July 6, 2026. As set forth therein, claims 3-7, 9 and 15 are original, claims 2, 8, 10-13, 16 and 19 are once amended and claims 1, 14, 17, 18, and 20 are twice amended. Claims 1-20 are pending.
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 .
Reissue Applications
For reissue applications filed before September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the law and rules in effect on September 15, 2012. Where specifically designated, these are “pre-AIA ” provisions.
For reissue applications filed on or after September 16, 2012, all references to 35 U.S.C. 251 and 37 CFR 1.172, 1.175, and 3.73 are to the current provisions.
Applicant is reminded of the continuing obligation under 37 CFR 1.178(b), to timely apprise the Office of any prior or concurrent proceeding in which Patent No. 10,560,576 is or was involved. These proceedings would include any trial before the Patent Trial and Appeal Board, interferences, reissues, reexaminations, supplemental examinations, and litigation.
Applicant is further reminded of the continuing obligation under 37 CFR 1.56, to timely apprise the Office of any information which is material to patentability of the claims under consideration in this reissue application.
These obligations rest with each individual associated with the filing and prosecution of this application for reissue. See also MPEP §§ 1404, 1442.01 and 1442.04.
Response to Arguments
Broadening
The Examiner notes that in view of the claim amendments, the broadening issue, as set forth in the previous non-final office action, has been resolved. Therefore, the rejection will be withdrawn.
Rejections Under 35 U.S.C. 103
The Applicant states that the cited references do not teach “while the calling application is being executed in the standby mode, determine whether the attendant is connected based on a voice transmitted by the service provider using a determination model comprising a deep-learning model for determining whether the voice transmitted by the service provider in an utterance of the attendant or an announcement previously stored as an automatic response service, wherein the deep-learning model is learned with respect to multiple audio signals related to an attendant voice,” as claimed.
With respect to Cosic, the Applicant states that Cosic does not teach or suggest a deep-learning model specifically learned with respect to multiple audio signals related to an attendant voice. The Applicant states that Cosic discloses training based on a broad range of sounds for virtual environment applications.
The Examiner notes that Cosic discloses a Sound Recognizer for detecting or recognizing a human voice using various models and based on machine learning, deep learning, and/or other artificial intelligence techniques. These include thresholds for similarity, statistical techniques, and/or optimization techniques. It is acknowledged that Cosic does not specifically disclose whether its deep learning includes training/learning with respect to multiple audio signals related to an attendant voice.
The Applicant also states that Gupta does not teach or suggest content, words or acoustic characteristics, such as tone, pitch and spectral features, that are related to an attendant voice.
The Examiner acknowledges the Applicant’s comments; however, the claims do not specifically recite any characteristics with respect to “multiple audio signals related to an attendance voice”. Therefore, the Applicant is arguing limitations which are not claimed.
The Examiner notes that in view of the Applicant’s arguments with respect to Cosic, it is agreed that the Cosic does not specifically disclose “wherein the deep-learning model is learned with respect to multiple audio signals related to an attendant voice.”
Nonetheless, as set forth below, the Examiner finds that it would have been obvious to train using multiple audio signals related to an attendant voice.
With respect to claim 17, the Applicant states that Binder does not teach or suggest learning an attendant’s voice to determine whether an attendant has connected on a call. The Applicant states that Binder discloses a classification task that is fundamentally different from that of claim 17 and that Binder discloses distinguishing a human voice from non-human sounds, a trigger phrase from a non-trigger phase, an authorized user from non-authorized user. The Applicant states that Binder does not teach or suggest determining whether a voice “is an utterance of the attendant or an automatic response service.”
The Examiner notes that rejection, as set forth below, shows that Gupta specifically discloses determining whether a voice is an utterance of an attendant or an automatic response service. In addition, newly cited prior art Gross also discloses distinguishing between a human voice and a machine. Therefore, the combination of teachings discloses determining whether a voice “is an utterance of the attendant or an automatic response service.”
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.
Claim(s) 14, 15, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US Patent 2010/0303227 in view of Cosic US 10,402,731 and further in view of Gross US Patent 8,380,503.
