DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. 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 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
2. The information disclosure statements (IDS) submitted on the following dates are in compliance with the provisions of 37 CFR 1.97 and are being considered by the Examiner: 01/16/2025.
Claim Objections
3. Claim 12 objected to because of the following informalities:
Claim 12, line 5, "a virtual representation of content that" should be changed to "a virtual representation of content for the product that".
Appropriate correction is required.
Claim Rejections - 35 USC § 102
4. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
5. Claims 1-6 and 12-17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Parfenov et al., (“Parfenov”) [US-2024/0037875-A1]
Regarding claim 1, Parfenov discloses a method (Parfenov- ¶0004, at least discloses systems, methods, and apparatus, including computer program products, for generating AR content, and for using that AR content to improve the human-machine interaction) comprising:
generating, at a server, a product digital twin that is a virtual representation of a product (Parfenov- ¶0006-0007, at least disclose locating, based on a digital twin for the device [product digital twin], a feature of the device on which the action is to be performed; locating the feature on the actual graphic […] The digital twin may include data about the device in three dimensions […] identifying a component of a device [product]; generating, on-demand, a digital twin of part of the device proximate to the component, where the digital twin is generated based, at least in part, on sensor readings from the component and information about the device available from one or more sources, and where the digital twin includes information that models a structure and function of the part of the device and the component [generating a product digital twin that is a virtual representation of a product]; Fig. 1 and ¶0047, at least disclose The computer graphics are generated by a computing device, such as a server or tablet computing device, based on information about the device displayed, in this case, the motorcycle […] The actions to be taken may be based on readings from sensors placed on the actual device and/or based on service history data feed alerts from a server for identified objects; ¶0091, at least discloses a remote computing device, such as a server, may include most of the intelligence and processing resources in the AR system, enabling creation of an on-demand DT for identified device(s)), wherein the product digital twin is dynamically updatable (Parfenov- ¶0022, at least discloses The digital twin may be based on sensor readings obtained from sensors on the device and may be based on information about the structure and function of the device obtained from one or more sources. The example method may include: updating the digital twin in real-time based at least in part on changes in the sensor readings to produce an updated digital twin; ¶0072, at least discloses In the case of real-time video, the DT may be generated or updated in real-time, and the resulting computer graphics superimposed on frames in real-time. Updating the DT may include changing the declarative model and/or the compiled model, and/or other data used to define the DT);
generating, at the server, a content digital twin that is a virtual representation of content for the product that is dynamically updatable (Parfenov- ¶0007, at least discloses generating, on-demand, a digital twin of part of the device proximate to the component, where the digital twin is generated based, at least in part, on sensor readings from the component and information about the device available from one or more sources, and where the digital twin includes information that models a structure and function of the part [content digital twin] of the device and the component; generating augmented reality content for the part and the component based on at least one of the information in the digital twin or an actual graphic of the part and the component; Fig. 1 and ¶0047, at least disclose The computer graphics are generated by a computing device, such as a server or tablet computing device, based on information about the device displayed, in this case, the motorcycle […] The actions to be taken may be based on readings from sensors placed on the actual device and/or based on service history data feed alerts from a server for identified objects; ¶0091, at least discloses a remote computing device, such as a server, may include most of the intelligence and processing resources in the AR system, enabling creation of an on-demand DT for identified device(s));
receiving, at the server (As discussed above), data from at least one source (Parfenov- ¶0007-0008, at least disclose generating, on-demand, a digital twin of part of the device proximate to the component, where the digital twin is generated based, at least in part, on sensor readings from the component and information about the device available from one or more sources, and where the digital twin includes information that models a structure and function of the part of the device and the component […] obtaining the information may include querying the device, querying one or more data sources containing the information, or querying both the device and the one or more data sources; ¶0012, at least discloses obtaining data from digital twins of multiple instances of a same type of device, where each digital twin is based, at least in part, on sensor readings from a corresponding instance of the device and information about the device available from one or more sources, and where each digital twin includes information that models a structure and function of each corresponding instance of the device);
