DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA and is in response to communications filed on 2/05/2026 in which claims 21-40 are presented for examination.
Priority
Acknowledgment is made of Provisional Application No. 62/564,247, filed on 9/27/2017.
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 USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The 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/process/file/efs/guidance/eTD-info-I.jsp.
Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11768826 in view of Shaashua. The only differences are that there is explicitly a graph connecting the entities and a relationship type between the entities, but this is an obvious difference in view of Shaashua because Shaashua teaches types of relationships between entities in a graph in at least [0163].
Pending Application:
Claim 21. One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
generating a graph data structure including a plurality of nodes that represent a plurality of smart entities, the plurality of smart entities comprising (i) a plurality of object entities that represent a plurality of physical devices and (ii) a plurality of data entities that represent data generated as a result of operating the plurality of physical devices, the plurality of smart entities being interconnected by a plurality of relational objects that indicate relationships between the plurality of object entities and the plurality of data entities;
receiving, from a first physical device of the plurality of physical devices, data that resulted from operation of a second physical device of the plurality of physical devices;
identifying a first object entity of the plurality of object entities that represents the first physical device of the plurality of physical devices;
determining a second object entity of the plurality of object entities that represents the second physical device of the plurality of physical devices using a first relational object of the plurality of relational objects that connects the first object entity and the second object entity;
identifying a data entity of the plurality of data entities that corresponds to the data that resulted from the operation of the second physical device, the data entity identified using a second relational object of the plurality of relational objects that connects the data entity and the second object entity; and
modifying the data entity to include the data that resulted from the operation of the second physical device and received from the first physical device.
Patent No. 11768826
Claim 1. One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
generating a database of interconnected smart entities, the smart entities comprising object entities representing each of the plurality of physical devices and data entities representing data generated by the plurality of physical devices, the smart entities being interconnected by relational objects indicating relationships between the object entities and the data entities, wherein at least one relational object of the relational objects indicates a type of relationship;
receiving data from a first device of the plurality of physical devices and identifying a first object entity of the object entities representing the first device;
determining a second device of the plurality of physical devices using a firs relational object of the relational objects connecting the first object entity representing the first device and a second object entity of the object entities representing the second device;
identifying a data entity of the data entities storing data for the second device using a second relational object of the relational objects connecting the data entity and the second object entity representing the second device; and
modifying the data entity with the data received from the first device.
Claim 21 is rejected on the ground of nonstatutory double patenting as being unpatentable over claim 1 of U.S. Patent No. 11314788 in view of Shaashua. Although there’s a slight difference in the claim language with physical devices and objects associated with buildings, this is an obvious difference in view of Shaashua because Shaashua teaches building sensors in [0071].
Pending Application:
Claim 21. One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
generating a graph data structure including a plurality of nodes that represent a plurality of smart entities, the plurality of smart entities comprising (i) a plurality of object entities that represent a plurality of physical devices and (ii) a plurality of data entities that represent data generated as a result of operating the plurality of physical devices, the plurality of smart entities being interconnected by a plurality of relational objects that indicate relationships between the plurality of object entities and the plurality of data entities;
receiving, from a first physical device of the plurality of physical devices, data that resulted from operation of a second physical device of the plurality of physical devices;
identifying a first object entity of the plurality of object entities that represents the first physical device of the plurality of physical devices;
determining a second object entity of the plurality of object entities that represents the second physical device of the plurality of physical devices using a first relational object of the plurality of relational objects that connects the first object entity and the second object entity;
identifying a data entity of the plurality of data entities that corresponds to the data that resulted from the operation of the second physical device, the data entity identified using a second relational object of the plurality of relational objects that connects the data entity and the second object entity; and
modifying the data entity to include the data that resulted from the operation of the second physical device and received from the first physical device.
Patent No. 11314788
Claim 1. One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors, cause the one or more processors to perform operations comprising:
generating a database of interconnected smart entities, the smart entities comprising object entities representing each of a plurality of objects associated with one or more buildings and the plurality of objects each representing a space, person, building subsystem, and/or device, and data entities representing data generated by the plurality of objects,
the smart entities being interconnected by relational objects indicating relationships between the object entities and the data entities;
receiving data from a first object of the plurality of objects;
determining a second object of the plurality of objects from a relational object of the relational objects for the first object based on the received data;
identifying a data entity storing data for the second object by identifying a particular relational object of the relational objects between the data entity and an object entity of the object entities representing the second object; and
modifying the data entity of the data entities connected to the object entity of the object entities representing the second object with the data received from the first object.
