DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This action is in reply to the amendments field on 17 November 2025.
Claims 1-15 were previously canceled.
Claims 16, 19, 27 and 33 have been amended.
Claims 16-35 are currently pending and have been examined.
Claim Objections
Claims 1-35 are objected to because of the following informalities: typographical errors. The filed claim set no longer lists claims 1-15 as canceled. This is believed to be a mere typographical error. The numbering of claims is not in accordance with 37 CFR 1.126 which requires the original numbering of the claims to be preserved throughout the prosecution. When claims are canceled, they must remain listed as such. Appropriate correction is required.
Response to Amendment
The amendments filed on 17 November 2025 are insufficient to overcome the 101 rejections previously raised. Those rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
The amendments are insufficient to overcome the 103 rejections previously raised. Those rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Response to Arguments
Applicant’s arguments filed on 17 November 2025 have been fully considered but are not persuasive.
Regarding the 101, applicant argues that the claims recite elements incapable of performance in the mind and are therefore not abstract. Examiner respectfully disagrees.
Making identifications of clusters by way of performing a walk of the user graph is not meaningfully integrated into a practical application. Lexicography is not invoked with regards to “performing a walk of the user graph” and therefore the broadest reasonable interpretation of the limitations is applied and an identification is made by reviewing a graph in a step by step manner. This step is merely performed by the computing system with no further meaningful integration that limits the implementation of the step. The use of a computer in a generalized fashion does not meaningfully limit the otherwise abstract claims. In order for the addition of the machine to impose a meaningful limit on the scope of a claim, it must play a significant part in permitting the claimed function to be performed, rather than function solely as an obvious mechanism for permitting a solution to be achieved.
Applicant argues that the fact that the graph is a knowledge graph and is a computer implemented knowledge graph demonstrates a computer implemented data structure that exists within a distinct computing environment and the human mind cannot interface with such an environment. Examiner respectfully disagrees. This argument is more specific than the limitations set forth in the claim. The claim does not define that the graph is a knowledge graph. However, this is irrelevant because the additional elements in the claims do not establish meaningful limitations beyond generally linking the use of the recited abstract idea to a particular technological environment. There are no additional elements that limit the implementation of the identifying, computing or assigning functions in a meaningful way but instead merely establish a generic link to the computing system that applies the steps.
Applicant argues that the claims are directed towards a technical solution that is rooted in computer technology to overcome a program in the realm of the computer technology. Examiner respectfully disagrees. The specification describes that the claims relate to facilitating time management via graph intelligence. Time management is a problem that exists outside of the realm of computer technology. Using graphical data to facilitate time management does not illustrate a technical solution to a technical problem but instead illustrates using a computer as a tool to apply the steps in a particular environment computerized graphing environment but does not establish particular elements that perform the identifying functions or depict how a “walk” is performed or what a “walk” of the graph entails.
Computing a score and assigning rankings by a system does not confer patent eligibility on an otherwise abstract idea. Making the identifications, computations/calculations and assignments faster and more efficient is not a technical solution to a technical problem, the additional elements do not integrate the abstract idea into a practical application nor do they amount to significantly more. The added computer functionality amounts to mere instructions to implement the abstract idea on a computer and does not impart eligibility. The 101 rejections are respectfully maintained and updated below as necessitated by the amendments to the claims.
