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 .
Status of claims: claims 14-35 are pending and examined below.
Response to Arguments
Applicant's arguments filed 7/27/2026 have been fully considered but they are not persuasive.
Applicant’s remark – (page 8) Applicant will consider resolution of the non-statutory obviousness-type double patenting rejection upon the indication of allowable subject matter. Please see Remarks for further detail.
Examiner response – Examiner acknowledge and we will address this toward the end of the prosecution.
Applicant’s remark – (pages 8-10) Applicant argued claim 14 the lack of teaching by Pestun fails to disclose this feature of using image reconigtion model as supported in paragraph 0081-0082, Claim 23 also argued that not using such GPS information in the identification of El feature types. Pestun does not use GPS information to actively identify El feature types or activity taking place at such El features. For claim 27, The Office Action fails to support the rejection of these claim features. First, Pestun does not disclose "receiving from a supplemental information source, supplemental information indicative of human activity at an El site in a portion of terrain." Claims 34-35, Pestun does not disclose either of these features which is the same argument that combine use of GPS and El features. Please see remarks for further detail.
Examiner response – Examiner respectfully disagree. Applicant has amended the claims to center the use of image recognition model using GPS/position/location/coordinate with feature. Examiner does not view this as unique or novel as PESTUN et al (US 2018/0218214) teaches such combination of generic claim elements in 0039 and 0130, where in both of these paragraphs teaches such combination of using machine learning pattern recognition with relevant information such as their global positioning system (GPS) position, size (EL feature) as monitoring and condition analysis to perform time and cost-effective matter. Paragraph 0130 further detailing aerial monitoring with analytic information such as approximate infrastructure entities positions for towers detection with entity recognizer 124 such as size in the area (feature with GPS/position)). Please see the Office Action below that addresses other claim amendment in the claim such as human, type, development, EL feature…etc.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 14-16, 18, 20-25, 27-31 and 33-35 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 11,379,971. Although the claims at issue are not identical, they are not patentably distinct from each other because the invention defined by the claims of the instant application would have been obvious to one of ordinary skill in the art in view of the claims 1-30 of the U.S. Patent No. 11,379,971. Independent claims 14, 23, 27, 34 and 35 and dependent claims 20, 29 and 31 in the current application 18/581206 are anticipated by the independent claims 1, 9, 3, 5 and 23 in U.S. Patent No. 11,379,971. Dependent claims 15-16, 18, 21-25, 28, 30 and 33 follow likewise mapping to dependent claims 2-6 and 23 in U.S. Patent No. 11,379,971.
Claims 17, 19, 26 and 32 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-30 of U.S. Patent No. 11,379,971 in view of Pestun et al (US 2018/0218214).
U.S. Patent No. 11,379,971 recited all the subject matter above, but lack the all the detail of claim 17. Pestun et al discloses the detail of claim 17 in 0035 teaches monitoring of entities, and condition analysis of such entities (towers for power grids, power lines for power grids) using computer vision and predictive analytics.
U.S. Patent No. 11,379,971 and Pestun et al are both in the field of image analysis, especially the use of images for monitoring of energy/power grid/infrastructure such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify U.S. Patent No. 11,379,971 by Pestun et al regarding predictive analysis for potential and prevent damages to infrastructure as disclosed by Pestun et al in paragraph 0035.
U.S. Patent No. 11,379,971 recited all the subject matter above, but lack the all the detail of claims 19, 26 and 32. Pestun et al discloses the detail of claims 19, 26 and 32 in 0035 teaches using of aerial images from UVA, drones, helicopter, satellite…etc to monitor power grid, infrastructure entities.
U.S. Patent No. 11,379,971 and Pestun et al are both in the field of image analysis, especially the use of images for monitoring of energy/power grid/infrastructure such that the combine outcome is predictable.
Therefore it would have been obvious to one having ordinary skill before the effective filing date to modify U.S. Patent No. 11,379,971 by Pestun et al regarding the monitoring energy/power grid/infrastructure as part of condition analysis of such entities using computer vision and predictive analytics for potentially may damage or preventing of damage of energy/power grid/infrastructure as disclosed by Pestun et al in 0035.