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 21-25, 29-33, and 37-40 are rejected under 35 U.S.C. 103 as being unpatentable over Shaashua et al. US 20160342906 A1 (hereinafter referred to as “Shaashua”) in view of Devi et al. US 20170118240 A1 (hereinafter referred to as “Devi”) and further in view of Gomadam et al. US 20160314202 A1.
As per claim 21, Devi teaches:
One or more non-transitory computer readable media containing program instructions that, when executed by one or more processors (Shaashua, [0103]),
cause the one or more processors to perform operations comprising:
(ii) a plurality of data entities that represent data generated as a result of operating the plurality of physical devices (Shaashua, [0038] – Context relevant entities include people, places, groups, physical objects, brands, things, or any combination thereof. [0053] – The data correlation module 306 can receive data (e.g., real-time from continuous or discrete data stream, non-real-time data, device sensor data, user-device interaction dataset, user reporting dataset, or any combination thereof including metadata thereof) from the IoT devices 324 such as status updates. [0062] – Data generated through these semantically labeled IoT devices 324 may also be semantically labeled. [0082] and [0087] – When a user turns off his office lights and leaves the work, then his home temperature may be set automatically for a desired temperature, wherein temperature is the data associated with and generated from the thermostat. [0158] – Context indicators associated with target entities and/or raw data in an entity graph),
the plurality of smart entities being interconnected by a plurality of relational objects that indicate relationships between the plurality of object entities and the plurality of data entities (Shaashua, [0158] – Context indicators associated with target entities and/or raw data in an entity graph. [0163] and Fig. 12A – Inference and prediction is done over an entity graph, where users, devices and places are entity nodes, and edges model different types of relationships between entities);
receiving, from a first physical device of the plurality of physical devices, data that results from operation of a second physical device of the plurality of devices (Shaashua, [0050] – Physically connected devices…. May employ at least one of the following methods to identify the IoT devices 324: (a) device based identification, where unique IoT device identifier may be created based on the device's data elements; (b) protocol based identification, where unique device identifier may be created based on a device communication element or elements; (c) device and protocol based identification, where a combination of device data and communication protocol data may define the device identification; (d) device behavior based identification, where the device is identified based on its predefined or observed behavior, or any combination thereof);
identifying a first object entity of the plurality of object entities that represents a first physical device of the plurality of physical devices (Shaashua, [0049] – The device identification module 312 is configured to generate a unique ID for each IoT device detected by the integration platform);
determining a second object entity of the plurality of object entities that represent the second physical device of the plurality of physical devices using a first relational object of the plurality of relational objects that connects the first object entity and a second object entity (Shashua, [0163] – Edges are interpreted as relational objects because they indicate relationships between object entities and data entities. [0169] – An example for how a relationship is used is that there’s context between an object and data, wherein an object entity (person A or B) connects to a data entity (“has left” or “has arrived”).
Specification, [0111] gives an example of sensors and other data as entities which may define links among themselves);
…
modifying the data entity to include the data that resulted from the operation of the second physical device and received from the first physical device (Shaashua [0087] – Recognition that every morning a user starts the coffee machine, turns on the music, leaves a house, and turns off all lights and thermostat. The IoT device can command the thermostat and the lights to be triggered by the context event of the user leaving the house. [0158] – The evolving context indicators can be updated in real-time and/or in response to new data from the activity data streams. [0167] – The profile attribute can be an enumerated value, such as “male” or “female”, “on” or “off”, or “day” or “night”. A profile attribute can also be a numeric value, such as a numeric value representing temperature, heartbeat, time, location coordinate, IP address, physical address, or any combination thereof, wherein a switch of a physical device can modify the data for “on” and “off” as an example.
Specification, [0013] gives an example for dynamic attributes and storing values).