Regarding the 103 rejections, applicant argues that the previously cited references fail to teach each and every limitation of the amended claim. Specifically applicant alleges that Rogynskyy merely suggests assigning different weights not weights assigned with respect to productivity areas. Examiner respectfully disagrees. Rogynskyy in at least [0483] the node graph generation system using job history and performance history reconstructed from an internal member node graph, can generate a performance score or other information for a member node, by syncing info associated with the systems of record and electronic activities with a member node graph, the node graph can generate or extrapolate types of opportunities or features on the public profile, [0191] describes determining how much weight to assign to a particular activity based on different match scores. [0253, 0258, 0262-0263, 0320, 0353-0354, 0418, 0420] describing assigning different weights for electronic activities for different jobs by type, seniority, department, as well as a variety of other criteria. The specification of the instant application merely describes that the productivity areas are different projects. Therefore, Rogynskyy teaches weights assigned to different activities with respect to the projects/productivity areas, where the weights are based on the types of activities performed by the user with respect to the projects/productivity areas because Rogynskyy describes assigning weights to each activity for different types of jobs in different departments or to users with different seniority. See updated grounds of rejection set forth below as necessitated by the amendments to the claims.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 16-35 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Independent Claims 1, 27 and 33 recite limitations for identifying a user graph, identifying clusters of topic nodes where the topic nodes are associated with application activities, identifying activities by performing a walk of the user graph, computing scores for the productivity areas based on types of activities, a number of times the activities were performed and weights assigned to each activity, and assigning rankings. These limitations, as drafted, illustrate a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind. But for the “computer-implemented” language, the claims encompass a user simply making identifications, following connections, computing a score and determining a ranking as a series of observations and evaluations that could be performed the same way mentally or manually with pen and paper . The mere nominal recitation of a generic computer implemented environment does not take the claim limitations out of the mental processes grouping. Thus, the claims recite a mental process, which is an abstract idea.
This judicial exception is not integrated into a practical application. The claims recite additional elements including causing graphical data to be display, a processor and memory, as well as the language reciting that the other steps are “computer-implemented”. The causing graphical data to be presented on a display is recited at a high level of generality and amounts to mere data transmission, which is a form of insignificant extra solution activity. The processor and memory that can execute instructions to implement the other steps in the computer environment are also recited at a high level of generality and merely automate those steps. Each of the additional components is no more than mere instructions to apply the exception using a generic computer component in a generic computer environment. The combination of these additional elements is no more than mere instructions to apply the exception in a generic computer environment with generic computer components. Accordingly, even in combination, these additional elements do not integrate the abstract idea into a practical application. The claims are directed to an abstract idea.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed with respect to step 2A Prong 2, the additional elements in the claims amount to no more than mere instructions to apply the exception using a generic computer component or linking the steps to a generic computer environment. The same analysis applies here in 2B and does not provide an inventive concept.
For the causing to be presented on a display step that was considered extra solution activity in step 2A above, this has been re-evaluated in step 2B and determined to be well-understood, routine and conventional activity in the field. The specification does not provide any indication that the system components are anything other than generic, off the shelf computer components, and the Symantec, TLI and OIP Techs. court decisions in MPEP 2106.05 indicate that the mere collection, receipt or transmission of data over a network is a well-understood, routine and conventional function when it is claimed in a merely generic manner, as it is here.
Dependent claims 17-26, 28-32 and 34-35 include all of the limitations of the independent claims and therefore recite the same abstract idea. The claims merely narrow the recited abstract idea by describing additional observation and evaluation steps including determinations, comparing scores to thresholds, generating suggestions, computations using time and weights, describe the activities for a productivity area, describe following the connections in a graph by selecting, traversing, and making determinations, describing the productivity areas and clustering techniques, scores repeating the steps for a second user and graphs, and computing a third score.. The additional elements recited fail to transform the claims into a patent eligible invention but instead describe additional “apply it” type language in a generic environment and extra solution displaying that do not integrate the abstract idea into a practical application nor do they amount to significantly more for the same reasons and rationale as set forth above.
Accordingly, claims 16-35 are not drawn to eligible subject matter as they are directed to an abstract idea without significantly more.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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 16-35 are rejected under 35 U.S.C. 103 as being unpatentable over Rogynskyy (US 2019/0364130) in view of Kulkarni et al. (US 2020/0004565).