generating updates, at the server (As discussed above), for at least one selected from a group consisting of: the product digital twin, and the content digital twin based on the received data (Parfenov- ¶0013-0014, at least disclose determining, based at least in part on the digital twin, that there has been a change in at least one component of the instance of the device; updating a bill of materials for the device automatically using information from the digital twin to produce an updated bill of materials […] receiving confirmation that the at least one component has been changed in the instance of the device; and updating the digital twin to reflect a change in the at least one component […] updating the digital twin in real-time based at least in part on changes in the sensor readings to produce an updated digital twin; updating the augmented reality content in real-time based on the updated digital twin to produce updated augmented reality content; ¶0022, at least discloses The digital twin may be based on sensor readings obtained from sensors on the device and may be based on information about the structure and function of the device obtained from one or more sources. The example method may include: updating the digital twin in real-time based at least in part on changes in the sensor readings to produce an updated digital twin); and
transmitting, at the server (As discussed above), the generated updates to a plurality of different platforms (Parfenov- ¶0017, at least discloses updating a digital twin of a device that is a subject of the displayed augmented reality content based on the input, where the digital twin includes information that models a structure and function of the device; and updating displayed augmented reality content based on one or more updates to the digital twin […] process may include publishing updated augmented reality content to social media; ¶0113, at least discloses The app may include tools to receive, through a user interface, user-provided comments and mark-ups to AR content, to incorporate those comments and mark-ups into the AR content, and to allow the user to publish the result to social media or elsewhere online (e.g., on the Internet or an intranet)).
Regarding claim 2, Parfenov discloses the method of claim 1, and further discloses wherein the receiving the data comprises:
receiving, from at least one data source, the data that includes at least one selected from a group consisting of: user interactions, behavioral analytics, contextual information, and external data feeds, market trend information, social media data, product data from a product information management (PIM) system, and content data from a content management system (CMS) (Parfenov- ¶0008, at least discloses Obtaining the information may include querying the device, querying one or more data sources containing the information, or querying both the device and the one or more data sources. Operations for obtaining the information may include recognizing distinctive attributes of the device based on an image of the device and based on stored information about the device; ¶0050, at least discloses The information and parameters may originate from, and/or be managed by, systems such as, but not limited to, PLM (product lifecycle management), CAD (computer-aided design), SLM (service level management), ALM (application lifecycle management), CPM (connected product management), ERP (enterprise resource planning), CRM (customer relationship management), and/or EAM (enterprise asset management); ¶0055, at least discloses Example processes performed by the example AR system identify an instance of a device, generate AR content for the device using the DT for that device, and use that AR content in various ways to facilitate human-machine interaction with the device, including real-life, real-time interaction between the user and the device; ).
Regarding claim 3, Parfenov discloses the method of claim 1, and further discloses wherein the generating updates comprises:
updating, at the server, the digital product twin (see Claim 1 rejection for detailed analysis) based on data received from at least one selected from a group consisting of: an actual product that the digital product twin is a virtual representation of (Parfenov- ¶0009, at least discloses process includes: recognizing an instance of a device […] and generating augmented reality content based on the digital twin and an actual graphic of the instance of the device, where the augmented reality content is generated based also on the position of the user relative to the instance of the device; ¶0014, at least discloses generating augmented reality content based on the digital twin and an actual graphic of the device […] Generating the augmented reality content may include: identifying content of the actual graphic based at least in part on annotations relating to the actual graphic; querying the digital twin for the content; and superimposing computer graphics generated at least in part based on the digital twin over content in the actual graphic, where the computer graphics are superimposed based on locations of the content in the actual graphic), and product simulation data (Parfenov- ¶0011-0012, at least disclose obtaining data representing an entity that is configurable […] and enabling performance a simulation on the configured entity based on the digital twin. Information from the digital twin may be exported to a computer-based simulation system where the simulation is performed, results of the simulation may be received from the computer-based simulation system, and the results may be incorporated into the digital twin […] performing a simulation of a version of the device using the data, where the simulation provides an expected operation of the version of the device, and where the expected operation is predicted based, at least in part, on the data; and outputting results of the simulation for display on a computing device).