Shaashua doesn’t explicitly teach generating the graph data structure, however, Devi teaches:
generating a graph data structure including a plurality of nodes that represent a plurality of smart entities (Devi, [0007] – Generating an entity graph describing the entity relationships, wherein nodes of the entity graph represent one or more of the plurality of entities. [0034] – Devices such as smartphones are interpreted as smart entities),
the plurality of smart entities comprising (i) a plurality of object entities that represent a plurality of physical devices (Devi, [0027] – An entity is a physical or logical subject of analysis, and can be a user, a device, or a group of users and/or devices. [0034] – Smartphones. [0121] and Fig. 7 entity types such as devices are within the graph) and
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shaashua invention in view of Devi in order to include a generation of an entity graph; this is advantageous because it allows the system to create an initial entity graph which allows entities to be established with timeframes of their relationships (Devi, paragraph [0007]).
Shaashua as modified doesn’t explicitly teach a relational object that identifies a data entity corresponding to data that results from an operation of a physical device, however, Gomada teaches:
identifying a data entity of the plurality of data entities that corresponds to the data that resulted from the operation of the second physical device, the data entity identified using a second relational object of the plurality of relational objects that connects the data entity and the second object entity (Gomadam, [0030] – Store the data objects from the data sources within one or more databases of the diverse data system 100 in a manner that captures, stores, and manages relational linkages between different data objects in a centralized location. [0050] – Relationship edge 504 may indicate that this sensor core model has sensor readings of type “sensor readings,” as is indicated at node 506. Relationship edge 508 may indicate that this sensor core model has sensor data of type “sensor data,” as is indicated at a first dataset type node 510 that corresponds to a type of dataset. [0056] – The dataset context information may identify the dataset as coming from a particular type of data source (e.g., a pressure sensor) or may be of a particular data type (e.g., pressure sensor data)); and
It would have been obvious for one of ordinary skill in the art before the effective filing date of the claimed invention to modify Shaashua invention as modified in view of Gomadam in order to include a relational object which obtains data and identifies data from one entity to another; this is advantageous because it allows the system to identify types of data, the context of the data, and ultimately the overall core model type without prior knowledge (Gomadam, paragraph [0050]).
As per claim 22, Shaashua as modified teaches:
The one or more non-transitory computer readable media of claim 21, wherein one or more object entities of the plurality of object entities comprise (i) a static attribute to identify the first object entity (Shaashua, [0167] – A profile attribute can also be a numeric value, such as a numeric value representing temperature, heartbeat, time, location coordinate, IP address, physical address, or any combination thereof, wherein these addresses are interpreted as static attributes),
(ii) a dynamic attribute to store a data point associated with the first object entity that changes over time, and (iii) a behavioral attribute that defines an expected response of the first object entity in response to an input (Shaashua, [0067] and [0068] – Context awareness is interpreted as the device being able to identify different dynamic attributes of a data entity from data and storing a value in the data entity associated with the dynamic attribute. These paragraphs give examples such as recognition of child arriving home or the TV is tuned to a specific channel, wherein home arrival is a Boolean attribute which is a variable and is therefore dynamic. Also, the TV being tuned to a specific channel is also a variable attribute and is dynamic as well because at any time, the channel can change. Other important paragraphs which give context to this idea are [0070] – Geo-location of the IoT devices. Paragraph [0087] –recognition that every morning a user starts the coffee machine, turns on the music, leaves a house, and turns off all lights and thermostat. The IoT device can command the thermostat and the lights to be triggered by the context event of the user leaving the house. [0158] – The evolving context indicators can be updated in real-time and/or in response to new data from the activity data streams).
As per claim 23, Shaashua as modified teaches:
The one or more non-transitory computer readable media of claim 22, wherein the data entity that corresponds to the data that resulted from the operation of the second physical device is configured to store the dynamic attribute (Shaashua, [0160] – Context indicators in the form of historical profiles of trackable entities are interpreted as data entities storing values).
As per claim 24, Shaashua as modified teaches:
The one or more non-transitory computer readable media claim 23, wherein the second relational object semantically defines a connection between the data entity and the second object entity representing the second device (Shaashua, [0070] – An advantage of the data analysis and data correlation is generation of one or more layers of contextual, correlative, and/or semantic insights, trigger events, and/or actions. The data analysis module 308 may apply machine learning on the analyzed and/or correlated data coming from the three layers described above and create a sense of “cognition”—understanding of contextual, correlative, and/or semantic events in a user's life. These layers enable predictive or reflective comprehension of user and/or IoT device behavior patterns and/or trends, and may further enable synthesis of generalizations of user and/or IoT device activity or need).