As per Claim 16 Rogynskyy teaches:
A computing system, comprising: a processor; and memory storing instructions that, when executed by the processor (Rogynskyy in at least Fig. 1-4, 18 and 28 illustrates a computer system including a processor and memory) , cause the processor to perform acts comprising:
identifying a computer-implemented user graph for a user from amongst a plurality of computer-implemented user graphs for users based upon an identifier for the user, wherein the user graph comprises nodes and edges connecting the nodes (Rogynskyy in at least Fig. 6B and [0184] illustrates and describes a system that can rely on the first name, last name and cell number which is generally unique to map the electronic activity to the correct node profile), wherein the nodes comprise: entity nodes, wherein the entity nodes represent documents associated with the user (Rogynskyy in at least Fig. 4 and [0144-0147] illustrates and describes a node graph generic system for constructing a node graph based on electronic activity that can include any type of electronic communication that can be stored or logged, examples including email, calls, calendar activity, social media messaging, mobile application message, instant messages, as well as electronic records of any other activity including digital content such as files, photographs, screenshots, browser history, internet activity and shared documents, see also Fig. 6B);
identifying, by way of performing a walk of the user graph, activities performed by the user with respect to the productivity areas within a period of time based upon the activity data (Rogynskyy in at least [0481] describes the node graph generation system including a performance module configured to generate performance profiles, the profile can be a performance profile of an employee in a company, of a department of the company, a group within a department or individual employee, the module can generate profiles using accessible data including electronic activities and systems of record accessible by the node graph generation system from multiple companies, the module can generate certain types of profiles while generate other types of reports or insights for other node profiles of the system that are not necessarily employees, [0154-0164] describes the electronic activity ingestor that gathers data to generate the node graphs, [0165-0170] describes the electronic activity parser that identifies attributes, values or characteristics of the electronic activities, [0189-0195] describes matching electronic activities to profiles, [0295] describes the object identification modules to identify which record objects or objects in the system match different activities and linking them via a linking engine);
computing scores for the productivity areas for the user, wherein a score for a productivity area is based upon respective types of activities performed by the user with respect to the productivity area, a number of times each of the activities were performed by the user with respect to the productivity area within the period of time, and weights assigned to each of the activities performed by the user with respect to productivity areas, wherein the weights are based upon the respective types of the activities performed by the user with respect to the productivity areas (Rogynskyy in at least [0483] the node graph generation system using job history and performance history reconstructed from an internal member node graph, can generate a performance score or other information for a member node, by syncing info associated with the systems of record and electronic activities with a member node graph, the node graph can generate or extrapolate types of opportunities or features on the public profile, [0191] describes determining how much weight to assign to a particular activity based on different match scores, see also [0253, 0258, 0262-0263, 0320, 0353-0354, 0418, 0420, ] describing assigning different weights for electronic activities for different jobs by type, seniority, department, etc., [0523] The node graph generation system 200 can generate an effort estimation model for each member node based on electronic activities or metrics thereof. The metrics can indicate low responsiveness, empty times on calendar during key business hours, or other predictors that someone is not putting in a threshold level effort. ; [0535] The system 200 can be configured to assign different weights to different factors used for matching leads and employees. In some embodiments, the system can enable each company to establish its own rules or policies for recommending matches between leads and employees. In some embodiments, the system 200 can be configured to train a machine learning model to match leads and salespersons based on analyzing a salesperson's matches with leads in the past as well as analyzing the lead's matches with other salespersons in the past);
assigning rankings to the productivity areas based upon the scores (Rogynskyy in at least [0484] describes how the system can parse or featurize information corresponding to tasks or activities associated with a member node that is derived from a record, outputs can include a performance score or grade indicating how well a member node has performed or may perform in general, at a type of task, in a specific job or under certain circumstances, as determined by the metadata extracted from the node graph); and
causing graphical data pertaining to at least a subset of the productivity areas to be presented on a display based upon the rankings (Rogynskyy in at least Fig. 15 illustrates displaying performance score or grade outputs).
