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 .
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.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 19 November 2025 by the applicant has been considered and is included in the file.
Response to Amendment
Claims 1 and 11 were amended 22 September 2025; no new claims were added and no claims have been canceled. Therefore, claims 1-21 are pending in the current application and are addressed below.
Response to Arguments
Applicant's arguments, as discussed below, filed 26 February 2026 have been fully considered but they are not persuasive.
In response to applicant's argument that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971).
On pages 14-18 of Remarks, applicant disagrees with the application of Mesher (US 20200086903 A1) as the primary prior art for teaching independent claims 1 and 11, mainly focusing on the fact that the intended use of Mesher does not match the intended use of the instant application. Applicant also states that it is impermissible hindsight to combine other sources (such as Singh, US 20180339720 A1) however as both Mesher and Singh were published prior to the effective filing date of the instant application, one of ordinary skill in the art of distance measurement and data analysis regarding rail wear would be readily able to combine the two sources.
Applicant again attests that the sensors of Mesher only operate at oblique angles due to the need to see information printed on the rail webs, further disagrees that sensors are in an M x N array and aimed downward, and that Mesher’s arrangement is far more complex than the instant application (Remarks, pgs. 18-21). The examiner feels that many of these points were appropriately discussed in the prior office action, dated 28 October 2025, see paragraphs 4, 5, 7 and 9.
Regarding the last point of processing needs, the examiner again respectfully points out that any time-of-flight (ToF) system requires some processing from measurements collected by a sensor, as to obtain a distance measurement in a ToF system the time between emission and reception of a signal is measured, which is then used to determine a most likely distance to an object, such as a rail head. Both the embodiments of Mesher and the current application which utilize ToF sensors in plurality will require calculations of some form.
In response to applicant's argument (Remarks, pgs. 20, 27) that the references fail to show certain features of the invention, it is noted that the features upon which applicant relies (i.e., that the sensors are “individual”, per se, and do not include an array of pixels within each sensor which will require averaging of signals) are not recited in the rejected claim(s). Although the claims are interpreted in light of the specification, limitations from the specification are not read into the claims. See In re Van Geuns, 988 F.2d 1181, 26 USPQ2d 1057 (Fed. Cir. 1993). The current application does not claim, nor depend upon, an array of individual receivers, such as individual SPADs. The examiner points to paragraph [056] of the instant application, which states that the intended sensors within the system “…may include, e.g., a model AFBR-S5OMV85G which is commercially available from Broadcom Inc. of San Jose, California, USA. According to the Broadcom data sheet the transmitter thereof emits IR light from an 850nm wavelength laser light source and the receiver thereof has a 4x8 pixel array of adjacent receiver elements (32 total) of which about 7-16 pixels are typically illuminated by the reflected IR light”.(emphasis added). It is well known to one of ordinary skill in the art of ToF ranging and analysis that to get an M x N array of data out of an M x N array of sensors such as disclosed above, an average or other data analysis will need to be performed on the range data collected for each individual sensor before it is output into the M x N array of range data. For the purposes of examination and in response to the amendments to the independent claims 1 and 11, the examiner has updated the rejection of claims 1 and 11 to explicitly provide art which teaches this concept.
Applicant’s arguments, see Remarks, pgs. 23-26, filed 26 February 2026, with respect to the rejection(s) of claim(s) 1 and 11 (and therefore 2-10 and 12-21) under 35 USC § 103 have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of both newly found prior art references and more in depth interpretation of the previously applied prior art.
The previously applied reference (Mesher) teaches that an array of ToF sensors may be used ([0131] – [0132]; Figs. 11 and 12), where “a plurality of ToF sensors may be used” and “the one or more sensors (212) may be arranged in various patterns”, such as shown in the left-hand group of Fig. 12, which shows a 2x6 array of sensors (212) which teaches on the limitation of a varied size array of sensors. Mesher does disclose that each ToF sensor may be “a ToF camera, such as those manufactured by Basler AG or pmdtechnologies AG.” ([0131]), but is silent on the exact nature of the model of camera and the fact that these ToF cameras have both emitting and receiving portions to them. Therefore, prior art which is more descriptive in the nature of ToF cameras is now referenced in combination with Mesher (and Singh) and is discussed below.
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 1-21 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.
