DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendment
Applicants’ submission, filed on 06/08/2026, in response to claims 1, 5-6, and 8-9 rejection from the non-final office action (03/30/2026), by amending claims 1 and 8 and cancelling claims 5 and 9 is entered and will be addressed below.
Claim Interpretation
The previously added limitations “wherein the measurement result of the abnormality comprises at least one of (i) a number of particles adhering to the substrate and (ii) a reflected-wave intensity“ of claims 1 and 8, this is considered an intended use of the apparatus. The controller does not have a mechanism to detect whether the abnormality signal is due to particles adhering to the substrate, or reflected-wave intensity, or any other abnormality. An apparatus that is capable of detecting such abnormality is considered read into this portion of the claim.
It has been held that claim language that simply specifies an intended use or field of use for the invention generally will not limit the scope of a claim (Walter, 618 F.2d at 769, 205 USPQ at 409; MPEP 2106). Additionally, in apparatus claims, intended use must result in a structural difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure is capable of performing the intended use, then it meets the claim (In re Casey, 152 USPQ 235 (CCPA 1967); In re Otto, 136 USPQ 458, 459 (CCPA 1963); MPEP2111.02). When the structure recited in the reference is substantially identical to that of the claims, claimed properties or functions are presumed to be inherent (In re Best, 562 F.2d 1252, 1255, 195 USPQ 430, 433 (CCPA 1977); MPEP 2112.01).
Election/Restrictions
Claim 10 remain withdrawn from further consideration pursuant to 37 CFR 1.142(b) as being drawn to a nonelected Invention Group II, there being no allowable generic or linking claim.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 5-6, and 8-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claim 1 recites “the controller obtains a two-dimensional distribution representing the state of plasma with respect to installation positions of the plurality of sensors, from data obtained from the plurality of sensors, and estimates the state of plasma in the processing container based on the obtained two-dimensional distribution, wherein the controller obtains the two-dimensional distribution by interpolating … Zernike polynomials … wherein the controller performs machine learning… wherein the controller estimates a distribution of the electron density of the plasma …” (and similarly claim 8). The limitation of estimating the two-dimensional distribution of plasma, interpolating, decomposing by Zernike polynomials, machine learning, is a process that, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components. That is, other than reciting “the controller,” nothing in the claim element precludes the step from practically being performed in the mind. For example, but for the “by a processor” language, “estimating” in the context of this claim encompasses the user manually drawing a two-dimensional sensor reading. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
This judicial exception is not integrated into a practical application. In particular, the claim only recites one additional element – using a controller to perform estimation and determination. The controller in this step is recited at a high-level of generality (i.e., as a generic controller performing a generic computer function of estimating two-dimensional distribution based on sensor data and determining abnormality) such that it amounts no more than mere instructions to apply the exception using a generic computer component. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional element of using a processor to perform estimating step amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claim is not patent eligible.
Applicants argue that the claim involves physical hardware, sensors that measure specific physical plasma states. However, the input does not make it a practical Application.
Similarly, “a storage part storing a first predictive model that learns a relationship between a processing result of the plasma processing and the component intensity of each term of the Zernike polynomials, and a second predictive model, wherein the controller predicts the component intensity of each term of the Zernike polynomials which results in a predetermined processing result on the substrate, by the first predictive model, and predicts the processing condition of the plasma processing in which the component intensity of each term of the Zernike polynomials becomes the predicted component intensity, by the second predictive model” of claim 8 is an abstract idea.
Dependent claim 6 is also rejected under USC 101 at least due to dependency to rejected claim 1.
Claim Rejections - 35 USC § 103
The text of those sections of Title 35, U.S. Code not included in this action can be found in a prior Office action.
Claims 1, 6, and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Bao et al. (US 20150124250, hereafter ‘250), in view of Collins et al. (US 20080182416, hereafter ‘416), Lian et al. (US 20070249071, hereafter ‘071), Matsumoto et al. (US 20050009347, hereafter ‘347), and Mihaylov et al. (US 20170140905, hereafter ‘905).
