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 .
This action is made final.
Claims 1-20 are pending. Claims 1, 15 and 20 and are independent claims.
Response to Arguments
Applicant’s arguments, dated 3/27/2026, regarding the 35 U.S.C. 101 rejections of the previous office action have been fully considered and are persuasive. The 101 rejections have been withdrawn.
Applicant’s arguments, dated 3/27/2026, regarding the 35 U.S.C. 103 rejections of the previous office action have been fully considered, but are not persuasive. Due to the amendments, the scope of the claims has changed and new grounds of rejection have been applied – see the updated 103 rejections below.
Due to the amendments, the 35 U.S.C. 112 rejection for claim 14 has been withdrawn. However, due to the amendments, new 112 rejections have been applied to claims 1, 15 and 20 – see below.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1, 15 and 20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 1 recites the limitation "the independent semantic object model" in line 6. There is insufficient antecedent basis for this limitation in the claim.
Claim 15 recites the limitation "the independent semantic object model" in lines 6-7. There is insufficient antecedent basis for this limitation in the claim.
Claim 20 recites the limitation "the independent semantic object model" in line 7. There is insufficient antecedent basis for this limitation in the claim.
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.
Claim(s) 1, 2, 3, 4, 5, 15, 16, 17, 18, 19 and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski et al. (US 20210389968 A1), herein Majewski, in view of Bansal et al. (US 20210004770 A1), herein Bansal and Deligio et al. (US 20200358630 A1), herein Deligio.
Regarding claim 1, Majewski teaches: A computer-implemented method for maintaining a seeded semantic object model using one or more processors configured to execute stored instructions, the method comprising (¶61, In some cases, the EOM may include a collection of application programming interfaces (APIs) that enables a seeded semantic object model (e.g. the common object model) to be extended): identifying, by the processors, from a data store, a plurality of independent semantic object models having a plurality of object types, each object type defined by one or more attributes (¶62, Typically, in facilities having several assets, there may exist several inherent data ontologies, data models, enterprise patterns, relationships etc. associated with data that is pulled from the assets. For example, a building facility may include thousands or even millions of physical assets (e.g. sensors, valves, lightning units etc.) deployed within the facility. These assets may be provided by various manufacturers, and each of these assets may be transacting the data in its own way and data format – and – ¶60, For instance, in an industrial facility, an asset can be a ‘pump’ and its properties can include inlet and outlet pressure, speed, flow… In some examples, the asset instances can also define expected attributes of the asset), wherein the independent semantic object model defines the plurality of object types representing at least assets of a building (¶59, the edge controller 16 is configured to capture the data (e.g. the telemetry data and the semantic model) from various assets in the facility… the cloud 14 can further process the received data and/or the received common object model to create an extended object model (EOM). An extended object model (EOM) is representative of a data model which unifies several data ontologies, data relationships, and/or data hierarchies into a unified format)… generated through a machine-executable normalization operation (¶54, the edge controller 16 is configured to perform at least one of… normalizing the received data, which can include transforming the received data from a first format into a second format that supports a common object model)… automatically updating, by the processors, the seeded semantic object model… through a machine-executed aggregation operation, based at least in part on the at least one similar object type shared between at least the subset of the plurality of independent semantic object models (¶62, develop an EOM based on performing data processing which can include at least one of: (a) data normalization and/or data aggregation; (b) identification of several contextual relationships existing amongst the data and/or the assets; (c) identifying data patterns associated with an enterprise systems (i.e. OT data); and (d) extending the data model based on different data patterns such as, data architecture pattern, message flow pattern, enterprise data pattern etc. to identify contextual relationships amongst the assets and/or the data transacted through the assets –and – ¶54, aggregate the data (e.g., but not limited to, telemetry data and/or model data) from one or more sources in a facility. In some cases, the data and/or metadata information can be received and/or pulled from multiple assets corresponding to various independent and diverse sub-systems in the facility)…
Majewski fails to explicitly teach: processing, by the processors, the plurality of object types by computing a similarity score using a vector-based comparison of attribute-value sets… to determine at least one similar object type shared between at least a subset of the plurality of independent semantic object models… determining, by the processors, that the at least one similar object type shared between at least the subset of the plurality of independent semantic object models satisfies model updating criteria including a stability check requiring the similarity score to exceed a threshold evaluated at a defined time interval; in response to determining that the at least one similar object type shared between at least the subset of the plurality of independent semantic object models satisfies the model updating criteria… updating the model by integrating attribute-value information of the similar object type.
