DETAILED ACTION
This communication is a Final Office Action on the merits in response to communications received on 10/14/2025. Claims 1, 9, and 17 have been amended. Therefore, claims 1-4, 7-9, 11-12, 15-18, and 20 are pending and have been addressed below. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Rejections - 35 USC § 101
1. 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.
2. Claims 1-4, 7-9, 11-12, 15-18, and 20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Under Step 1 of the two-part analysis from Alice Corp, claim 1 recites a process (i.e., an act or step, or a series of acts or steps), claim 9 recites a machine (i.e., a concrete thing, consisting of parts, or of certain devices and combination of devices), claim 17 recites a manufacture (i.e., an article that is given a new form, quality, property, or combination through man-made or artificial means.) Thus, each of the claims fall within one of the four statutory categories.
3. Under Step 2A – [Prong One] of the two-part analysis from Alice Corp, the claimed invention recites an abstract idea.
Claims 1, 9, and 17 recite:
“receiving…for user, a message comprising an identifier for an issue comprising one or more of a computer issue, an application issue, a network issue, and a remote location issue”, “wherein the message is identified;”, “identifying…a solution category for the issue, wherein using historical service data and historical sentiment scores, wherein historical service data comprises processing of service tickets and service requests;”, “generating…a custom solution;”, “identifying and retrieving…information associated with the custom solution for the solution category, the information comprising one or more of an article, a video, a patch, a search result, an automated batch solution, and a script;”, “determining…whether the custom solution was successful by monitoring operation…to detect resolution of the issue;”, “in response to the custom solution being unsuccessful, requesting…feedback on the custom solution;”, “receiving…the feedback …, wherein the feedback is requested;”, “assigning…a sentiment score to the custom solution based on the feedback, wherein the sentiment score is positive, neutral, or negative;”, “using the sentiment score and solution effectiveness”, and “identifying…a format for the custom solution”, “wherein the format is selected based on a success rate for the format”, and “wherein the success rate is based on the feedback requested from the user on the effectiveness of the solution.”
4. Under the broadest reasonable interpretation, the limitations recite the abstract idea of providing a customized solution to a customer to help troubleshoot and resolve an issue and requesting the customer to provide feedback regarding the customized solution encompasses concepts such as commercial interaction, (i.e., marketing or sales activities, business relations), managing personal behavior or interactions, and mental processes, (i.e., observation, evaluation, judgement, opinion), that fall within the certain methods of organizing human activity and mental processes groupings of abstract ideas. See MPEP 2106.04
The Applicant’s Specification, [¶ 0002] Information technology departments in large organizations often provide self-service systems for users seeking to resolve minor issues. A disorganized and lacking self-service system increases user frustration in solving their specific ticket issue. Searching for multiple, often irrelevant, knowledge articles, different applications for general support, a lack of ability to easily contact representatives, and confusing terminology often results in an influx of reopened tickets. Often, the users that provide feedback are those that experienced some difficulty in resolving the issue.
Consistent with the disclosure the series of steps describe sales/marketing activities and managing personal behavior because the limitations recite interactions that normally take place between a customer and a help desk and/or contact center. For example, a customer may submit a service request, receive a custom solution, and provide feedback, i.e., helpful, relevant, for the solution. Also, the series steps recite a mental process for analyzing a user’s service request to identify generate a custom solution. The acts also recite a mental processes for determining when the recommended solution is unsuccessful and assigning a sentiment score to the user’s feedback which is used to customize future recommended solutions. Thus, the series of steps may be reasonably characterized as mental processes that can be practically performed in the human mind, with or without the use of a physical aid such as pen and paper. As such, the claim recites an abstract idea.
5. Under Step 2A – Prong Two of the two-part analysis from Alice Corp, this judicial exception is not integrated into a practical application because the additional elements of: “by a solution recommendation computer program that is executed”, “by an electronic device”, “from a user electronic device”, “by the solution recommendation computer program using natural language”, “, by the solution recommendation computer program and in communication with a trained category identification machine learning engine”, “the category identification trained machine learning engine is trained”, “from a knowledge base”, and “retraining, by the solution recommendation computer program, the trained machine learning engine.” – see claim 1, “a system” – see claim 9, “a non-transitory computer readable storage medium”, “one or more computer processors” – see claim 17 are recited at a high-level of generality in light of the specification. Thus, because the specification describes the additional elements in general terms without describing the particulars the additional elements may be broadly but reasonably construed as reciting generic computer components performing conventional computer functions in light of the applicant’s specification. Therefore, the additional elements recited in the claim add the words “apply it” with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely use a computer processor as a tool to perform the abstract idea as discussed in MPEP 2106.05 (f).