‘250 teaches some limitations of:
Claims 1 and 8: FIG. 1 shows an embodiment of a plasma processing system 10 equipped with a plasma optical emission spectroscopy (OES) system 15. Plasma processing system 10 comprises plasma processing chamber 20, inside which a substrate holder 30 is disposed, such as an electrostatic chuck, for receiving a substrate 40 to be processed. RF and/or microwave power is supplied to the plasma processing chamber 20 (not shown) to ignite and sustain a plasma 50 proximate the substrate 40, wherein the energetic chemical species from the plasma 50 are used to perform a plasma processing step on substrate 40 ([0027], includes the claimed “A plasma processing apparatus comprising: a processing container in which a mounting table on which a substrate is mounted is arranged and plasma processing is performed”);
In the exemplary embodiment of FIG. 2, two optical detectors 60 are used to collect plasma optical emission spectra … Alternatively, a third or more optical detectors, each with an associated ray fan, may be used ([0031], 3rd and 7th sentences, includes the claimed “a plurality of sensors for detecting a state of plasma generated in the processing container”);
The resultant optical detector output Di represents … (this is a valid assumption because the plasma density and thus plasma light emission is generally low in these areas) ([0041], includes the claimed “and the state of plasma being at least one of electron density, ion density and electron temperature of the plasma“),
The plasma optical emission spectroscopy (OES) system 15 is used to acquire plasma optical emission spectra via at least one optical detector 60, which communicates the acquired plasma optical emission spectra to and is controlled by controller 80 ([0028], includes the claimed “and a controller for estimating a state of plasma in the processing container based on the state of plasma obtained from the plurality of sensors”),
Controller 80, as mentioned before, is used to control the plasma optical emission spectroscopy (OES) system 15, and to also compute the (1) plasma optical intensity distribution as a function of spatial location and wavelength, and to compute (2) the spatial distribution of chemical species of interest from the computed plasma optical intensity distribution. This information can then be used for process development, plasma processing tool development, in-situ plasma process monitoring, plasma process fault detection, plasma process endpoint detection, etc. ([0030], includes the claimed “wherein the controller performs a frequency analysis on the detected current value output from each of the plurality of sensors to calculate at least one of the electron density, the ion density, and the electron temperature of the plasma as the state of plasma“, note wavelength [Symbol font/0x6C] and frequency f of the light are related as c = [Symbol font/0x6C] x f, c is the speed of light),
The present invention relates to a method, computer method, system, and apparatus for measuring concentrations of chemical species in semiconductor plasma processing using plasma optical emission spectroscopy (OES). Specifically, it relates to determining two-dimensional distributions of plasma optical emissions from which two-dimensional distributions of chemical species concentrations can be determined ([0003], includes the claimed “wherein the controller obtains a two-dimensional distribution representing the state of plasma with respect to installation positions of the plurality of sensors, from data obtained from the plurality of sensors, and estimates the state of plasma in the processing container based on the obtained two-dimensional distribution”),
The disclosed technique is computationally simple and inexpensive, and involves the use of an expansion of the assumed optical intensity distribution into a sum of basis functions that allow for circumferential variation of optical intensity. An example of suitable basis functions are Zernike polynomials (abstract), other basis functions can be chosen in this application, as long as they are orthogonal and as long as their derivatives are continuous over the unit circle, just as is the case with Zernike polynomials ([0045], includes the claimed “wherein the controller obtains the two-dimensional distribution by interpolating a state between the installation positions of the plurality of sensors using an orthogonal function system, from the data obtained from the plurality of sensors, wherein the orthogonal function system is Zernike polynomials, wherein the controller decomposes the data obtained from the plurality of sensors into components of terms of the Zernike polynomials, and reconstructs each term of the Zernike polynomials with a component intensity of each decomposed term, thus obtaining the two-dimensional distribution”, the optical detectors 60 is capable of detecting reflected light intensity, includes the claimed “wherein the measurement result of the abnormality comprises at least one of (i) a number of particles adhering to the substrate and (ii) a reflected-wave intensity“, see claim interpretation above).