Rogynskyy does not explicitly recite topic nodes and identifying clusters of topic nodes associated with activity data. However, Kulkarni teaches an AI engine that generates activity graphs defining a hierarchy of relationships. Kulkarni further teaches:
wherein the nodes comprise: topic nodes associated with activity data (Kulkarni in at least Fig. 2 illustrates topic nodes that are characters 202A-D, [0004] describes a user interacting with a large number of applications to fine content, see also [0034-0035, 0051, 0056, 0061, 0066] describing presenting an interactive activity specific UI of activity specific content for activity based applications and the ability to use an activity graph to reflect the user’s interaction with the configuration UI and other UIs provided by the activity management application and managing data from various sources, see also [0084-0085, 0133, 0151-0158, 0166-0168, 0174);
identifying clusters of the topic nodes (associated with activity data) corresponding to productivity areas of the user, wherein the topic nodes are associated with user activity data corresponding to user activity across one or more applications (Kulkarni in at least [0058, 0071] and Fig. 2 illustrates and describes identifying based on user indications, the AI engine can adjust the AI model and an activity graph that defines the clusters of content and associations between the content and activities, the activity graph including clusters);
Therefore, it would be obvious to one of ordinary skill in the art to modify the node and edge graphing to include techniques for topic nodes and clustering topic nodes because each of the elements were known, but not necessarily combined as claimed. The technical ability existed to combine the elements as claimed and the result of the combination is predictable because each of the elements performs the same function as they did individually. By utilizing and clustering topic nodes the combination presents a specific selection of content that is most relevant to a user at a particular point in time based on context and past activities.
As per Claim 17 Rogynskyy further teaches:
determining whether the score for the productivity area exceeds a threshold value for the productivity area; and when the score exceeds a threshold value, identifying a second user that belongs to a tenancy of the user via the user graph or a computer-implemented tenancy graph, wherein the graphical data includes an identifier for the second user and a suggestion that the user delegate certain activities performed with respect to the productivity area to the second user (Rogynskyy [0523] The node graph generation system 200 can generate an effort estimation model for each member node based on electronic activities or metrics thereof. The metrics can indicate low responsiveness, empty times on calendar during key business hours, or other predictors that someone is not putting in a threshold level effort.; [0528] The system 200 can be configured to automatically assign at least one employee of a company to one or more record objects or provide recommendations to the company (for instance, the data source provider) to assign the at least one employee to the one or more record objects. The system 200 can be configured to automatically assign or generate a recommendation to assign a business process or associated record object to an employee of a company associated with the business process. Perhaps, more generally, the system 200 can automatically match or generate a recommendation to match or pair an employee of a company and a record object of a system of record of the company)
As per Claim 18 Rogynskyy further teaches:
determining whether the score for the productivity area exceeds a threshold value for the productivity area; and when the score exceeds the threshold value, generating a suggestion that the user work in the productivity area for a lesser amount of time than an amount of time the user worked in the productivity area within the period of time, wherein the suggestion is presented within the graphical data (Rogynskyy e.g. [0523] The node graph generation system 200 can generate an effort estimation model for each member node based on electronic activities or metrics thereof. The metrics can indicate low responsiveness, empty times on calendar during key business hours, or other predictors that someone is not putting in a threshold level effort).
As per Claim 19 Rogynskyy further teaches:
wherein the scores are additionally computed based upon: amounts of time spent by the user on each of the activities performed by the user with respect to the productivity areas, wherein the amounts of time are obtained from the user graph ( Rogynskyy e.g. [0523] The node graph generation system 200 can generate an effort estimation model for each member node based on electronic activities or metrics thereof. The metrics can indicate low responsiveness, empty times on calendar during key business hours, or other predictors that someone is not putting in a threshold level effort. ; [0535] The system 200 can be configured to assign different weights to different factors used for matching leads and employees. In some embodiments, the system can enable each company to establish its own rules or policies for recommending matches between leads and employees. In some embodiments, the system 200 can be configured to train a machine learning model to match leads and salespersons based on analyzing a salesperson's matches with leads in the past as well as analyzing the lead's matches with other salespersons in the past).