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 14-22 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claim 14 was amended “applying image recognition model that combines the GPS information and the EL feature information” where the applicant directed to paragraph 0081-0082 of the specification of the instant invention for support. A review of the cited paragraph 0081-0082, the Examiner is unable to find support where there is a combine the GPS information and the EL feature information, there is no mention of GPS/position/location/coordinate data/information. Paragraph 0081-0082 focused on feature-based information. This is view as new matter. Please amend or direct to proper support to overcome the rejection. For compact prosecution the Examiner will still move forward with examination.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 14-35 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by PESTUN et al (US 2018/0218214).
Claim 14:
PESTUN et al (US 2018/0218214) anticipated the following subject matter:
A computer-implemented method to identify Energy Infrastructure (EI), the method to be carried out by at least one processor executing computer instructions, the method comprising:
receiving an image including at least one captured image of a portion of terrain (0001 teaches use of manned or unmanned aerial vehicles (UAVs), drones, unmanned land vehicles, even collaborative robots with imaging system, video camera, IR sensor for area of interest; 0046 detail terrain where assets are identified and inspected, where assets such as towers for power grids, power lines for power grids in paragraph 0035);
applying an El feature recognition model to the image to generate El feature information on at least one El feature in the portion of terrain (0039 teaches computer vision, machine learning, pattern recognition, and advanced analytics with GPS for provide for the monitoring and condition analysis for electric lines region (energy infrastructure));
receiving from a supplemental information source, GPS information (0039-0040 teaches data such as GPS as well as historical data analysis to monitor and condition analysis); and
identifying the at least one El feature by applying an image recognition model that combines the GPS information and the El feature information (paragraph 0039 and 0130 both detail teaching using machine learning patter recognition that use relevant information such as their global positioning system (GPS) position, size (EL feature) as monitoring and condition analysis to perform time and cost effective matter; paragraph 0130 further detail aerial monitoring with analytic information such as approximate infrastructure entities positions for towers detection with entity recognizer 124 such as size in the area (feature with GPS/position); 0039 teaches computer vision, machine learning, pattern recognition (identifying), and advanced analytics with GPS for provide for the monitoring and condition analysis for electric lines region (energy infrastructure)).
Claim 15:
The computer-implemented method of claim 14, further comprising identifying activity at the at least one El feature based on the El feature information and the GPS information (paragraph 0039-0040).
Claim 16:
The computer-implemented method of claim 14, further comprising classifying a level of the activity at the at least one El feature based on the El feature information and the GPS information (figure 29 and 0314 teaches level of activities such as flow of area regarding tall tree, vegetation, anthropogenic objects are considered).
Claim 17:
The computer-implemented method of claim 14, further comprising predicting El site status based on the El feature information and the GPS information (0035 teaches monitoring of entities, and condition analysis of such entities (towers for power grids, power lines for power grids) using computer vision and predictive analytics).
Claim 18:
The computer-implemented method of claim 14, further comprising identifying a composite indication of El site status of an El site associated with the at least one El feature based on the El feature information and the GPS information (0161-0163, especially 0162 teaches combing (composite) classification result, positive results, filter seasonal, binary prediction, distance or the features of infrastructures).
Claim 19:
The computer-implemented method of claim 18, wherein the composite indication of El site status includes at least one of: an indication that the El site has reached a particular stage of development; an indication that the El site has all necessary facilities to commence drilling; an indication that drilling has commenced at the El site; an indication that drilling has ceased at the El site; an indication that hydraulic fracturing has commenced at the El site; an indication that hydraulic fracturing has ceased at the El site; an indication that the El site exhibits an absence of activity; an indication that the El site exhibits a commencement or recommencement activity; an indication of a need for transport or infrastructure associated with a resource at the El site has been identified; an indication of a shortage or abundance of supplies, resource or equipment has been detected at the El site; an indication that the El site is non-operational and is not supplying energy to an electrical power grid; and an indication that the El site is operational and is supplying energy to an electrical power grid (0035 teaches using of aerial images from UVA, drones, helicopter, satellite…etc to monitor power grid, infrastructure entities).