Regarding claim 14:
An electronic device comprising:
Gupta discloses a user electronic device 110. See Figure 1 and paragraph [0024].
a speaker;
Gupta discloses that the electronic device includes a user interface 212 which includes an audio output such as a speaker. See Figure 2 and paragraph [0041].
a touchscreen display;
Gupta discloses in paragraph [0041] that the user interface includes a display such as a touchscreen display.
a communication circuit;
Gupta discloses in paragraph [0038] of communications circuitry 208. See also Figure 2.
at least one processor operatively connected to the speaker, the display, and the communication circuit; and
As illustrated in Figure 2, Gupta discloses at least one processor 210 (see paragraph [0039]) coupled to the speaker, display and communication circuit via bus 214. See also paragraph [0037]
memory operatively connected to the at least one processor and storing instructions that, when executed by the at least one processor, cause the at least one processor to:
Gupta discloses in paragraph [0039] of a memory 204, store 206 with computer-readable instructions which is executable by a processor included in control circuitry or speech processor. See also paragraph [0036].
execute a calling application;
Gupta in paragraph [0046] discloses the reception of a user indication to make a telephone call.
attempt to connect a call to a service provider using the communication circuit;
See paragraphs [0046-0048] which disclose that the dialed numbers is for a call recipient which is a call center.
during call connection to the service provider, receive a first user input requesting a standby mode for connection to an attendant of the service provider, wherein the calling application is executed in a background in the standby mode;
See paragraphs [0047-0049] which disclose that the user may initiate on-hold monitoring (standby mode) when the call is for connection to a call center. As explained in paragraph [0043], when performing the on-hold monitoring, the electronic device can run a background process that may take control of the telephone call from the user so that the user can perform other non-phone related activities. Thus, the phone application is executed in the background while in a standby mode (on-hold monitoring mode).
in response to the first user input, execute the calling application in the standby mode;
See paragraphs [0050-0051] which discloses that in response to the user selection of “yes” icon 502 on-hold monitoring may be enabled for the telephone call and the electronic device can free up the user interface so that the user can perform non-phone related tasks (calling application in the standby mode). See also paragraph [0057].
while the calling application is being executed in the standby mode, determine whether the attendant is connected based on a voice transmitted by the service provider
Gupta discloses that during the on-hold monitoring and any non-phone related task, the electronic device can monitor the telephone line for incoming telephone data using a speech processor to detect any indicators that a live operator is present. Gupta also discloses that the electronic device can monitor for non-speech or non-audible data. Gupta at step 320 (Fig. 3) at paragraph [0054] discloses that the electronic device determines that the live operator is present. See paragraphs [0052-0053 and 0055].
using a determination model comprising a deep-learning model for determining whether the voice transmitted by the service provider is an utterance of the attendant or an announcement previously stored as an automatic response service, wherein the deep-learning model is learned with respect to multiple audio signals related to an attendant voice;
Gupta discloses monitoring an on-hold telephone call with a call center using a speech processor. With reference to Figure 8 and paragraphs [0067-0069], the speech processor uses keyword spotting to determine whether the voice transmitted is of the attendant or of a status announcement. See also paragraphs [0007 and 0052]. In addition, Gupta discloses using a speech model (paragraph [0036]) for speech processing by the speech processor. See also paragraph [0075-0077].
The Examiner notes that although Gupta discloses using a speech model, Gupta does not specifically disclose using a deep learning model for determining whether the voice transmitted by the service provider is an utterance of the attendant or an announcement previously stored as an automatic response service.
Nonetheless, using a deep learning model for recognizing voice would have been obvious to a person of ordinary skill in the art. For example, Cosic is directed in a part to a Sound Recognizer for detecting or recognizing human voice. As disclosed by Cosic, the Sound Recognizer may detect or recognize properties of the sound by comparing collection of sound samples from the sounds with collections of sound samples of known objects and/or their properties. See col. 79, lines 23-48. As further explained by Cosic, the Sound Recognizer may include deep learning, machine learning or another artificial intelligence techniques. See col. 79, lines 65-col. 80, line 16.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to use deep-learning model for determining whether the voice transmitted by the service provider is an utterance of the attendant. The Examiner notes that Gupta discloses determining whether the voice corresponds to a voice of a live operator or if it is part of an on-hold announcement. Cosic discloses that it was known to also detect a human voice by using a deep learning system as well as other models. Thus, both Gupta and Cosic are directed to using models to determine whether a live person is present. The Examiner finds that using a deep leaning model is a known technique and Cosic discloses the use of a deep leaning model can also be used by a speech processing system for the detection of voice for the detection of not only human voices but other sounds. Therefore, it would have been predictable to a person of ordinary skill in the art to use different models for the detection of a human voice as well as for the detection of other sounds so in order to determine the presence of a live operator.