Thus, the additional claim elements are not indicative of integration into a practical application, because the claims do not involve improvements to the functioning of a computer, or to any other technology or technical field (MPEP 2106.05(a)), the claims do not apply or use the abstract idea to effect a particular treatment or prophylaxis for a disease or medical condition (Vanda Memo), the claims do not apply the abstract idea with, or by use of, a particular machine (MPEP 2106.05(b)), the claims do not effect a transformation or reduction of a particular article to a different state or thing (MPEP 2106.05(c)), and the claims do not apply or use the abstract idea in some other meaningful way beyond generally linking the use of the abstract idea to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception (MPEP 2106.05(e) and Vanda Memo). Therefore, the claims do not, for example, purport to improve the functioning of a computer. Nor do they effect an improvement in any other technology or technical field. Accordingly, the additional elements do not impose any meaningful limits on practicing the abstract idea and the claims are directed to an abstract idea.
6. The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, as discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of: “by a solution recommendation computer program”, “an electronic device”, “using a trained machine learning engine”, “wherein the trained machine learning engine is trained”, “from a knowledge base”, and “retraining, by the solution recommendation computer program, the trained machine learning engine.” – see claim 1, “a system”, “a user electronic device;” “an electronic device”, “a solution recommendation computer program”, “a trained solution recommendation machine learning engine;”, “a solution knowledge base;”, “the solution recommendation computer program retrains the trained solution recommendation machine learning engine” – see claim 9, “a non-transitory computer readable storage medium”, “one or more computer processors”, “a user electronic device”, “using a trained machine learning engine”, “wherein the trained machine learning engine is trained”, “a knowledge base”, “retraining the trained machine learning engine – see claim 17 at best amounts to nothing more than mere instructions in which to apply the judicial exception and cannot provide an inventive concept.
7. Claims 2-4, 7-9, 11-12, 15-18, and 20 are dependent of claims.
Claims 2, 3, 11, 18 recite “wherein the solution recommendation computer program provides the custom solution in the format”, “wherein the format comprises one of an article, a video, and a script” which further describes the data/information recited in the abstract idea, but does not make the claim any less abstract. Claims 4 and 12 recite “wherein the format is selected based on a prior custom solution that was provided to the user.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. Claims 7, 15, 20 recite “wherein the solution recommendation computer program determines whether the custom solution was successful by monitoring operation of the user electronic device” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract, Claims 8 and 16 recite “further comprising: in response to the custom solution being successful, assigning, by the solution recommendation computer program, a neutral sentiment score for the custom solution.” which further narrows how the abstract idea may be performed, but does not make the claim any less abstract. The additional elements recited in the dependent claims use computer components or other machinery in their ordinary capacity for economic or other tasks (e.g., to receive, process, or output data) and/or simply adds generic computer components after the fact to the abstract idea which does not integrate the judicial exception into a practical application or provide an inventive concept.
Claim Rejections - 35 USC § 103
8. 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.
9. The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or non-obviousness.
10. Claim(s) 1-4, 7-9, 11-12, 15-18, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over El-Nakib in view of Liu (US 2013/0159881 A1) in further view of Sayko (US 2014/0126714 A1)
With respect to claims 1, 9, and 17, El-Nakib discloses
a method (col. 18:43-45), system (Fig. 9: discloses a system), and non-transitory computer readable storage medium (Fig. 9: discloses a computer readable medium) comprising:
receiving, by a solution recommendation computer program (Fig. 2, col. 7:50-54: discloses an automated support engine 207) that is executed by an electronic device (Fig. 2, col. 7:50-54: discloses one or more servers 210) and from a user electronic device for user (Fig. 2, col. 7:50-54: discloses a user device 202),
a message comprising an identifier for an issue comprising one or more of a computer issue, an application issue, a network issue, and a remote location issue (col. 3:38-48, col. 7:61-67, col. 8:35-47: discloses a service request includes any type of request from a user for assistance, advice, information, or other support associated with a computing device or computing system. Examples of service requests include requests for assistance with establishing a network connection, diagnosing performance issues, obtaining configuration information, assistance re-booting a computer, assistance installing new hardware or new software components, or any other type of support assistance. A service request from a user identifies a unique issue.),
wherein the message is identified by the solution recommendation computer program using natural language (col. 8:48-53, col. 12:4-9: discloses the automated support engine 207 includes a text analyzer that parses the problem description to identify the one or more issues. The natural language processing 310 is included in the automatic support engine to assist with text analysis of the problem description.);