However, in the same field of endeavor, Bansal teaches: processing, by the processors, the plurality of object types by computing a similarity score using a vector-based comparison of attribute-value sets… to determine at least one similar object type shared between at least a subset of the plurality of independent semantic object models (¶101 Similarly, a rule or logic might compare an update entity with existing event entities of the same event type, and then determine whether attributes in the update entity match attributes in the existing event entities of that type, in order to determine the specific existing entity for which the update regards. As described previously, the attributes or features used for the comparison may comprise extracted or supplemental event content. In some embodiments, feature similarity between the entities may be determined using clustering, nearest neighbor, or similar process and a similarity score determined based on the similarity of entity attributes. Where the similarity score satisfies a threshold, the one or more entities may be determined to be associated with the update entity. For instance, updated event determiner 282 may determine that an update entity indicating a delayed flight is similar to an existing event entity for a flight event based on identical origin or destination city, airline name, flight number, or other attributes – comparing entities, i.e., objects, by comparing their attributes, including possibly a similarity score)… determining, by the processors, that the at least one similar object type shared between at least the subset of the plurality of independent semantic object models satisfies model updating criteria including a stability check requiring the similarity score to exceed a threshold evaluated at a defined time interval (¶81, related-event logic 235 may comprise rules that specify event entities having time attributes that are within a timeframe (such as events occurring on the same date, within a 24-hour interval, within eight hours, or within thirty minutes of each other) are more related than event entities having time attributes that are further apart (such as events that are separated by one or two months). In some embodiments, the time interval may be predetermined, such as 24 hours); in response to determining that the at least one similar object type shared between at least the subset of the plurality of independent semantic object models satisfies the model updating criteria… updating the model by integrating attribute-value information of the similar object type (¶102, For instance, the content in the existing event entity may be modified based on the content from the update entity. The modification may comprise changing existing attributes in the existing event entity or adding to the existing event entity additional or new information from the update entity).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use an attribute-value based similarity score evaluated at a defined time interval followed by an integrating of attribute-value information of similar objects as disclosed by Bansal in the method disclosed by Majewski to identify and link related elements (¶79, related-event logic 235 may comprise instructions (which may be carried out by a component of system 200, such as related events determiner 274) to analyze one or more attributes of an event entity, such as its location attribute, time attribute, attendance attribute, event category attribute, or other attributes to determine if two or more event entities are related).
Majewski in view of Bansal fails to teach: and causing, by the processors, rendering of a user interface that provides access to a seeded semantic object model template corresponding to the updated seeded semantic object model for enabling end-use of the updated seeded semantic object model template to generate or customize one or more independent semantic object model.
However, in the same field of endeavor, Deligio teaches: and causing, by the processors, rendering of a user interface that provides access to a seeded semantic object model template corresponding to the updated seeded semantic object model for enabling end-use of the updated seeded semantic object model template to generate or customize one or more independent semantic object model (¶126, User interface module 144 may be configured to prompt a user (e.g., visually, graphically, audibly, etc.) for input regarding building object database 142, building object templates 140, relationship database 152 or hierarchical projection models 154. In an exemplary embodiment, user interface module 144 prompts the user to create (or otherwise provides a user interface for creating) a template building object 140. User interface module 144 may also prompt the user to map BMS inputs to the template building object).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide access to manipulatable object model templates as disclosed by Deligio in the method disclosed by Majewski in view of Bansal to improve clarity and controllability of a system (¶68, BMS controller 12 is configured to make differences in building subsystems transparent at the human-machine interface or client interface level (e.g., for connected or hosted user interface (UI) clients 16, remote applications 18, etc.) – and – ¶70, A software defined building object of the present disclosure is intended to group otherwise ungrouped or unassociated devices so that the group may be addressed or handled by applications together and in a consistent manner).
Regarding claim 2, Majewski in view of Bansal fails to teach: The computer-implemented method of claim 1, wherein each of the plurality of independent semantic object models is independently customizable by at least one user.