The Examiner finds that one of ordinary skill in the art could have combined the element as claimed by known methods (using models for the detection of keywords as well as for detecting a human voice) and therefore, one of ordinary kill in the art would have recognized the results of the combination were predictable based on the teachings that using models were known to be used for the detection of a human voice such as a live operator.
The Examiner notes that although Cosic discloses using a deep learning model, Gupta and Cosic do not specifically disclose wherein the deep-learning model is learned with respect to multiple audio signals related to an attendant voice.
The Examiner however notes that it would have been obvious to a person of ordinary skill in the art to train the deep-learning model of the Gupta/Cosic combination since Cosic already discloses that its model is a deep learning model and it able to distinguish a human voice from other sounds.
Nonetheless, the Examiner notes that Gross is directed to determine with its trained computer system whether a speech utterance was vocalized by a machine or a human. See col. 7, lines 25-47. As set forth therein, Gross discloses determining acoustical characteristics between a human voice and a computer synthesized voice and these acoustical characteristics are used to train the speech models.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to train the deep-learning model of the Gupta/Cosic combination based multiple audio signals related to an attendant voice (i.e. a human voice).
As set forth above, Gupta already discloses of using a speech model to distinguish a voice from an attendant and a status announcement. Gross discloses it was known to train a system based on acoustical characteristics (multiple audio signals related to a human voice) for a human. Gross, like Gupta, also uses speech models. As explained above, Cosic discloses it was known to use artificial intelligence including deep-learning models for recognizing a human voice.
Therefore, it would have been predictable for a person of ordinary skill in the art to use a deep-learning model which was learned with respect to multiple audio signals related to an attendant voice in order to accurately differentiate a human from a machine. See col. 15, lines 32-43.
in response to completion of connection to the attendant, output a notification indicating that the connection to the attendant has been completed using the speaker or the touchscreen display; and
See paragraph [0055] which discloses that the electronic device can alert the user that the live operation is present on the telephone line using visual, audio or haptic alerts. See also Figure 6.
in response to reception of a second user input for the output notification, terminate the standby mode.
See paragraph [0057], in response to a reception of a user response to the alert (indication that the user want to regain control of the telephone call). Gupta discloses that the call was previously in a background process and now may become the foreground process (thus standby mode is terminated).
Regarding claim 15:
The electronic device of claim 14, wherein the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to: display an icon indicating a function to provide the standby mode via the touchscreen display; and in response to an input of selecting the icon, switch a mode of the calling application to the standby mode.
Gupta as illustrated in Figure 5, shows the display of an icon (YES) which indicates a function to provide the standby mode via a touchscreen display. As further explained in paragraphs [0050-0051],by selecting the selectable icon 502, the on-hold monitoring will be enabled for the telephone call. See paragraphs [0043 and 0057] which discloses the on-hold monitoring and of the telephone call being a background process (standby mode).
Regarding claim 19:
The electronic device of claim 14, wherein the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to, while the calling application is being executed in the standby mode: refrain from displaying an execution screen of the calling application on the touchscreen display, and restrict a function of the speaker or microphone.
As explained in paragraph [0033], during the on-hold mode (standby mode), the call application is placed in a background process which will free user interface 212 (i.e. restrict a function of the speaker or microphone for the calling application). This would allow the user to use the speaker and user interface for other functions. See also paragraph [0043].
Claim(s) 16-17 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US Patent 2010/0303227 in view of Cosic US 10,402,731 and further in view of Gross and further in view of Binder US Patent Pub. 2022/0230653.
Regarding claim 16:
The electronic device of claim 14, wherein, to cause the at least one processor to determine whether the attendant is connected based on at least one voice transmitted by the service provider, the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to:
by using the determination model, perform comparison to determine whether a first voice from among the at least one voice is similar to the determination model by a threshold value or greater, wherein the determination model is obtained by learning an attendant voice and the first voice.
Gupta discloses using a speech model (paragraph [0036]) for speech processing by the speech processor. Gupta does not specifically disclose performing a comparison to determine whether a first voice from among the at least one voice is similar to the determination model by a threshold value or greater, wherein the determination model is obtained by learning an attendant voice and the first voice.
Nonetheless, Binder discloses in paragraph [0110] of using a sound-type detector which includes a voice activity detector. The sound-type detector includes a frequency-domain analysis of a sound input and generates a spectrogram and then analyzes the spectral components to determine whether the sound corresponds to human speech. See also paragraph [0151], the electronic device determines that the sound is a human voice.