Claims 1 and 11 have been amended to include the limitation “…the array of sensors that are configured in an M x N array to…” (claim 1, lines 15-16), however an M x N array of sensors has already been introduced as “an array… arranged side by side in an M x N planar array…” (claim 1, lines 4-5). It is unclear if the latter M x N array is a separate array or the priorly introduced planar array. For examination purposes, the latter limitation will be interpreted as “…the array of sensors that are configured in the M x N planar array to…”, as it is believed both limitations apply to the same planar array of sensors based upon the specification.
Claims 2-10 and 12-21 are similarly rejected as being dependent upon claims 1 and 11, respectively.
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.
Claims 1-3, 6-13, and 16-21 are rejected under 35 U.S.C. 103 as being unpatentable over Mesher (US 20200086903 A1), in view of Vanek (US 20110224840 A1) and further in view of Singh (US 20180339720 A1) and BaslerAG (BaslerAG Time-of-Flight camera, 2019 Wayback Machine/Internet Archive access on 21 April 2026 for dates in December 2019).
Regarding independent claim 1, Mesher teaches a system for detecting and measuring track defects including degree of wear-defects, structural defects, and rail track distortions (Fig. 1, (100); [0017]), wherein the rail has a rail head having a width and a height ([0003]), the system comprising:
an array of sensors including plural time-of-flight (TOF) distance measuring sensors arranged side by side in an M x N planar array, where M and N are integers ([0131] – [0132]; Figs. 11 and 12, where “a plurality of ToF sensors may be used” and “the one or more sensors (212) may be arranged in various patterns”, such as shown in the left-hand group of Fig. 12, which shows a 2 x 6 array of sensors (212)),
for measuring vertical distance to the rail head for directly obtaining dimensional data of the rail head ([0131] - [0134], [0145], [0153]; Figs. 11, 12 where sensors (212) are aimed vertically downwards and are time-of-flight (ToF) sensors);
wherein the array of sensors is configured to have a substantially vertically downward field of view that covers more than half of the width of the rail head of a known good rail ([0131] - [0134], [0145], [0153]; Figs. 11, 12 where system may operate three-dimensional mapping by using only sensors (212), and sensors (212) are aimed vertically downwards and are time-of-flight (ToF) sensors) and that covers a predetermined distance along the rail in a train traveling direction ([0132]);
wherein the field of view of the array of sensors is configured to have distance measuring sensor spot areas of each of the distance measuring sensors of the array of sensors that are configured in an M x N array to together cover at least the more than half of the width of the rail head and the predetermined distance along the rail in the train traveling direction, ([0130]-[0133]; Figs. 11-12) and wherein the spot area of each distance measuring sensor is selected to complement others of the spot areas of the other distance measuring sensors to obtain distance measurement data to the rail head within the field of view ([0031]; Figs. 18, 28; where “is selected to complement others” is interpreted to mean that sensor locations are chosen to maximize coverage while minimizing individual field of view overlap”);
whereby an array of distance measurement data therefrom is representative of the covered area of the rail head width and in the train traveling direction ([0130]),
wherein the distance measuring sensors of the array of sensors are synchronized with each other for measuring distance substantially concurrently ([0031]) and wherein the dimensional data therefrom is geo-tagged by being associated with a location ([0022]) and a time ([0049]) that are representative of where and when the distance measuring sensors measure distance ([0018]);
a memory for receiving and storing the distance measurement data including the associated geo-tagging data ([0036]);
a processor for processing the distance measurement data to the rail head to determine the dimensional data of the rail head ([0031]) including at least the height of the rail head with respect to that of a known good rail ([0034]), the processor processing the dimensional data for detecting rail wear and rail defects, ([0034])
wherein the array of sensors are operated to directly obtain dimensional data from which rail wear, surface defects, rail structural defects, rail fractures and cracks, and/or a gap in a joint in the rail, are measured and detected ([0034] - [0040], [0145], [0153], [0173]).
Mesher teaches use of a ToF camera/sensor as each of the sensors within the M x N array used in the various arrayed patterns, where the at least one sensor of the system comprises a light emitter and a camera in communication with the processor ([0019]), such as that each ToF sensor may be “a ToF camera, such as those manufactured by Basler AG or pmdtechnologies AG.” ([0131]), but does not explicitly state that these ToF cameras have both emitting and receiving portions to them. Mesher does not teach that the dimensional data of the rail head specifically includes measuring the width of the rail head, nor does Mesher explicitly discuss the format of the data collected by the array of sensors.