Regarding claim 4, Parfenov discloses the method of claim 1, and further discloses wherein the generating updates comprises:
generating updates, at the server, for the content digital twin based on the received data when the received data meets a predetermined metric or threshold (Parfenov- ¶0007, at least discloses The part of the device may be less than an entirety of the device, and the part of the device may be within a threshold distance of the component; ¶0010, at least discloses recognizing an instance of a first device; recognizing an instance of a second device; determining that the first device and the second device are within a threshold proximity of each other […] following interaction of the first device and the second device, updating the first digital twin of the first device with information from the second digital twin of the second device, or updating the second digital twin of the second device with information from the first digital twin of the first device; generating augmented reality content based on at least one of the first digital twin or the second digital twin).
Regarding claim 5, Parfenov discloses the method of claim 1, and further discloses wherein the generating updates further comprises:
generating updates, at the server, for the content digital twin (see Claim 1 rejection for detailed analysis) based on received data from at least one selected from a group consisting of: real-time data, user interaction data, and contextual information (Parfenov- ¶0010, at least discloses following interaction of the first device and the second device, updating the first digital twin of the first device with information from the second digital twin of the second device, or updating the second digital twin of the second device with information from the first digital twin of the first device; ¶0014, at least discloses updating the digital twin in real-time based at least in part on changes in the sensor readings to produce an updated digital twin; updating the augmented reality content in real-time based on the updated digital twin to produce updated augmented reality content; ¶0055, at least discloses processes performed by the example AR system identify an instance of a device, generate AR content for the device using the DT for that device, and use that AR content in various ways to facilitate human-machine interaction with the device, including real-life, real-time interaction between the user and the device; ¶0104, at least discloses Through interaction with the AR system, a user may add components to the vehicle cabin, adjust their positions, change their kinematic set-up, change their geometry, and so forth; ¶0105, at least discloses The response to these stimuli may be recorded in the DT, and reported to the user in real-time on their mobile device or other appropriate computing device).
Regarding claim 6, Parfenov discloses the method of claim 1, and further discloses wherein the plurality of different platforms (see Claim 1 rejection for detailed analysis) include at least one selected from a group consisting of: websites, social media sites, mobile applications, and email messages (Parfenov- ¶0017, at least discloses process may include publishing updated augmented reality content to social media; ¶0050, at least discloses The information and parameters may originate from, and/or be managed by, systems such as, but not limited to, PLM (product lifecycle management), CAD (computer-aided design), SLM (service level management), ALM (application lifecycle management), CPM (connected product management), ERP (enterprise resource planning), CRM (customer relationship management), and/or EAM (enterprise asset management). The information and parameters can cover a range of characteristics stored, e.g., in a bill of material (BOM) associated with the device (e.g., EBOM—engineering BOM, MBOM—manufacturing BOM, or SBOM—service BOM), the device's service data and manuals, the device's behavior under various conditions, the device's relationship to other device(s) and artifacts connected to the device, and software that manages, monitors, and/or calculates the device's conditions and operations in different operating environments; ¶0113, at least discloses The app may include tools to receive, through a user interface, user-provided comments and mark-ups to AR content, to incorporate those comments and mark-ups into the AR content, and to allow the user to publish the result to social media or elsewhere online (e.g., on the Internet or an intranet)).
The system of claims 12-17 are similar in scope to the functions performed by the method of claims 1-6 and therefore claims 12-17 are rejected under the same rationale.