As per claim 25, Shaashua as modified teaches:
The one or more non-transitory computer readable media of claim 21, wherein modifying the data entity to include the data that resulted from the operation of the second physical device and received from the first physical device comprises:
identifying a dynamic attribute in the data that resulted from the operation of the second physical device (Shaashua, [0082] – When a user turns off his office lights and leaves the work, then his home temperature may be set automatically for a desired temperature [0087] – The data correlation module 306 and the data analysis module 308 can recognize that every morning a user starts the coffee machine, turns on the music, leaves a house, and turns off all lights and thermostat, wherein the temperature is a dynamic attribute in the data and is also associated with the thermostat which is a device. See also [0154] – System identifies the different devices which is interpreted as object entities representing devices);
determining that the second relational object connects the data entity to the second object entity (Shaashua, [0038] – Context relevant entities may include people, places, groups, physical objects, brands, things, or any combination thereof. See also paragraph [0080] for door locking); and
storing a value that corresponds to the dynamic attribute in the data entity (Shaashua, [0067] and [0068] – Context awareness is interpreted as the device being able to identify different dynamic attributes of a data entity from data and storing a value in the data entity associated with the dynamic attribute. [0160] – Context indicators in the form of historical profiles of trackable entities are interpreted as data entities storing values).
Claims 29-33 are directed to a method performing steps recited in claims 21-25 with substantially the same limitations. Therefore, the rejections made to claims 21-25 are applied to claims 29-33.
Claims 37-40 are directed to a system performing steps recited in claims 21-25 with substantially the same limitations. Therefore, the rejections made to claims 21-25 are applied to claims 37-40.
Claims 26-27 and 34-35 are rejected under 35 U.S.C. 103 as being unpatentable over Shaashua in view of Devi in view of Gomadam and further in view of Ignatowski et al. US 20030200059 A1 (hereinafter referred to as “Ignatowski”).
As per claim 26, Shaashua doesn’t explicitly teach tracking historical values, however, Ignatowski teaches:
The non-transitory computer readable media of claim 21, wherein the program instructions further cause the one or more processors to create a shadow entity to store historical values of the data entity connected to the second object entity representing the second device (Ignatowski, [0018] – To calculate the performance estimate, the script measurements data is read from a table of previously measured values).
It would have been obvious for one of ordinary skill in the art at the time of the filing of the application to modify Shaashua’s invention as modified in view of Ignatowski in order to track arrival rates of a person; this is advantageous because it allows the system to fulfill objectives (Ignatowski, paragraph [0062]).
As per claim 27, Shaashua as modified with Ignatowski teaches:
The non-transitory computer readable media of claim 26, wherein the program instructions further cause the one or more processors to calculate an average value from the historical values stored in the shadow entity (Ignatowski, [0018] and [0062] – Objectives include a user arrival rate or average response time or any number of criteria specified such as weighted average).
Claims 34-35 are directed to a method performing steps recited in claims 26-27 with substantially the same limitations. Therefore, the rejections made to claims 26-27 are applied to claims 34-35.
Claims 28 and 36 are rejected under 35 U.S.C. 103 as being unpatentable over Shaashua in view of Devi in view of Gomadam in view of Ignatowski and further in view of De Baynast et al. US 20160179063 A1 (hereinafter referred to as “De Baynast”).
As per claim 28, Shaashua as modified doesn’t teach detection of an outlier, however, De Bayanst teaches:
The non-transitory computer readable media of claim 26, wherein the program instructions further cause the one or more processors to calculate an abnormal value from the historical values stored in the shadow entity (De Baynast, [0019] – Can input historical or live data. [0045] – A template for anomaly detection is a template specifying various different components which may be interconnected in different ways to achieve univariate outlier detection. The various different components in this scenario can be a component for calculating a moving average, a component for calculating a finite impulse response (FIR) filter, and a component for calculating a Z Test, wherein outlier and anomaly detection is interpreted as calculation of an abnormal value in the data).
It would have been obvious for one of ordinary skill in the art at the time of the filing of the application to modify Shaashua’s invention as modified in view of De Baynast in order to detect outliers; this is advantageous because it allows the system to alert a user of a potential problem (De Baynast, [0021]).
Claim 36 is directed to a method performing steps recited in claim 28 with substantially the same limitations. Therefore, the rejections made to claims 28 are applied to claim 36.
Response to Arguments
Applicant’s arguments filed 2/05/2026 have been fully considered but they are not persuasive. Applicant’s arguments begin on page 9 of Remarks wherein each specific argument is addressed below.