As per Claim 20 Rogynskyy further teaches:
wherein the activities performed by the user with respect to the productivity area include one or more of: drafting first emails; reading second emails; participating in real-time message exchanges; attending meetings; authoring first documents; or reading second documents (Rogynskyy e.g. [0145] The electronic activity can be stored on one or more data source servers. The electronic activity can be owned or managed by one or more data source providers, such as companies that utilize the services of the data processing system 9300. The electronic activity can be associated with or otherwise maintained, stored or aggregated by an electronic activity source, such as Google G Suite, Microsoft Office365, Microsoft Exchange, among others. In some embodiments, the electronic activity can be real-time (or near real-time) electronic activity, asynchronous electronic activity (such as emails, text messages, among others) or synchronous electronic activity (Such as meetings, phone calls, video calls), or other activity in which two parties are communicating simultaneously.; [0167] The electronic activity parser 210 can be configured to first identify each of the nodes associated with the electronic activity. In some embodiments, the electronic activity parser 210 can parse the metadata of the electronic activity to identify the nodes. The metadata of the electronic activity can include a To field, a From field, a Subject field, a Body field, a signature within the body and any other information including in the activity header that can be used to identify one or more values of the one or more fields of any node profile of nodes associated with the activity, non-email activity can include meetings or phone calls, meta data of such activity can include a duration, participants location, etc., nodes are associated with activity if the node is a sender, recipient, participant or identified in the contents, it can also be inferred based on maintained information based on connections)).
As per Claim 21 Rogynskyy further teaches:
wherein performing the walk of the user graph comprises: traversing an edge connecting the topic node to an entity node within the user graph; determining a type of a document represented by the entity node based upon metadata for the entity node; determining a manner of access of the document based upon the metadata; and determining an activity performed by the user based upon the type of the document and the manner of access of the document (Rogynskyy in at least [0481] describes the node graph generation system including a performance module configured to generate performance profiles, the profile can be a performance profile of an employee in a company, of a department of the company, a group within a department or individual employee, the module can generate profiles using accessible data including electronic activities and systems of record accessible by the node graph generation system from multiple companies, the module can generate certain types of profiles while generate other types of reports or insights for other node profiles of the system that are not necessarily employees).
Rogynskyy does not teach but Kulkarni further teaches selecting a topic node in a cluster of the topic nodes (Kulkarni in at least Fig. 2 illustrates topic nodes that are characters 202A-D, [0058, 0071] and Fig. 2 illustrates and describes identifying based on user indications, the AI engine can adjust the AI model and an activity graph that defines the clusters of content and associations between the content and activities, the activity graph including clusters).
Kulkarni is combined based on the reasons and rationale set forth in the rejection of Claim 16 above.
As per Claim 22 Rogynskyy further teaches:
wherein the productivity areas and the activities performed by the user with respect to the productivity areas within the period of time are identified via a plurality of graph clustering techniques (Rogynskyy in at least [0322-0323] describes the ability to prune or restrict objects as matches and to perform specific groupings to cluster and analyze data) .
As per Claim 23 Rogynskyy further teaches:
wherein the graphical data comprises a first score for a first productivity area and a second score for a second productivity area (Rogynskyy in at least [0483] the node graph generation system using job history and performance history reconstructed from an internal member node graph, can generate a performance score or other information for a member node, by syncing info associated with the systems of record and electronic activities with a member node graph, the node graph can generate or extrapolate types of opportunities or features on the public profile).