‘250 does not teach actively generating light signal from the optical detectors 60 at various wavelength/frequency. ‘250 does not teach the other limitations of:
Claim 1: (1A) the plurality of sensors being installed on a top wall portion of the processing container,
(1B) wherein each of the plurality of sensors includes an antenna part disposed inside the processing container,
(1C) wherein the controller is configured to output, to each of the plurality of sensors, a signal of a predetermined frequency,
wherein each of the plurality of sensors is configured to detect a current value corresponding to a signal from the plasma in response to the signal of the predetermined frequency and to output the detected current value to the controller,
(1D) wherein the controller performs machine learning on a data set in which a measurement result of the abnormality occurring during the plasma processing is associated with the component intensity of each term of the decomposed Zernike polynomials, and identifies the component intensity of the term of the Zernike polynomials that contributes highly to the abnormality,
(1E) wherein the controller estimates a distribution of the electron density of the plasma on an upper surface of the mounting table, by solving a diffusion equation based on the obtained two-dimensional distribution, and
(1F) wherein the controller detects an abnormality depending on whether the component intensity of each term of the decomposed Zernike polynomials is within a predetermined normal range.
Claim 8: (8A) the plurality of sensors being installed on a top wall portion of the processing container,
(8B) wherein each of the plurality of sensors includes an antenna part disposed inside the processing container,
(8C) wherein the controller is configured to output, to each of the plurality of sensors, a signal of a predetermined frequency,
wherein each of the plurality of sensors is configured to detect a current value corresponding to a signal from the plasma in response to the signal of the predetermined frequency and to output the detected current value to the controller,
further comprising:
(8D) a storage part storing a first predictive model that learns a relationship between a processing result of the plasma processing and the component intensity of each term of the Zernike polynomials, and a second predictive model that learns a relationship between a processing condition of the plasma processing and the component intensity of each term of the Zernike polynomials,
wherein the controller predicts the component intensity of each term of the Zernike polynomials which results in a predetermined processing result on the substrate, by the first predictive model, and predicts the processing condition of the plasma processing in which the component intensity of each term of the Zernike polynomials becomes the predicted component intensity, by the second predictive model,
(8E) wherein the controller estimates a distribution of the electron density of the plasma on an upper surface of the mounting table, by solving a diffusion equation based on the obtained two-dimensional distribution, and
(8F) wherein the controller detects an abnormality depending on whether the component intensity of each term of the decomposed Zernike polynomials is within a predetermined normal range.
Claim 6: wherein the controller monitors the component intensity of each term of the decomposed Zernike polynomials during plasma processing, and detects whether the component intensity of any term has changed beyond an allowable value.
‘416 is an analogous art in the field of PLASMA PROCESS UNIFORMITY ACROSS A WAFER BY APPORTIONING POWER AMONG PLURAL VHF SOURCES (title), relating to plasma density distribution ([0036], 3rd sentence). ‘416 teaches that the metrology tool 272 may embody in-situ sensors may provide real-time signals to the controller 270. OES (optical emission spectroscopy) sensors may be placed on the ceiling 204 at various radii, providing an indication of radial plasma excited species density (Fig. 1, [0032], 7th-8th sentence).
Before the effective filing dates of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have re-arrangement the OES sensors at various radii of the ceiling of the process chamber of ‘250 (the limitations of 1A and 8A), as taught by ‘416, for the purpose of obtaining radial plasma distribution, as taught by ‘416 ([0032], 7th-8th sentence).
‘071 is an analogous art in the field of Neural Network Methods And Apparatuses For Monitoring Substrate Processing (title), a substrate processing system 100 for fabricating integrated devices (Fig. 1, [0019]), an optical electromagnetic emission (OES) monitor assembly to monitor the chamber plasma state ([0035], last sentence).