Argument: Applicant argues in Remarks on page 9 that the prior art of record doesn’t adequately teach the claimed limitation: “identifying a data entity of the plurality of data entities that corresponds to the data the resulted from the operation of the second physical device, the data entity identified using a second relational object of the plurality of relational objects that connects the data entity and the second object entity,” as recited in claim 21. Reasoning for this is that the second relational object in claim 21 is not between the sensor and the data it measures, but rather is between the data and the device that operates to affect the data.
In Response: A second or first device could mean either a thermostat or an HVAC for example due to the description of the relationship between these entities in the claim language. For instance, claims describe the respective entities as “receiving, from a first physical device of the plurality of physical devices, data that resulted from operation of a second physical device of the plurality of physical device;” Although Applicant makes a compelling argument for why a second relational object is not between a sensor and the data it measures, this doesn’t have to be the case. An HVAC could receive data from the sensor to switch on based on data received by the sensor OR the sensor could receive an increase in temperature based on the HVAC running. Furthermore, even if it is the case that certain devices must be interpreted in a specific way, Shaashua reads on exactly how the specification supports the claimed limitations. Gomadam is merely brought in to fill in the gap of a relational edge which could be a relational edge between a variety of entities without causing the reasons for obviousness to fall apart. For instance, Shaashua teaches a thermostat with data correlation in [0087] and [0154]. [0163] also teaches edges and types of relationships between entities. Gomadam is merely brought in to explicitly teach “a relational object that identifies a data entity corresponding to data that results from an operation of a physical device.” In response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
Applicant’s follow-up argument with respect to how a person of ordinary skill in the art should interpret a first and second device is not persuasive as laid out in the arguments above where an HVAC or Sensor could be a first or second device affecting the other to receive data in a way that affects the latter. Even with a concrete interpretation, this argument isn’t persuasive due to the precise mapping of Shaashua in view of the relational edge which Gomadam provides. The entities can be connected in a variety of ways whether it’s a sensor, or some other device. It would be obvious to one of ordinary skill in the art to combine the prior art of record to teach a relation of any two devices or any data going to or from the devices in a spatial graph because swapping one device with another device or one type of data with another wouldn’t break the teachings of the references used in the rejection. Many of the examples used in the references explicitly state that these are exemplary so that undue limitations may not be used to exclude specific scenarios from their general teachings (see paragraphs [0026]-[0034], [0037], [0039], etc. and more throughout Gomadam and [0033], [0039], [0040], [0050]-[0053], [0058], etc. and more throughout Shaashua).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
Baez et al. US 20170123389 A1 teaches classifying, by the processor-based capability determining element, the different devices according to capability maturity rankings supported by the different capabilities to provide maturity-based capability classifications for the different devices in an IoT environment (Abstract and [0005]).
Chi et al. US 20170220641 A1 teaches a relational database management system ([0043]).
Dong et al. US 20160119434 A1 teaches an intelligent negotiation service for internet of things (Title).
Reid et al. US 20160277374 A1 teaches unique identities in a database for devices in paragraphs [0075]-[0100].
Chung et al. US 20140205155 A1 teaches a unique identifier of RFID tags for smart devices in a relational database in paragraph [0195].
Angle et al. US 20140207282 A1 teaches a mobile robot that can identify rooms by combining identity information, an RSSI, and a remote control in paragraph [0097].
Penilla et al. US 20170103327 A1 teaches connected objects being used within the home, internet of things objects within or near or associated with the home or networks of the home in [0309].
Schindlauer et al. US 20120005220 A1 teaches dynamic asset monitoring and management using a continuous event processing platform (Title).
Fierro et al., November 10, 2019, “Dataset: An Open Dataset and Collection Tool for BMS Point Labels” (3 Pages).
Nagesh et al. US 20180034701 A1 teaches an Internet of Things (IoT) policy manager that generates a virtual entity comprising a plurality of data streams and associates a base policy with the virtual entity, the base policy defining a status of the virtual entity based on a first subset of the plurality of data streams (Abstract).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Matthew J. Ellis whose telephone number is (571)270-3443. The examiner can normally be reached on Monday-Friday 8AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Kavita Stanley can be reached at (571) 272-8352. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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April 28, 2026
/MATTHEW J ELLIS/Primary Examiner, Art Unit 2153
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