Claim 20:
The computer-implemented method of claim 14, further comprising associating the GPS information with the portion of terrain, wherein the GPS information includes information on the at least one El feature (0039 condition or electrical line region regard to GPS).
Claim 21:
The computer-implemented method of claim 14, further comprising updating the El feature recognition model based on the GPS information (0039 updating condition or electrical line region regard to GPS; 0094 teaches updated with active image with image GPS; 0261).
Claim 22:
The computer-implemented method of claim 15, further comprising generating a report of the activity at the at least one El feature (0128 teaches aerial monitoring of towers, for each detected tower, the output report generated by the entity recognizer 124 may further include the tower ID, the type of tower and a consolidated position of the tower (e.g., GPS (longitude and latitude), and altitude).).
Claim 23:
PESTUN et al (US 2018/0218214) anticipated the following subject matter:
A computer-implemented method to identify Energy Infrastructure (EI), the method to be carried out by at least one processor executing computer instructions, the method comprising:
receiving El feature information on at least one El feature in a portion of terrain (0001 teaches use of manned or unmanned rial vehicles (UAVs), drones, unmanned land vehicles, even collaborative robots with imaging system, video camera, IR sensor for area of interest; 0046 detail terrain where assets are identified and inspected, where assets such as towers for power grids, power lines for power grids in paragraph 0035);
receiving from a supplemental information source, GPS information related to the portion of terrain (0039-0040 teaches data such as GPS as well as historical data (supplemental information) analysis to monitor and condition analysis); and
identifying at least one second El feature in the portion of terrain based on the GPS information (0039 teaches computer vision, machine learning, pattern recognition (identifying), and advanced analytics with GPS for provide for the monitoring and condition analysis for electric lines region (energy infrastructure));
identifying activity at the at least one second El feature based on the GPS information (figure 29 and 0314 teaches level of activities such as flow of area regarding tall tree, vegetation, anthropogenic objects are considered); and
generating a report of the activity at the at least one second El feature (0128 teaches aerial monitoring of towers, for each detected tower, the output report generated by the entity recognizer 124 may further include the tower ID, the type of tower and a consolidated position of the tower).
Claim 24:
The computer-implemented method of claim 23, further comprising classifying a level of the activity at the at least one second El feature based on the GPS information (0130 teaches classifying using position result, data from previous monitoring).
Claim 25:
The computer-implemented method of claim 23, further comprising identifying a composite indication of El site status of an El site associated with the at least one second El feature based on the GPS information (0161-0163, especially 0162 teaches combing (composite) classification result, positive results, filter seasonal, binary prediction, distance or the features of infrastructures).
Claim 26:
The computer-implemented method of claim 25, wherein the composite indication of El site status includes at least one of:an indication that the El site has reached a particular stage of development; an indication that the El site has all necessary facilities to commence drilling; an indication that drilling has commenced at the El site; an indication that drilling has ceased at the El site; an indication that hydraulic fracturing has commenced at the El site; an indication that hydraulic fracturing has ceased at the El site; an indication that the El site exhibits an absence of activity; an indication that the El site exhibits a commencement or recommencement activity; an indication of a need for transport or infrastructure associated with a resource at the El site has been identified; an indication of a shortage or abundance of supplies, resource or equipment has been detected at the El site; an indication that the El site is non-operational and is not supplying energy to an electrical power grid; andan indication that the EI site is operational and is supplying energy to an electrical power grid (0035 teaches using of aerial images from UVA, drones, helicopter, satellite…etc to monitor power grid, infrastructure entities).