‘071 teaches that monitoring a first set of reflected electromagnetic radiation from an electromagnetic radiation source during processing ([0007]), The measuring tool 103 generally includes an optics assembly 104 coupled to an actuator assembly 105, an electromagnetic radiation source (e.g., light source 154), a spectrometer 156, and a computer 162 … the controller 136 is used for controlling the measuring tool 103, while the computer 162 is used for data collection and analysis. The computer 162 may include a neural network module (e.g., neural network software 170). The neural network software 170 may include an executable program module, for example a Dynamic Link Library (DLL) that performs one or more neural network (e.g., a multilayer perceptron network) functions at runtime. The neural network software 170 may also be stored and/or executed by a second CPU (not shown) ([0027]), A spectrometer may be used to collect the radiation from a broadband light source, split the radiation into discrete wavelengths, and detect the intensity of the radiation at each discrete wavelength ([0028], i.e. discrete frequencies), Output from the spectrometer 156 is delivered to the computer 162 or to the controller 136 for analysis and may be used as learning data by a multilayer perceptron network ([0033], i.e. machine learning) ([0027]), for the purpose of improving the sensitivity and accuracy of the measuring tool ([0030], 2nd sentence).
Before the effective filing dates of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have added a light source/antenna 154 and to have applied neural network learning of ‘071, to the OES and controller of ‘250 (the limitations of 1C and 8C), for the purpose of improving the sensitivity and accuracy of the measuring tool, as taught by ‘071 ([0030], 2nd sentence).
‘347 is an analogous art in the field of Method And Apparatus For Measuring Electron Density Of Plasma And Plasma Processing Apparatus (title), This plasma electron density measuring apparatus includes a vector network analyzer in a measuring unit, which measures a complex reflection coefficient and determines a frequency characteristic of an imaginary part of the coefficient (abstract), spectrum of a certain wavelength is extracted from plasma light exiting from the processing chamber through the window using a spectroscope or an optical filter, and the intensity or variation of the extracted spectrum is measured ([0008]). ‘347 teaches that a plasma electron density measuring apparatus includes: a cylindrical insulating pipe 50 fixedly attached to the chamber 10; a coaxial cable 52 provided with a probe portion (antenna probe) 52a formed by exposing a core wire of a front end of the coaxial cable 52, and slidabley inserted into one end (left-hand end in FIG. 1) of the insulating pipe 50; a measuring unit 54 for measuring a resonance frequency and electron density through the coaxial cable 52 with respect to the plasma PZ generated in the chamber 10; and a linear actuator 56 for moving the coaxial cable 52 in an axial direction thereof (Figs. 1-2, [0076]), In a series resonant state, signal power transmission attributable to the plasma reactance X is maximized and the energy of an incident wave from the probe portion 52a is transmitted to electrons in the plasma through a so-called Landau damping mechanism ([0099], 7th sentence), for the purpose of high precision in any plasma condition ([0021]).
Before the effective filing dates of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have incorporated an antenna probe 52a into the chamber, as taught by ‘347, to the combined apparatus of ‘250 and ‘071 (the limitations of 1B and 8B), for the purpose of high precision in any plasma condition, as taught by ‘347 ([0021]).