Regarding claim 12, Parfenov discloses a system (Parfenov- ¶0004, at least discloses systems, methods, and apparatus, including computer program products, for generating AR content, and for using that AR content to improve the human-machine interaction; Fig. 4 and ¶0057, at least disclose AR system 400 includes a front end 401 and a back end 402. Front end 401 includes one or more mobile computing devices (or simply, mobile devices)) comprising:
a server (Parfenov- Fig. 4 and ¶0059-0060, at least disclose Back end 402 includes one or more computing devices 412, examples of which include servers, desktop computers, and mobile devices […] Front end 401 and back end 402 may communicate with each other, and with other systems, such as those described herein, over one or more computer networks, which may include wireless and/or wired networks [Wingdings font/0xE0] suggests source (see Claim 1 rejection for detailed analysis) communicatively coupled to the server; ¶0091, at least discloses a remote computing device, such as a server, may include most of the intelligence and processing resources in the AR system, enabling creation of an on-demand DT for identified device(s); ¶0118, at least discloses Elements of a computer (including a server) include one or more processors for executing instructions and one or more storage area devices for storing instructions and data; ) configured to perform the method of claim 1.
Claim Rejections - 35 USC § 103
6. 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.
7. Claims 7-8 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Parfenov in view of Belkin et al., (“Belkin”) [US-2025/0021919-A1]
Regarding claim 7, Parfenov discloses the method of claim 1, and further discloses wherein the generating updates comprises:
generating updates for the content digital twin based on at least a portion of the received data (Parfenov- ¶0072, at least discloses The computer graphics portion of the AR content may track movement frame-by-frame of the actual device during playback of the video […] In the case of real-time video, the DT may be generated or updated in real-time, and the resulting computer graphics superimposed on frames in real-time; ¶0091, at least discloses A DT may be generated on-the-fly for a relevant portion of the airplane and for the smart torque wrench. Following use of the smart torque wrench on a bolt of the airplane, the DTs for both the airplane and torque wrench may be updated to reflect the use).
Parfenov does not explicitly disclose, but Belkin discloses
an artificial intelligence system or machine learning system that is part of or communicatively coupled to the server (Belkin- ¶0013, at least discloses Techniques are disclosed to create, maintain, and use Artificial Intelligence (AI)-based digital twins of company employees. Artificial Intelligence in this context includes, without limitation, the use of large language models (LLMs) and other machine learning techniques to mimic human interaction behavior. In various embodiments, such interactions may be mimicked in the enterprise context in a manner that is consistent with company policies and codes of conduct; ¶0029, at least discloses Data reflecting what the user knows about the subject matter of the query may be extracted from vector database 132 and generative or other artificial intelligence technologies may be used to determine a best answer to the query and to construct a response (or draft response) to the query. In some embodiments, generative AI techniques are used to generate the response based on knowledge of the user, as reflected in data retrieved from vector database 132, and expressed using language, tone, and other content (e.g., graphics) that reflect a style, voice, etc. that the system 100 has learned and/or configured to associate with the user for whom the digital twin response is being generated; Fig. 2 and ¶0033, at least disclose all or some of the modules and components comprising system 200 of FIG. 2 may be provided as modules running on one or more servers comprising digital twin service 130 of FIG. 1; Fig. 3 and ¶0085, at least disclose the process 300 of FIG. 3 is performed by one or more servers configured to provide a digital twin service, such as digital twin service 130 of FIG. 1 […] an LLM or other techniques may be used to determine for each item of content an intent and/or subject matter content of the item; Fig. 4 and ¶0088, at least disclose the process 400 of FIG. 4 is performed by one or more servers configured to provide a digital twin service, such as digital twin service 130 of FIG. 1 […] an LLM or other AI may be used to select the best response).
It would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to have modified Parfenov to incorporate the teachings of Belkin, and apply the artificial Intelligence and other machine learning techniques into Parfenov’s teachings for generating updates, at an artificial intelligence system or machine learning system that is part of or communicatively coupled to the server, for the content digital twin based on at least a portion of the received data.
Doing so would provide an AI-based digital twin capable of generating and providing responses that reflect the knowledge and/or expertise of the given employee (or another person) the AI-based digital twin is configured to mimic.