Claim 27:
PESTUN et al (US 2018/0218214) anticipated the following subject matter:
A computer-implemented method to identify Energy Infrastructure (EI) site status, the method to be carried out by at least one processor executing computer instructions, the method comprising:
receiving from a supplemental information source, supplemental information indicative of human activity of an EI site in a portion of terrain (0039-0040 teaches data such as GPS as well as historical data (supplemental) analysis to monitor and condition analysis; 0041-0043 detail identifying of movement such as vehicles and crew (human) to carry out and track the work order; 0042 detail tracking information of vehicle and its operation and operation crew; 0043 detail complete operation with operation crew);
receiving an image including at least one captured image of the portion of terrain (0001 teaches use of manned or unmanned rial vehicles (UAVs), drones, unmanned land vehicles, even collaborative robots with imaging system, video camera, IR sensor for area of interest; 0046 detail terrain where assets are identified and inspected, where assets such as towers for power grids, power lines for power grids in paragraph 0035);
applying an EI feature recognition model to the image to generate EI feature information on an EI feature associated with the EI site at a location in the portion of terrain (0039 teaches computer vision, machine learning, pattern recognition (identifying), and advanced analytics with GPS for provide for the monitoring and condition analysis for electric lines region (energy infrastructure)); and
determining a composite indication of the EI site status indicative of development of the EI site based on at least the EI feature information and the supplemental information (0044 detail monitoring of scenario with sensors of the development’s condition, development such as pad placement, facility rendering, capital project, surveillance; 0161-0163, especially 0162 teaches combing (composite) classification result, positive results, filter seasonal, binary prediction, distance or the features of infrastructures).
Claim 28:
The computer-implemented method of claim 27, wherein the supplemental information includes GPS information associated with the portion of terrain (0039-0040 teaches data such as GPS as well as historical data (supplemental) analysis to monitor and condition analysis).
Claim 29:
The computer-implemented method of claim 28, wherein the GPS information includes information on the EI feature associated with the EI site (0039-0040 teaches data such as GPS as well as historical data (supplemental) analysis to monitor and condition analysis.).
Claim 30:
The computer-implemented method of claim 27, wherein the supplemental information includes regulatory information associated with the portion of terrain (0047-0049 teaches operation decision from determined task in workflow for problem detection, or predictive maintenance with towers and electric lines, for example, in the power systems with related regulation and compliance, readiness, and safety and privacy).
Claim 31:
The computer-implemented method of claim 27, wherein the supplemental information includes EI feature information associated with the portion of terrain (0039-0040 teaches data such as GPS as well as historical data (supplemental) analysis to monitor and condition analysis).
Claim 32:
The computer-implemented method of claim 27, wherein the composite indication of the EI site status includes at least one of:an indication that the EI site has reached a particular stage of development;an indication that the EI site has all necessary facilities to commence drilling;an indication that drilling has commenced at the EI site;an indication that drilling has ceased at the EI site;an indication that hydraulic fracturing has commenced at the EI site;an indication that hydraulic fracturing has ceased at the EI site;an indication that the EI site exhibits an absence of activity;an indication that the EI site exhibits a commencement or recommencement activity;an indication of a need for transport or infrastructure associated with a resource at the EI site has been identified;an indication of a shortage or abundance of supplies, resource or equipment has been detected at the EI site;an indication that the EI site is non-operational and is not supplying energy to an electrical power grid; andan indication that the EI site is operational and is supplying energy to an electrical power grid (0035 teaches using of aerial images from UVA, drones, helicopter, satellite…etc to monitor power grid, infrastructure entities).
Claim 33:
The computer-implemented method of claim 27, further comprising: generating a report of the composite indication of the EI site status (0128 teaches aerial monitoring of towers, for each detected tower, the output report generated by the entity recognizer 124 may further include the tower ID, the type of tower and a consolidated position of the tower (e.g., GPS (longitude and latitude), and altitude)).