However, in the same field of endeavor, Deligio teaches: wherein each of the plurality of independent semantic object models is independently customizable by at least one user (¶97, For example, if the user inputs a fan that serves a break room space into building object database 142, the user can manually tag the fan with tags describing each configuration the fan can operate under (i.e. variable speed, constant speed, heating, cooling, high air flow, low air flow, etc.), areas of the breakroom and areas immediately surrounding the breakroom that the fan can impact, and other pieces of building equipment the fan may have a relationship with).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use independently customizable object models as disclosed by Deligio in the method disclosed by Majewski in view of Bansal to enable a user to configure an automated building control system to their needs (¶105, In some embodiments, a user, such as a technician, implementing automatic control based on object relationships into a building can create different configuration schedules for each building object depending on the needs of a building owner. For example, an owner may wish to keep a building cool during the day when people are likely to occupy the space but warmer at night to avoid spending money on electricity to cool an empty building).
Regarding claim 3, Majewski further teaches: wherein the one or more attributes for each object type of the plurality of object types comprises a set of object properties for the object type (¶60, the edge controller 16 can utilize one or more asset templates associated with each asset for modelling the data into the unified format (i.e. object model). As an example, an asset template defines the typical properties for assets of an asset type).
Regarding claim 4, Majewski in view of Deligio fails to teach: The computer-implemented method of claim 1, wherein the at least one similar object type is determined using a machine learning model.
However, in the same field of endeavor, Bansal teaches: wherein the at least one similar object type is determined using a machine learning model (¶101, feature similarity between the entities may be determined using clustering, nearest neighbor, or similar process and a similarity score determined based on the similarity of entity attributes).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a machine learning model to determine similar types as disclosed by Bansal in the method disclosed by Majewski in view of Deligio to automate the discovery of similar items (¶79, related-event logic 235 may comprise instructions (which may be carried out by a component of system 200, such as related events determiner 274) to analyze one or more attributes of an event entity, such as its location attribute, time attribute, attendance attribute, event category attribute, or other attributes to determine if two or more event entities are related).
Regarding claim 5, Majewski in view of Deligio fails to teach: The computer-implemented method of claim 1, wherein the at least one similar object type is determined using a rules-based engine.
However, in the same field of endeavor, Bansal teaches: wherein the at least one similar object type is determined using a rules-based engine (¶81, related-event logic 235 may comprise rules that specify event entities having time attributes that are within a timeframe).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a rules based engine to determine similar types as disclosed by Bansal in the method disclosed by Majewski in view of Deligio to automate the discovery of similar items (¶79, related-event logic 235 may comprise instructions (which may be carried out by a component of system 200, such as related events determiner 274) to analyze one or more attributes of an event entity, such as its location attribute, time attribute, attendance attribute, event category attribute, or other attributes to determine if two or more event entities are related).
Regarding claim 15, it is an apparatus that implements a method similar to claim 1 and is rejected on the same grounds – see above.
Regarding claims 16-19, they recite similar limitations to claims 2-5 respectively and are rejected on the same grounds – see above.
Regarding claim 20, it is a product claim that implements a method similar to claim 1 and is rejected on the same grounds – see above.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Wang et al. (US 20240333602 A1), herein Wang.
Regarding claim 6, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein the model updating criteria comprise a certain percentage of the plurality of independent semantic object models having the at least one similar object type.
However, in the same field of endeavor, Wang teaches: wherein the model updating criteria comprise a certain percentage of the plurality of independent semantic object models having the at least one similar object type (¶23, In some examples, the orchestrator 102b may determine whether a simple majority of the votes (e.g., 50% or more of a highest number of possible votes) indicates that the AI model update 112 is to be implemented, and implement the AI model update if so – i.e., Wang teaches a percentage based model updating criteria).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use 50% update criteria as disclosed by Wang in the method disclosed by Majewski in view of Bansal and Deligio to improve model accuracy (¶19, may correspond to an overall improvement in the accuracy (e.g., less errors) relative to the original model)
Claim(s) 7, 12 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Bellinger et al. (US 20220284362 A1).
Regarding claim 7, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein determining whether the at least one similar object type satisfies the model updating criteria is determined using a machine learning model.