The time-of-flight (ToF) camera sold by BaslerAG at the time of filing, as noted by Mesher, includes both a light source and a camera/receiver, and measures by pulsed ToF, which is a form of direct ToF detection and measurement.
Singh teaches using dimensional data of the rail head to determine measurements such as height, width, and relative difference across the width of a rail ([0026], [0170]; Fig. 10 (E)).
Vanek teaches methods od real time image collection and enhancement for LIDAR systems, such as used in flash lidar, where the size of the data point array (also known as a point cloud) is proportional to the size of the sensor array as well as the number of pixels within each detector, and a 2-dimensional M x N array of detectors will output at least a 2-dimensional M x N array of data ([0092] – [0102]; In systems where the number of detector pixels, for example, is 128x128 there will be 16,384 pixels and therefore 16,384 data points representing a range R for that frame.) Vanek further notes that flash LIDARs form 3-D images based on range measurements on a per-pixel basis ([0049]).
To one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to modify Mesher to incorporate the specific teachings of BaslerAG to use ToF sensors which explicitly have both transmitting and receiving portions within the array of sensors, the teachings of Vanek that 2-D arrays of pixels/detectors will form a 2-D array of range measurements, and the teachings of Singh to include the width of a rail head by processing distance data collected with time-of-flight sensors with a reasonable expectation of success. The arrays of the current application (as shown in Figs. 3A, 3B or 4A) are aligned so that each subsequent row is horizontally offset from the prior row, which is similarly oriented to the example array in Fig. 12 of Mesher. To one of ordinary skill in the art, the 2 x 6 array as shown in the left-hand side of Fig. 12 of Mesher would directly incorporate the sensors that Mesher references, such as the ToF camera of BaslerAg, with a predictable result of a simultaneously controlled array which may be chosen in size to gather ToF data for an area which covers at least half the width of a standard rail head. Further, Mesher notes that “Any rail feature that can be measured using visual analysis can be compared by the system” ([0173]), which includes physical measurements such as the height or width of a rail head. This would have predictable results in determining wear or defects on multiple features of a rail and rail head ([0158]).
Regarding claim 2, Mesher (as modified above) teaches the system of claim 1, wherein the array of sensors are configured to have a field of view that covers more than the width of the rail head of the known good rail. ([0130]; Fig. 12).
Regarding claim 3, Mesher (as modified above) teaches the system of claim 1, wherein:
the array of sensors and the processor are configured to determine wear and/or deformation including plastic flow of the rail; or
the array of sensors and the processor are configured to determine a thickness of the rail head in comparison to the known-good rail including wear thereof in the vertical and horizontal directions; or
the array of sensors and the processor are configured to determine surface defects including squats, palling, corrugation and deformation in the vertical direction ([0020], [0177]);
or the array of sensors and the processor are configured to determine a dimension of gaps at rail joints and/or an aggregate the dimensions of rail joints over a length of at least two rail joints (Fig. 46; [0158], [0176]); or
any combination of any one or more of the foregoing. ([0178]).
Regarding claim 6, Mesher (as modified above) teaches the system of claim 1, wherein the array of sensors is a first array of sensors, the system further comprising:
a second array of sensors including plural time-of-flight (TOF) distance measuring sensors arranged for measuring vertical distance to the rail head for directly obtaining dimensional data of the rail head, wherein the second array of sensors is functionally the same as the first array of sensors recited in claim 1,
wherein the first array of sensors is positioned to have a first rail of a track within its field of view and the second array of sensors is positioned to have a second rail of the track within its field of view ([0131] - [0134], [0145], [0153]; Figs. 11, 12 where sensors (212) are aimed vertically downwards and are time-of-flight (ToF) sensors);
wherein the memory is for receiving and storing distance measurement data from the second array of sensors including associated geo-tagging data ([0031]-[0042]).
Regarding claim 7, Mesher (as modified above) teaches the system of claim 6 wherein: the processor processes the distance measurement data to the rail head of the second rail from the second array of sensors to determine the dimensional data of the rail head of the second rail the height of the rail head of the second rail with respect to that of a known good rail, the processor processing the dimensional data for detecting rail wear and rail defects of the second rail, wherein the second array of sensors is operated to obtain dimensional data from which rail wear, surface defects, rail structural defects, rail fractures and cracks, and/or a gap in a joint in the rail, are measured and detected.
Mesher does not teach determining the dimensional data of either rail including at least the width of the rail head.