Regarding claim 8, Parfenov discloses the method of claim 1, and does not explicitly disclose, but Belkin discloses wherein the generating updates (see Claim 1 rejection for detailed analysis) comprises:
personalizing, at the server, the content digital twin for a user or a group of users based on the received data (Belkin- ¶0071, at least discloses 3. Perform data search to ascertain specific data that is owned by the company versus data of generic nature and/or private or sensitive data that users may have created to train the digital twin agents. 4. Permit the user to export non-company data for personal use as a personal digital twin).
It would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to have modified Parfenov to incorporate the teachings of Belkin, and apply exporting non-company data for personal use as a personal digital twin into Parfenov’s teachings for personalizing the content digital twin for a user or a group of users based on the received data.
Doing so would provide an autonomous system that is capable of taking on many tasks currently expected to be performed by humans.
The system of claims 18-19 are similar in scope to the functions performed by the method of claims 7-8 and therefore claims 18-19 are rejected under the same rationale.
8. Claims 9-10 and 20-21 are rejected under 35 U.S.C. 103 as being unpatentable over Parfenov in view of Rethage et al., (“Rethage”) [US-12,374,065-B1]
Regarding claim 9, Parfenov discloses the method of claim 1, and does not explicitly disclose, but Rethage discloses the method further comprising:
mapping, at the server, changes to at least one selected from the group consisting of: the product digital twin, and the content digital twin based on the generated updates (Rethage- col 4, lines 29-32, at least discloses The 2D image is uploaded to a server configured to automatically detect the structural modification (e.g., the added deck) and to update a pre-existing 3D model that represents the house before the remodel (e.g., before the deck was added) to reflect the new deck. The server analyzes the 2D image using computer-vision and machine-learning techniques to generate a prediction of a 3D model of the remodeled house [...] The server queries the database of pre-existing 3D models using the descriptor to identify and retrieve the pre-existing 3D model of the house before the deck was added. The server compares the predicted 3D model to the pre-existing 3D model to detect the addition of the deck, and then updates the pre-existing 3D model of the house to reflect the new deck [Wingdings font/0xE0] suggests mapping process at the server; col 6, line 64 to col 7, line 8, at least discloses Performing the region-by-region mapping before the pixel-by-pixel mapping has the advantage of ensuring a “global match” for regional features of the building objects, and removing false negatives that would result from a pixel-to-pixel only approach to building façade matching and registration. For example, if an owner of a building has performed some remodeling to the physical building, any new photographs of the new building are mapped to the building model, due to the regional similarities between the façade residing in the image and the façade(s) associated with the 3D building object, with the remodeled region(s) highlighted as a changed region; col 16, lines 33-41, at least discloses Each region of the correlated stored image is matched against each region in the collected image […] Matched regions are annotated as being mapped to each other and saved and reflected database 206; col 23, lines 45-58, at least discloses the model generation system 700 includes a highlight module 724. The highlight module 724 is for detecting changes in facades of building models, including specific regions or facades as a whole. In other embodiments, the highlight module 724 also coordinates with the render module 708 to highlight the region of the facade that has changed. As new facades for a building model are uploaded from the image process system 702, the highlight module 724 can analyze the new mapped photos to highlight the regions in those photos that are different from the previous versions of stored facades. In some embodiments, the region match module 220 of FIG. 2 provides the information about which regions of the facade have been changed); and
determining, at the server, similarities or differences between the mapped changes and one or more metrics (Rethage- col 7, lines 50-62, at least discloses A server may perform any comparison between two descriptors to determine a similarity between the two descriptors […] If the distance between two descriptors, which are each represented by a vector, is within a threshold, then server 120 may determine that the two 3D models are similar. [Wingdings font/0xE0] suggests determining similarities or differences at the server; col 27, lines 57-63, at least disclose At block 980, image processing system 102 may be configured to map the identified target pre-existing 3D model to the inferred 3D model. The mapping may be used to identify a 3D shape representing the structural modification. The mapping may include calculating overlapping metrics between the inferred 3D model and the target pre-existing 3D model. Non-limiting examples of overlapping metrics may include a Dice similarity coefficient, distances errors, a Hausdorff distance, and other suitable metrics).