However, in the same field of endeavor, Bellinger teaches: wherein determining whether the at least one similar object type satisfies the model updating criteria is determined using a machine learning model (¶48, The processing of the data to determine whether to generate a new node, update an existing node and its edges, or delete an existing node and its edges, may be performed by edge ID and generation engine 130, which includes machine learning models 132 and natural language processing models 134).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a machine learning model to determine if model updating criteria is met as disclosed by Bellinger in the method disclosed by Majewski in view of Bansal and Deligio to automate the model updating process (¶37, natural language processing and/or machine learning models may be applied to electronic communication data and electronic documents, to automatically identify new relationships, determine when existing relationships have been modified, and determine when existing relationships no longer exist or have substantially declined. Utilizing these determinations, the organizational graph service can automatically propose updates to an organizational graph, thereby significantly reducing the amount of time needed to be spent by administrative users, such as human resources and information technology employees, in updating organizational graphs when changes are made in an organization).
Regarding claim 12, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, further comprising deploying the updated seeded semantic object model to at least one new independent semantic object model.
However, in the same field of endeavor, Bellinger teaches: further comprising deploying the updated seeded semantic object model to at least one new independent semantic object model (¶29, identify new organizational relationships amongst organizational members and/or user accounts, determine that existing relationships have been modified, and/or determine that previously existing relationships no longer exist or have substantially declined. The organizational graph service may subsequently modify a corresponding organizational graph based on these determinations).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to automatically deploy updates to a model as disclosed by Bellinger in the method disclosed by Majewski in view of Bansal and Deligio to avoid time consuming manual updates (¶37, significantly reducing the amount of time needed to be spent by administrative users…. in updating organizational graphs when changes are made in an organization).
Regarding claim 13, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein determining whether to update the seeded semantic object model is performed at a defined time interval.
However, in the same field of endeavor, Bellinger teaches: wherein determining whether to update the seeded semantic object model is performed at a defined time interval (¶47, The organizational graph service may analyze organizational data to determine whether new nodes should be generated and whether existing nodes and their edges should be updated or deleted. Those determinations may be made periodically (e.g., every 24 hours, every week)).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine if an update is required using a defined time interval as disclosed by Bellinger in the method disclosed by Majewski in view of Bansal and Deligio to automate the updating process and save time (¶37, significantly reducing the amount of time needed to be spent by administrative users…. in updating organizational graphs when changes are made in an organization).
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Smith et al. (US 20200133662 A1), herein Smith.
Regarding claim 8, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein determining whether the at least one similar object type satisfies the model updating criteria is determined using a rules-based engine.
However, in the same field of endeavor, Smith teaches: wherein determining whether the at least one similar object type satisfies the model updating criteria is determined using a rules-based engine (¶84, Automatic updating may occur at a fixed interval, based on a real-time event or trigger such as an update to the data sources, based on a rules-based system, or based on another mechanism – i.e., a rules-based system to determine if model update criteria is fulfilled).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to use a rules-based approach to determine update occurrence as disclose by Smith in the method disclosed by Majewski in view of Bansal and Deligio to automate the model updating process (¶84, Automatic updating may occur).
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Wirfs-Brock et al. (US 20080244624 A1), herein Wirfs-Brock.
Regarding claim 9, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein updating the seeded semantic object model comprises generating, in the seeded semantic object model, an additional object type corresponding to the at least one similar object type.
However, in the same field of endeavor, Wirfs-Brock teaches: wherein updating the seeded semantic object model comprises generating, in the seeded semantic object model, an additional object type corresponding to the at least one similar object type (¶26, logic for providing a framework with a common set of domain-specific entity identifier types that a plurality of object models representing a conceptually similar entity can use to correlate model-specific object instances 206; logic for providing one or more extensibility mechanisms to allow third parties to extend the set of domain-specific entity identifiers types – a third party is able to introduce new domain-specific object types in the models described by Wirfs-Brock, i.e., generate an additional object type).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to generate a new object type as disclosed by Wirfs-Brock to allow for additional types to be defined based on similar object types as disclosed by Wirfs-Brock in the method disclosed by Majewski in view of Bansal and Deligio to allow for a flexible model (¶50, Authors of object models cannot always know in advance the complete set of entity identifiers they must support. For example, a client may need to correlate two object models, yet neither object model recognizes the other's entity identifiers. An extensibility mechanism is provided in one implementation to allow third parties to extend the set of entity identifiers that a particular object model recognizes).