‘905 is an analogous art in the field of ADVANCED OPTICAL SENSOR AND METHOD FOR PLASMA CHAMBER (title), FIG. 2A is a top view schematic of the plasma processing system 100 equipped with the optical detection system 102 according to one example. The light collector 110 includes multiple optical paths covering a large portion of the plasma processing chamber 104 volume and the surface of the substrate 108, a focus ring 122, and the antenna 120 (detection area 118) ([0030]), arcing can cause material degradation of a wafer being processed and/or damage to the plasma processing system itself. Specific conditions may lead to abnormal discharge (arc) in the plasma processing chamber 104. The abnormal discharge may release acoustic, RF, chemical, and light energy. By detecting one or more of these energies (signals), it is possible to detect the arc as an abnormal discharge ([0028]), Light emitted in the plasma processing chamber 104 is collected by the light collector 110, and is passed to the light detector 112 via an optical fiber (not shown) for example. Then, the detected intensities may be filtered via the filter 300 (e.g., an electronic analog filter). The filtered intensities are then fed to the digital controller 304 via the ADC 302. The digital controller 304 passes the detected intensities to a computer 308 via the interface 306 ([0037], 2nd sentence), FIG. 6B is a schematic that shows the plasma intensity according to one example. Schematic 606 shows the plasma (emission) intensity in time domain. Trace 608 shows the emission intensity captured by a center channel. The center channel captures emission in the center of the plasma processing chamber 104 (e.g., P3 or P4 shown in FIG. 2B). Trace 610 shows the emission intensity captured by a side channel. As shown by trace 608 and trace 610 the plasma is not stable, about 10% fluctuation in emission intensity is observed in time domain ([0066]). ‘905 teaches that A machine learning algorithm, or a trained model (e.g., with dimensional reduction) may be used to discriminate between an abnormal signal (e.g., arc, unstable plasma) and a normal signal (i.e., signal after all filtering applied such optical, electrical filters or the like). Specific artificial intelligence algorithms may be dependent on the individual actual plasma process application (for example, etch runs). A trained model of signal pattern recognition may be used for identification of unusual signals from normal signals under many different scenarios (e.g., spike detection) ([0060]), The process data and instructions may be stored in memory 702. These processes and instructions may also be stored on a storage medium disk 704 such as a hard drive (HDD) or portable storage medium or may be stored remotely ([0068], 3rd sentence).
Before the effective filing dates of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have applied a machine learning algorithm to discriminate an abnormal signal and normal signal and stored the process data in storage medium, as taught by ‘905, to the wavelength data derive from Zernike polynomials of ‘250 (the limitations of 1D and 8D), for the purpose of improving abnormal discharge detection, as taught by ‘905 ([0060]).
‘097 is an analogous art in the field of Plasma Processing Apparatus And Method Of Designing The Same (title), a plasma processing apparatus such as, for example, an etching apparatus, a nitriding apparatus or an oxidation apparatus, for example, to be used in a semiconductor manufacturing process for semiconductor substrate or liquid crystal substrate ([0001]), prevent electric discharge inside the window of three-window structure (middle of [0004]). ‘097 teaches that Specifically, the shape, the size or the disposition of the holes is determined on the basis of an active species distribution at the plasma producing portion and of diffusion calculation, so that plasma active species adjacent the object to be processed has desired concentration and distribution (abstract and throughout ‘097).
Before the effective filing dates of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have adopted diffusion calculation of ‘097, including the electron density of ‘247, in determining two-dimensional distribution of plasma optical emissions and chemical species (the limitations of 1E and 8E), as required by ‘250.
‘963 is an analogous art in the field of Plasma Processing Apparatus And Method (title), in semiconductor device manufacture (col. 1, lines 11-12). ‘963 teaches that in the embodiment shown in FIG. 9, abnormal discharge is detected by detecting changes in emission intensity entering into the emission intensity detector 31. However, it is possible to perform more precise detection of abnormal discharge, by providing a spectrometer for the emission intensity detector 31, to detect the wavelength of specified ions or activated species relating to abnormal discharge (col. 10, line 66 to col. 11, line 5), if abnormal discharge is caused while a plasma is generated and a process is performed, the emission intensity is increased. Then, the emission intensity of a plasma extracted from a quartz window 25 is detected by an emission intensity detector 31. FIG. 10 shows a detection output of an emission intensity detector 31 when abnormal discharge is caused (col. 10, lines 44-50), it is possible to reduce sudden generation of dust, damages on the surface of a target substrate, contamination of the substrate, dielectric breakdown of electric elements of the substrate, and the like which are caused by abnormal discharge, by means of detecting generation of abnormal discharge in a plasma process chamber and of damping abnormal discharge (col. 11, lines 24-30).