Singh teaches using distance information collected from sensor arrays to determine the width of a rail head (Singh [0026]; Fig. 10 (E)).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Mesher (in view of Singh) to incorporate the specific teachings of Singh to include determining the width of a rail head by processing distance data with a reasonable expectation of success. Mesher notes that “Any rail feature that can be measured using visual analysis can be compared by the system” ([0173]), which includes physical measurements such as the height or width of a rail head.
Regarding claim 8, Mesher (as modified above) teaches the system of claim 6.
Mesher does not teach using the distance measurement data from the two sensor arrays to determine a gauge of the track.
Singh teaches the processing of the distance measurement data [0026] to the rail head of the first rail from the first array of sensors and the distance measurement data to the rail head of the second rail from the second array of sensors ([0033]) to determine a gauge of the track including the first and second rails with respect to that of a known good rail ([0204]; Fig. 11).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Mesher (in view of Singh) to incorporate the specific teachings of Singh to also calculate track gauge by processing the distance data from sensors associated with both rails with a reasonable expectation of success. Mesher describes that measuring values for the gauge side of rails becomes important as it allows for a running calculation of the difference of the two rail elevations which may represent other track distortions along a crosstie or rail seat ([0149]-[0151]), as well as for determining rail cant angles ([0174]).
Regarding claim 9, Mesher (as modified above) teaches the system of claim 1, wherein: the distance measuring sensors of the array of sensors are synchronized to operate at a repetition rate that is directly proportional to a speed at which the array of sensors is moving in the train traveling direction. ([0135])
Regarding claim 10, Mesher (as modified above) teaches the system of claim 1, further comprising a camera associated with the array of sensors for providing visual data representing the rail within the field of view of the array of sensors, ([0019]) wherein the visual data is geo- tagged by being associated with the location and time that are representative of where and when the distance measuring sensors measure distance. ([0022])
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 11 is similarly rejected to claim 1.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 12 is similarly rejected to claim 2.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 13 is similarly rejected to claim 3.
Regarding claim 16, Mesher (as modified above) teaches the method of claim 11, including: calibrating the array of sensors to a known good track and/or to a known-good rail ([0047]-[0049]; reference values are based on manufacturer values).
Regarding claim 17, Mesher (as modified above) teaches the method of claim 11, including:
configuring the array of sensors and a processor to determine surface defects including squats, palling, corrugation and deformation ([0155], [0173]; Fig. 45), or
or configuring the array of sensors and the processor to determine a dimension of gaps at rail joints and/or an aggregate the dimensions of rail joints over a length of at least two rail joints (Fig. 46; [0158], [0176]); or
configuring the array of sensors and the processor to determine a rate of defects over a predetermined period of time; or
any combination of any one or more of the foregoing ([0173]).
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 18 is similarly rejected to claim 6.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 19 is similarly rejected to claim 7.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 20 is similarly rejected to claim 9.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 21 is similarly rejected to claim 10.
Claims 4, 5, 14 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Mesher (US 20200086903 A1), in view of Vanek (US 20110224840 A1) and further in view of Singh (US 20180339720 A1) and BaslerAG (BaslerAG Time-of-Flight camera, 2019), as applied above, and further in view of Naithani et al. (hereinafter Naithani; US 20150294153A1).
Regarding claim 4, Mesher as modified above teaches the limitations of claim 1, where a processor which is configured to retrieve distance measurement data (Mesher, [0178]).
Mesher fails to teach wherein:
the processor is configured to retrieve distance measurement data for a predetermined period of time for determining a rate of wear and/or of defects in track and/or rail over the predetermined period of time; or
the processor is configured to retrieve distance measurement data for a predetermined period of time for determining a history of wear and/or of defects in track and/or rail over the predetermined period of time; or
the processor is configured to retrieve distance measurement data for a length of track and/or rail within a predetermined geographic area for determining a rate of wear and/or of defects in the track and/or rail over the predetermined geographic area; or
the processor is configured to retrieve distance measurement data for a length of track and/or rail within a predetermined geographic area for determining a history of wear and/or of defects in the track and/or rail over the predetermined geographic area; or
the processor is configured to retrieve distance measurement data for identifying wear and/or defects in the track and/or rail that require inspection, maintenance and/or repair within a predetermined period of time; or
the processor is configured to retrieve distance measurement data for identifying wear and/or defects in the track and/or rail that require inspection, maintenance and/or repair within a predetermined geographic area; or
the processor is configured to retrieve distance measurement data for determining a history of rates of wear and/or defects in the track and/or rail that have required inspection, maintenance and/or repair within a predetermined period of time; or
the processor is configured to retrieve distance measurement data for determining a history of rates of wear and/or defects in the track and/or rail that require inspection, maintenance and/or repair within a predetermined geographic area; or
any combination of any one or more of the foregoing.