Claim 34:
PESTUN et al (US 2018/0218214) anticipated the following subject matter:
A computer-implemented method of identifying Energy Infrastructure (EI) activity, the method to be carried out by at least one processor executing computer instructions, the method comprising:
receiving EI feature information on at least one EI feature in a portion of terrain (0001 teaches use of manned or unmanned rial vehicles (UAVs), drones, unmanned land vehicles, even collaborative robots with imaging system, video camera, IR sensor for area of interest; 0046 detail terrain where assets are identified and inspected, where assets such as towers for power grids, power lines for power grids in paragraph 0035);
receiving from a supplemental information source (0039-0040 teaches data such as GPS as well as historical data (supplemental information) analysis to monitor and condition analysis), regulatory information related to the portion of terrain (0047-0049 teaches operation decision from determined task in workflow for problem detection, or predictive maintenance with towers and electric lines, for example, in the power systems with related regulation and compliance, readiness, and safety and privacy);
identifying a type of at least one second EI feature in the portion of terrain based on the regulatory information (0047-0049 teaches operation decision from determined task in workflow for problem detection, or predictive maintenance with towers and electric lines, for example of types, in the power systems with related regulation and compliance, readiness, and safety and privacy);
identifying human activity at the at least one second EI feature based on at least one of aerial imaging or GPS information (0041-0043 detail identifying of movement such as vehicles and crew (human) to carry out and track the work order; 0042 detail tracking information of vehicle and its operation and operation crew; 0043 detail complete operation with operation crew; 0128 teaches aerial monitoring of towers, for each detected tower, the output report generated by the entity recognizer 124 may further include the tower ID, the type of tower and a consolidated position of the tower); and
generating a report of the activity at the at least one second EI feature (0128 teaches aerial monitoring of towers, for each detected tower, the output report generated by the entity recognizer 124 may further include the tower ID, the type of tower and a consolidated position of the tower (e.g., GPS (longitude and latitude), and altitude).
Claim 35:
PESTUN et al (US 2018/0218214) anticipated the following subject matter:
A computer-implemented method to identify Energy Infrastructure (EI) activity, the method to be carried out by at least one processor executing computer instructions, the method comprising:
receiving first EI feature information on at least one EI feature in a portion of terrain (0039-0040 teaches data such as GPS as well as historical data (supplemental) analysis to monitor and condition analysis);
receiving an image including at least one captured image of the portion of terrain (0001 teaches use of manned or unmanned rial vehicles (UAVs), drones, unmanned land vehicles, even collaborative robots with imaging system, video camera, IR sensor for area of interest; 0046 detail terrain where assets are identified and inspected, where assets such as towers for power grids, power lines for power grids in paragraph 0035);
applying an EI feature recognition model to the image to generate second EI feature information on at least one second EI feature in the portion of terrain (0039 teaches computer vision, machine learning, pattern recognition (identifying), and advanced analytics with GPS for provide for the monitoring and condition analysis for electric lines region (energy infrastructure));
identifying human activity at the at least one EI feature based on the at least one second EI feature (0041-0043 detail identifying of movement such as vehicles and crew (human) to carry out and track the work order; 0042 detail tracking information of vehicle and its operation and operation crew (human); 0043 detail complete operation with operation crew; 0047-0049 teaches operation decision from determined task in workflow for problem detection, or predictive maintenance with towers and electric lines, for example of types (second feature), in the power systems with related regulation and compliance, readiness, and safety and privacy; figure 29 and 0314 teaches level of activities such as flow of area regarding tall tree, vegetation, anthropogenic objects are considered also view as second type feature); and
generating a report of the activity at the at least one second EI feature (0128 teaches aerial monitoring of towers, for each detected tower, the output report generated by the entity recognizer 124 may further include the tower ID, the type of tower and a consolidated position of the tower).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Terrazas et al (US 2013/0226667) teaches METHODS AND APPARATUS TO ANALYZE MARKETS BASED ON AERIAL IMAGES – abstract: analyze market channels based on aerial images are disclosed. An example method includes determining, using a processor, whether a first element in an aerial image of a geographic area represents a man-made object.
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.
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/TSUNG YIN TSAI/Primary Examiner, Art Unit 2656
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