Before the effective filing dates of the claimed invention, it would have been obvious to a person having ordinary skill in the art to have applied the optical intensity distribution from ‘250 to detect the abnormal discharge of specified ions according to its wavelength (the limitations of 1F, 8F, and 6), as taught by ‘963, for the purpose of reducing sudden generation of dust, damages on the surface of a target substrate, contamination of the substrate, dielectric breakdown of electric elements of the substrate, as taught by ‘963 (col. 11, lines 27-30).
In case Applicants argue that “wherein the measurement result of the abnormality comprises at least one of (i) a number of particles adhering to the substrate” is not an intended use of the apparatus, ‘963 teaches this abnormality (col. 11, lines 24-30).
Response to Arguments
Applicant's arguments filed 06/08/2026 have been fully considered but they are persuasive.
In regarding to 35 USC 103 rejection, Applicants argue that
A) Uchiyama ‘097 is one-time computation, not a real-time estimation, by a controller during plasma processing of an electron-density distribution at a position different from the sensor’s positions, based on a Zernike-decomposed two-dimensional distribution reconstructed from in-situ sensor data, see the 2nd paragraph of page 11.
This argument is found not persuasive.
In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Each of the items Applicants argued is taught by the other references. For example, electron density by ‘347.
‘250 teaches a controller for real time estimation using Vernike polynomials for interpolation based on sensors. ‘347 is relied upon using diffusion equation as a way to derive active species distribution.
B) Applicants also argue that Tomioka ‘963 does not teach Zernike-polynomial components, see the top of page 12.
This argument is found not persuasive.
This is again attacking reference individually. 250 teaches a controller for real time estimation using Vernike polynomials for interpolation based on sensors. ‘963 is relied to detect the wavelength of specified ions or activated species relating to abnormal discharge (col. 11, lines 4-5).
C) Applicants further repeated the attacking reference individually to each of the secondary references by stating that each does not teach the limitations as taught by other references, and complains five references from different measurement modalities, see the bottom of page 12 to the top of page 14.
This argument is found not persuasive.
Attacking reference individually is already replied above.
The examiner maintain each reference is combined as set forth being analogous art. The measurement modalities are all related to plasma state detection and clearly teaching can be combined.
In regarding to 35 USC 101 rejection, Applicants repeatedly argue that claim include physical apparatus, sensors, plasma parameters, non-generic data processing, and process control, see the middle of page 14 to the middle of page 17.
This argument is found not persuasive.
The controller portion of the claim, nothing in the claimed element precludes the step from practically being performed in the mind.
The examiner notices that the SPEC includes send abnormality info to interface or human manager. However, the examiner considers these steps are still an abstract ides (performed in the mind).
In regarding to intended use portion, Applicants asserted that adding “within a predetermined normal range”, the claim is not intended use. See the bottom of page 17.
This argument is found not persuasive.
Using the analysis results as abnormality being a number of particles adhering to the substrate or a reflected-wave intensity is an intended use. The controller does not have a mechanism to determine the abnormality is causing by either cause.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. US 20060057854 is cited for sensor ([0037]) for diagnosing plasma ([0055]).
US 10559458 is cited for The plasma density is also referred to as “electron density” (col. 6, line 14). US 20030201162 is cited for sensor array 172 on segmented electrode 400 (Fig. 9), which is a top wall/electrode 60 of the plasma chamber 20 (Fig. 1), with microwave ([0004]) and OES ([0011]).
US 20060100824 is cited for computer to implement an abnormal discharge detecting method by intensity of light emission inside the chamber ([0033]).
THIS ACTION IS MADE FINAL. 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 KEATH T CHEN whose telephone number is (571)270-1870. The examiner can normally be reached 8:30am-5:00 pm.
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, Parviz Hassanzadeh can be reached at 571-272-1435. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/KEATH T CHEN/ Primary Examiner, Art Unit 1716
Prosecution Insights