Singh teaches the processor is configured to retrieve rail sensor data (indexed by time [0031]) for a predetermined period of time ([0070]) for determining a history of wear and/or of defects in track and/or rail over the predetermined period of time; ([0070], [0227])
or the processor is configured to retrieve rail sensor data for a length of track and/or rail within a predetermined geographic area ([0165]) for determining a history of wear and/or of defects in the track and/or rail over the predetermined geographic area; ([0227])
and or the processor is configured to retrieve distance measurement data for identifying wear and/or defects in the track and/or rail that require inspection, maintenance and/or repair within a predetermined geographic area ([0050], [0227]).
Naithani teaches a processor is configured to retrieve distance measurement data for a predetermined period of time ([0024]) for determining a rate of wear and/or of defects in track and/or rail over the predetermined period of time ([0057] - [0060], [0070]; Fig. 6 step (608) determines a historical trend for changes to the route over given time);
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Mesher (in view of Singh) to incorporate the teachings of Singh and Naithani to use the processor as described by Mesher in a process of analysis as describe by Singh and Naithani with a reasonable expectation of success. Mesher describes referencing data stored within a geospatially referenced database to flag wear ([0184]) as well as recording anomalies for specific locations ([0178]) and incorporating the teachings of Singh (to determine history of wear) and Naithani) to determine rates of wear based on track history) would have predictable results of a processor accessing stored data to apply to further analysis.
Regarding claim 5, Mesher (as modified above) teaches the system of claim 4, but fails to teach using the retrieved distance measurement data to determine a model of the rates of wear and/or defects.
Naithani teaches a processor which is configured to determine from the retrieved distance measurement data a model of the rates of wear and/or defects in the track and/or rail;
and the processor is configured to apply the determined model to extrapolate the retrieved distance measurement data for wear and/or defects in the track and/or rail for a selected prediction time in the future ([0051] - [0052]; Fig. 1 analysis unit (128) can predict if and/or when repair or other actions will be necessary with respect to the data regarding damage of rail and following analysis of Fig. 6).
Therefore, to one of ordinary skill in the art before the effective filing date of the claimed invention, it would have been obvious prima facie to further modify Mesher to incorporate the teachings of Naithani to have the processor determine a model of the rates of wear, as well as to further extrapolate future wear, with a reasonable expectation of success of wear projection. Naithani explains the safety concerns of wear and defects on the rail industry ([0002])., therefore enumerating why prediction of rail wear and/or maintenance is important to incorporate into the teachings of Mesher.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 14 is similarly rejected to claim 4.
As Mesher as modified above teaches both the system of claim 1 and the method of claim 11, claim 15 is similarly rejected to claim 5.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
"A. Kapoor, F.J. Franklin, S.K. Wong, M. Ishida, Surface roughness and plastic flow in rail wheel contact, 2002, Wear, Volume 253, Pgs 257–264". NPL was referenced in determining the physics and physicality of how a rail head is deformed when undergoing “plastic flow”, as mentioned in claims 3 and 13.
Dick et al. (US 20200239049 A1) described using a system and method for railroad inspection. The inspection data is collected with mainly imaging devices, such as cameras and thermal imaging devices, where sensors such as LiDAR (or other time-of-flight sensors) are used to generate 3D maps of the transportation pathway and surroundings. The data is logged according to GPS locations and includes wear of rails, but focuses its methods of analyzing on ballast, drainage, and vegetation conditions.
Ashtekar et al. (US 20210406430 A1) teaches a system and method for predicting and comparing wear scenarios in a rail system, in addition to a financial modeling system. This modeling is used for modeling cracks, wear, and grinding and may be based on physical examination, survey, or other inspection.
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 Kara Richter whose telephone number is (571)272-2763. The examiner can normally be reached Monday - Thursday, 8A-5P EST, Fridays are variable.
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, Helal Algahaim can be reached at (571) 270-5227. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/K.M.R./Examiner, Art Unit 3645
/HELAL A ALGAHAIM/SPE , Art Unit 3645
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