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Sager et al. (US 11561987 B1), herein Sager.
Regarding claim 10, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein updating the seeded semantic object model comprises replacing an existing object type in the seeded semantic object model with an object type corresponding to the at least one similar object type.
However, in the same field of endeavor, Sager teaches: wherein updating the seeded semantic object model comprises replacing an existing object type in the seeded semantic object model with an object type corresponding to the at least one similar object type (Col 14, line 63, reclassifying one or more of the documents or document sets includes receiving an input indicating a new classification for one or more documents or document sets and changing a classification of the one or more documents or document sets from a current classification to the new classification – object classification is being interpreted as an object type).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update the type of items by replacing an existing type in a model as disclosed by Sager in the method disclosed by Majewski in view of Bansal and Deligio to maintain the model in changing conditions (Col 14, line 59, On occasion, a document, or set of documents, are either misclassified, or the classification schema itself requires updating as classification conditions change. Under such scenarios, reclassification of part or all of a document category is useful).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Laxminarayanan (US 20090171996 A1).
Regarding claim 11, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1, wherein updating the seeded semantic object model comprises updating, in the seeded semantic object model, at least one object attribute of an existing object type corresponding to the at least one similar object type.
However, in the same field of endeavor, Laxminarayanan teaches: wherein updating the seeded semantic object model comprises updating, in the seeded semantic object model, at least one object attribute of an existing object type corresponding to the at least one similar object type (Abstract, The method includes propagating a change of data of a first entity type to one or more related or associated entity types in an active associative object model – and – ¶25, it is possible to propagate a change to an attribute of the object through a plurality of objects within the AOM).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to update attributes of objects of similar types as disclosed by Laxminarayanan in the method disclosed by Majewski in view of Bansal and Deligio to reduce redundancy (¶19, will instantly be able to observe the change to the entity in a highly normalized fashion such that no redundant actions or data is required to fully realize a change in the entity. This eliminates redundant data within the entire enterprise)
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Majewski in view of Bansal and Deligio as applied to claim 1 above, and further in view of Kaliski et al. (US 20240126238 A1), herein Kaliski.
Regarding claim 14, Majewski in view of Bansal and Deligio fails to teach: The computer-implemented method of claim 1 further comprising: evaluating one or more attributes of an object type associated with an instance of an independent semantic object model of the plurality of independent semantic object models for presence of an instantiation condition indicative of a potential issue with respect to the object type; generating a notification in response to a detected instantiation condition, the notification comprising one or more of an alert notification, a warning notification, a recommendation notification, or a corrective action notification, and/or the like; and transmitting the notification to a client device.
However, in the same field of endeavor, Kaliski teaches: evaluating one or more attributes of an object type associated with an instance of an independent semantic object model of the plurality of independent semantic object models for presence of an instantiation condition indicative of a potential issue with respect to the object type; generating a notification in response to a detected instantiation condition (¶108, the framework function can be configured to automatically generate an output in the form of a message or an alert in case a newly created delta object indicates that one of the said attributes of an instance of one of the said external object types is above or below the threshold), the notification comprising one or more of an alert notification, a warning notification, a recommendation notification, or a corrective action notification (¶108, an output in the form of a message or an alert); and transmitting the notification to a client device (¶114, returning the output to the client – the client possibly referring to a client device: ¶223, For example, the client can be a client program or a client device).
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to evaluate attributes of an object to generate a notification and transmitting the output to a client device as disclosed by Kaliski in the method disclosed by Majewski in view of Bansal and Deligio to automatically and continuously monitor a system or process for errors (¶7, changes… quickly lead to the system's mode of operation ultimately no longer meeting the respective requirements without this being even noticed. Errors occur sporadically and their assignment to a specific system component becomes more and more impossible).
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HARRISON CHAN YOUNG KIM whose telephone number is (571)272-0713. The examiner can normally be reached Monday - Thursday 10:00 am - 6:00 pm.
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/HARRISON C KIM/ Examiner, Art Unit 2145
/CHAU T NGUYEN/ Primary Examiner, Art Unit 2145
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