As per Claim 24 Rogynskyy further teaches:
identifying a second computer-implemented user graph for a second user from amongst the plurality of computer-implemented user graphs for the users based upon an identifier for the second user, wherein the second user graph comprises second nodes and second edges connecting the second nodes, wherein the second nodes comprise: and second entity nodes, wherein the second entity nodes represent second documents associated with the second user; wherein the second productivity areas include the productivity area; performing a second walk of the user graph to identify second activities performed by the second user with respect to the second productivity areas within the period of time; computing second scores for the second productivity areas for the second user, wherein a second score for the productivity area is based upon respective types of activities performed by the second user with respect to the productivity area and a number of times each of the activities were performed by the second user with respect to the productivity area within the period of time; assigning second rankings to the second productivity areas based upon the second scores; and causing second graphical data pertaining to at least a subset of the second productivity areas to be presented on a second display based upon the second rankings (Rogynskyy in at least Fig. 6B and [0184] illustrates and describes a system that can rely on the first name, last name and cell number which is generally unique to map the electronic activity to the correct node profile, Fig. 4 and [0144-0147] illustrates and describes a node graph generic system for constructing a node graph based on electronic activity that can include any type of electronic communication that can be stored or logged, examples including email, calls, calendar activity, social media messaging, mobile application message, instant messages, as well as electronic records of any other activity including digital content such as files, photographs, screenshots, browser history, internet activity and shared documents, see also Fig. 6B, [0481] describes the node graph generation system including a performance module configured to generate performance profiles, the profile can be a performance profile of an employee in a company, of a department of the company, a group within a department or individual employee, the module can generate profiles using accessible data including electronic activities and systems of record accessible by the node graph generation system from multiple companies, the module can generate certain types of profiles while generate other types of reports or insights for other node profiles of the system that are not necessarily employees, [0483] the node graph generation system using job history and performance history reconstructed from an internal member node graph, can generate a performance score or other information for a member node, by syncing info associated with the systems of record and electronic activities with a member node graph, the node graph can generate or extrapolate types of opportunities or features on the public profile, [0484] describes how the system can parse or featurize information corresponding to tasks or activities associated with a member node that is derived from a record, outputs can include a performance score or grade indicating how well a member node has performed or may perform in general, at a type of task, in a specific job or under certain circumstances, as determined by the metadata extracted from the node graph, Fig. 15 illustrates displaying performance score or grade outputs).
Rogynskyy does not explicitly recite topic nodes and identifying clusters of topic nodes. However, Rogynskyy does not teach but Kulkarni further teaches a second topic node…second clusters (Kulkarni in at least Fig. 2 illustrates topic nodes that are characters 202A-D, [0058, 0071] and Fig. 2 illustrates and describes identifying based on user indications, the AI engine can adjust the AI model and an activity graph that defines the clusters of content and associations between the content and activities, the activity graph including clusters).
Kulkarni is combined based on the reasons and rationale set forth in the rejection of Claim 16 above.
As per Claim 25 Rogynskyy further teaches:
wherein the user and the second user are members of a tenancy, the acts further comprising: computing a third score based upon the score for the productivity area and the second score for the productivity area, wherein the third score is for the tenancy; and causing the third score to be presented on a third display to a third user, wherein the third user is a leader of the tenancy (Rogynskyy in at least [0483] the node graph generation system using job history and performance history reconstructed from an internal member node graph, can generate a performance score or other information for a member node, by syncing info associated with the systems of record and electronic activities with a member node graph, the node graph can generate or extrapolate types of opportunities or features on the public profile, [0207-0209] describes maintaining address and location information in the profile).
As per Claim 26 Rogynskyy further teaches:
wherein the graphical data comprises identifiers for first documents and identifiers for first people associated with a first productivity area and identifiers for second documents and identifiers for second people associated with a second productivity area (Rogynskyy in at least Fig. 6B and [0184] illustrates and describes a system that can rely on the first name, last name and cell number which is generally unique to map the electronic activity to the correct node profile).
As per Claims 27-35 are conceptually and substantially similar to those limitations set forth in claims 16-26 and are therefore rejected based on the same reasons and rationale set forth in the rejections of claims 16-26 above. Rogynskyy teaches presenting data in a GUI, designated time periods including historical ranges, and historical scores, normalization techniques in at least [0159] and geographical regions or assigned territories in at least [0551-0553].
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 STEPHANIE Z DELICH whose telephone number is (571)270-1288. The examiner can normally be reached on Monday - Friday 7-3:30.
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/STEPHANIE Z DELICH/Primary Examiner, Art Unit 3623