As set forth in paragraph [0141], a user’s voice can be part of a voice enrollment or “training” procedure that can be used to create a reference representation of a person’s voice. See also paragraph [0022] which discloses that a comparison of the sound is made to a reference representation, and a match is determined if it’s within a predetermined confidence. See also paragraph [0113].
Binder further discloses of using acoustic and language models to recognize the speech input include a sequence of phonemes, and a sequence of words. These include acoustic models, language models and HMM. See paragraph [0077].
Thus, Binder discloses of using a model to determine whether a voice (sound input + first voice) is similar to a reference representation of a person’s voice within a confidence level (threshold).
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to perform comparison to determine whether a first voice from among the at least one voice is similar to the determination model by a threshold value or greater, wherein the determination model is obtained by learning an attendant voice and the first voice. The Examiner notes that Gupta, Cosic., Gross and Binder all pertain to determining the presence of a human voice. Therefore, Gupta, Cosic, Gross and Binder are all related to the same field of endeavor. In addition, Binder discloses it was known to use a threshold (confidence level) to determine if a voice is that of a specific person. Gupta is based on determining whether the voice is of a live operator. Therefore, a person of ordinary skill in the art could have combined the elements as claimed by known methods and that the results of the combination were predictable since a threshold/confidence type of comparison can be used to determine if the voice is a human voice of an agent. The Examiner finds that the combination would have yielded a predictable result since Binder discloses of determining whether the voice is a human voice and thus relates to the same type of analysis performed by Gupta. As explained by Binder, by using a confidence threshold, this will improve accuracy in recognizing the voice. See paragraph [0140]
Regarding claim 17:
The electronic device of claim 14, wherein, to cause the at least one processor to determine whether the attendant is connected based on at least one voice transmitted by the service provider, the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to:
extract at least one audio characteristic from a first voice; and perform comparison to determine, using the determination model, whether the first voice is similar to the determination model by a threshold value or greater, wherein the determination model is obtained by extracting characteristics from multiple audio signals related to the attendant voice and learning the extracted characteristics.
Gupta discloses using a speech model (paragraph [0036]) for speech processing by the speech processor. As set forth above, Gross discloses extracting acoustical characteristics with respect to a human voice and to determine a different in acoustical characteristics between a reference human voice and a computer synthesized voice that exceeds a target threshold. See col. 3, lines 38-49.
Gupta does not specifically disclose perform comparisons to determine, using the determination model, whether the first voice is similar to the determination model by a threshold value or greater, wherein the determination model is obtained by learning the at least one audio characteristic extracted from an attendant voice.
Nonetheless, Binder discloses in paragraph [0110] of using a sound-type detector which includes a voice activity detector. The sound-type detector includes a frequency-domain analysis of a sound input and generates a spectrogram and then analyzes the spectral components to determine whether the sound corresponds to human speech. See also paragraph [0151], the electronic device determines that the sound is a human voice.
As set forth in paragraph [0141], a user’s voice can be part of a voice enrollment or “training” procedure that can be used to create a reference representation of a person’s voice. See also paragraph [0022] which discloses that a comparison of the sound is made to a reference representation, and a match is determined if it’s within a predetermined confidence. See also paragraph [0113].
Binder further discloses of using acoustic and language models to recognize the speech input include a sequence of phonemes, and a sequence of words. These include acoustic models, language models and HMM. See paragraph [0077].
Thus, Binder discloses of using a model to determine whether a voice (sound input + first voice) is similar to a reference representation of a person’s voice within a confidence level (threshold) using characteristics of the voice.
In addition, as set forth above, Gross discloses it was known to train a system based on the acoustical characteristics of a human voice relative to a non-human voice. See col. 7, lines 37-47.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to determine whether the first voice is similar to the determination model by a threshold value or greater, wherein the determination model is obtained by learning the at least one audio characteristic extracted from an attendant voice. The Examiner finds that each of Gupta, Cosic, Gross and Binder relate to detecting a human voice and thus are all in the same field of endeavor. Binder discloses the need to improve the accuracy of recognizing the voice of a particular user. See paragraph [0140] of Binder. Thus, it would have been obvious to a person of ordinary skill in the art to use models as well as threshold values in order to determine that the person is a human and specifically from an attendant (live operator) so that the system can determine whether to end the on-hold monitoring mode.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gupta US Patent 2010/0303227 in view of Cosic US 10,402,731 and further in view of Gross and further in view of Haukioja et al. US Patent Pub. 2019/0253558.