It would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to have modified Parfenov to incorporate the teachings of Rethage, and apply the region-by-region mapping, determining a similarity and metrics into Parfenov’s teachings for mapping, at the server, changes to at least one selected from the group consisting of: the product digital twin, and the content digital twin based on the generated updates; and determining, at the server, similarities or differences between the mapped changes and one or more metrics.
Doing so would retrieve a target 3D model and yields accurate results.
Regarding claim 10, Parfenov in view of Rethage, discloses the method of claim 9, and discloses the method further comprising:
generating, at the server or an artificial intelligence system communicatively coupled to the server, changes to at least one selected from the group consisting of: the product digital twin, and the content digital twin (see Claim 9 rejection for detailed analysis) based on the determined differences between the mapped changes and the one or more metrics (Rethage- col 5, lines 36-63, at least disclose After the pre-existing 3D model of the physical structure without the structural modification is identified using the descriptors, that pre-existing 3D model may be compared with the inferred 3D model of the physical structure with the structural modification to determine the differences. The determined differences represent the structural modification that was added, modified, or removed from the physical structure. In some embodiments, comparing the pre-existing 3D model with the inferred 3D model to identify the differences may include calculating overlapping metrics between the two 3D models. Non-limiting examples of overlapping metrics may include a Dice similarity coefficient, distances errors, a Hausdorff distance, and other suitable metrics […] The point-to-point correspondence between the two 3D models may indicate regions of the pre-existing 3D model that are different from the inferred 3D model. The identified differences between the pre-existing 3D model and the inferred 3D model may be mapped and used to update the pre-existing 3D model; col 27, lines 57-63, at least disclose At block 980, image processing system 102 may be configured to map the identified target pre-existing 3D model to the inferred 3D model. The mapping may be used to identify a 3D shape representing the structural modification. The mapping may include calculating overlapping metrics between the inferred 3D model and the target pre-existing 3D model. Non-limiting examples of overlapping metrics may include a Dice similarity coefficient, distances errors, a Hausdorff distance, and other suitable metrics).
It would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to have modified Parfenov to incorporate the teachings of Rethage, and apply the determining a similarity and metrics into Parfenov’s teachings for generating, at the server or an artificial intelligence system communicatively coupled to the server, changes to at least one selected from the group consisting of: the product digital twin, and the content digital twin based on the determined differences between the mapped changes and the one or more metrics.
The same motivation that was utilized in the rejection of claim 9 applies equally to this claim.
The system of claims 20-21 are similar in scope to the functions performed by the method of claims 9-10 and therefore claims 20-21 are rejected under the same rationale.
9. Claims 11 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Parfenov in view of Roper et al., (“Roper”) [WO-2024/253782-A1]
Regarding claim 11, Parfenov discloses the method of claim 1, and does not explicitly disclose, but Roper discloses the method further comprising:
formatting the generated updates for the plurality of different platforms (Roper- page 48, section Multimodal User Interfaces, 3rd paragraph, at least discloses Dashboard-style interface 594 offers a customizable overview of data visualizations, performance metrics, and system status indicators. It enables monitoring of relevant information, sectional review of documents, and decision-making based on dynamic data updates and external feedback. Such an interface may be accessible via web browsers and standalone applications on various devices).
It would have been obvious to one of ordinary in the art before the effective filing date of the claimed invention to have modified Parfenov to incorporate the teachings of Roper, and apply a customizable overview of data visualizations into Parfenov’s teachings for formatting the generated updates for the plurality of different platforms.
Doing so would provide a digital model platform that enables the streamlined creation and management of digital twins and physical twins by leveraging external feedback and artificial intelligence (Al).
The system of claim 22 is similar in scope to the functions performed by the method of claim 11 and therefore claim 22 is rejected under the same rationale.
Conclusion
10. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. They are as recited in the attached PTO-892 form.
11. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL LE whose telephone number is (571)272-5330. The examiner can normally be reached 9am-5pm.
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, Kent Chang can be reached at (571) 272-7667. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL LE/Primary Examiner, Art Unit 2614
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