Regarding claim 18:
The electronic device of claim 14, wherein, to cause the at least one processor to determine whether the attendant is connected based on at least one voice transmitted by the service provider, the memory stores instructions that, when executed by the at least one processor, cause the at least one processor to:
convert a first voice of the at least one voice into text corresponding to the first voice; determine a correlation between the converted text corresponding to the first voice and the determination model, wherein the determination model is obtained by learning corpus for designated greetings; and based on the correlation including a value equal to or greater than a threshold value, determine that the first voice corresponds to an attendant voice.
Gupta discloses using a speech model (paragraph [0036]) for speech processing by the speech processor. Gupta does not specifically disclose convert a first voice of the at least one voice into text; determine a correlation between the converted text and the determination model, wherein the determination model is obtained by learning corpus for designated greetings; and based on the correlation including a value equal to or greater than a threshold value, determine that the first voice corresponds to an attendant voice.
Haukioja discloses of using an automatic speech recognition system which is configured to analyze an agent’s audio sample by giving the speech to text for detection of keywords and phrases relative to various greetings. See paragraph [0041]. See also paragraph [0044] which disclose performing ASR speech to text analysis on the agent’s audio signal to determine whether the agent under certain keywords. The examiner notes that as set forth in paragraph [0041], the speaker diarization is performed and therefore, a determination is made that the voice corresponds to the agent’s voice. See paragraphs [0025] and [0031]. As set forth in paragraph [0006], the system is part of a learning pattern recognition of a machine learning.
The Examiner further finds that Cosic discloses the use of threshold to determine a match with respect to the detection or recognition techniques when recognizing a human voice. See col. 80, lines 6-16.
Therefore, it would have been obvious to a person of ordinary skill in the art before the effective filing date of the claimed invention to convert a first voice of the at least one voice into text; determine a correlation between the converted text and the determination model, wherein the determination model is obtained by learning corpus for designated greetings; and based on the correlation including a value equal to or greater than a threshold value, determine that the first voice corresponds to an attendant voice. The Examiner finds that Gupta discloses that it was known to look for certain greetings (see paragraphs [0052, 0075 and 0075] of Gupta). Gupta discloses processing the speech but does not specifically disclose converting the speech to text. Haukioja discloses it was known to perform automatic speech recognition by converting an agent’s speech into text for the purpose of detection of keywords and for detection of a specific agent. In addition, using a threshold to determine whether the voice corresponds to the live operator of Gupta would have further been obvious to a person or ordinary skill in the art since Cosic discloses that it was known to use thresholds to determine a match using detection or recognition techniques. As set forth above, the examiner found that Gupta discloses the use of methods to recognize words that are being spoken. Thus, a person of ordinary skill in the art would have found it obvious to use thresholds in order to verify if there was a match between what was spoken and what is set forth in the system as a keyword to look for in determining whether a live operator is present.
Allowable Subject Matter
Claims 1-13 and 20 are allowed.
The following is a statement of reasons for the indication of allowable subject matter: With respect to claim 1, the prior art of record does not disclose “identify a service provider and a detailed service based on at least a part of the first user input; select a first menu corresponding to the detailed service from menu information comprising one or more detailed services provided by the service provider; attempt to connect a call to the service provider using the communication circuit; based on the call to the service provider being connected, control the communication circuit to transmit one or more responses until reaching a step corresponding to the first menu in response to one or more voice prompts provided by the service provider” in combination with the rest of the limitations of the claim.
With respect to claim 20, the prior art of record does not disclose “control the communication circuit to receive a request for a call connection to a service provider from an external electronic device, wherein the request comprises user information of the external electronic device and at least one piece of keyword information related to a detailed service and the service provider; in response to receiving the request, acquire an identification number for the call connection to the service provider; select a first menu corresponding to the detailed service included in the request from menu information comprising one or more detailed services provided by the service provider; control the communication circuit to attempt to connect a call between the service provider and the external electronic device; based on the call to the service provider being connected, control the communication circuit to transmit one or more responses until reaching a step corresponding to the first menu in response to one or more voice prompts provided by the service provider” in combination with the rest of the limitations of the claim.
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 Ovidio Escalante whose telephone number is (571)272-7537. The examiner can normally be reached on Monday to Friday - 6:00 AM to 2:30 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michael Fuelling, can be reached at telephone number (571)272-7537. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Ovidio Escalante/
Primary Examiner, Art Unit 3992
Conferees:
/MATTHEW E HENEGHAN/Primary Examiner, Art Unit 3992 /M.F/Supervisory Patent Examiner, Art Unit 3992
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