identifying, by the solution recommendation computer program (Fig. 2, col. 7:50-54: discloses an automated support engine 207) and in communication with a trained category identification machine learning engine (col. 15:4-11: discloses training component 602), a solution category for the issue (col. 8:41-53: discloses identifies a set of one or more keywords associated with the one or more issues.),
wherein the category identification trained machine learning engine is trained using historical service data and historical sentiment scores (col. 15:4-11: discloses training component 602 analyzes all responses and scores associated with a same issue and similar issues.), wherein historical service data comprises processing of service tickets and service requests (col. 3:34-48, col.9:20-22: discloses text documents, previously generated responses to closed service requests, and other unstructured data sources.);
generating, by the solution recommendation computer program in communication with a trained custom solution recommendation machine learning engine, a custom solution (col. 9:24-40, col. 10:3-6: discloses the automated support engine generates a response to the service request. The response is an automatic initial response that includes the at least one solution.);
identifying and retrieving, by the solution recommendation computer program, information associated with the custom solution for the solution category (col.6:5-9: discloses searches the database of response resources to identify solution(s) and other information related to an identified problem.) from a knowledge base, the information comprising one or more of an article, a video, a patch, a search result, an automated batch solution, and a script (cols.5-6:62-4: discloses the data storage system 128 includes a database storing a plurality of response resources. A response resource includes electronic documents, videos, sound files, knowledge base (KB) articles, previous service request responses, manuals, reference materials, and any data capable of being stored in a data repository.);
determining, by the solution recommendation computer program, whether the custom solution was successful by monitoring operation of the user electronic device to detect resolution of the issue (col. 14:1-4: discloses after the automatic initial response 500 is sent, if the automatic initial response solves the issues, the service request is closed.);
in response to the custom solution being unsuccessful (col. 10:60-67, col. 14:47-67), requesting, by the solution recommendation computer program, feedback on the custom solution from the user electronic device (col. 10:60-67, col. 11:1-15, col. 14:47-67: discloses the automated support engine 207 prompts the user 204 to provide a score rating the quality of response. A score lower indicates a less relevant response that was not helpful in resolving the issues. A thumbs down icon for a less relevant response.);
receiving, by the solution recommendation computer program, the feedback from the user electronic device (col. 10:60-67, col. 14:47-67: discloses one or more users 612 provide a set of scores 622 ranking a quality of response.),
assigning, by the solution recommendation computer program, a sentiment score to the custom solution based on the feedback (col. 10:60-67, col. 14:47-67: discloses the set of scores 622 may include a score rating the overall user experience utilizing the automatic initial response.), wherein the sentiment score is positive, neutral, or negative (col. 11:1-20: discloses the score may be an indicator such as a check symbol or plus sign for a relevant response and an “x” symbol or a thumbs down icon for a less relevant response.);
retraining, by the solution recommendation computer program, the trained custom solution recommendation machine learning engine using the sentiment score and solution effectiveness (col. 11:21-32: discloses the training component 238 analyzes at least one score in the plurality scores 242 to modify or adjust one or more parameters associated with the automated support engine. The training component modifies the parameters associated with the automated support engine to improve the quality of the responses and increase the scores received from users. As new responses are generated and new scores are received from users, the training component 238 recursively adjusts the parameters to improve the accuracy, helpfulness, and relevance of the responses generated by the automated support engine 207.), and
identifying, by the solution recommendation computer program, a format for the custom solution (col. 10:11-25, col. 12:50-67, col. 13:59-67: discloses the automated support engine 207 sends the response 232 to the user device 202 associated with the service request 206. Displaying the automatic initial response to the user includes outputting the automatic initial response to the user in a graphical display, an audio output, a projected image, or any other type of output associated with the user device 202. If an issue identified in an open service request is the same or similar to an issue addressed by a pre-generated automatic initial response in the response history data, the automated support engine sends the pre-generated response to the client to assist the user in resolving the currently open service request instead of generating a new automatic initial response to the open service request. The response 232 in some examples is output to the user in a visual format, an audio format, or a combination of a visual and audio format.),
The El-Nakib reference does not explicitly disclose the following limitation. In the same field of endeavor, the Liu reference is related to techniques for providing remote technical support (¶ 0005) and teaches:
wherein the format is selected based on a success rate for the format (¶ 0024, 0026, 0028-0029: discloses as an end-user performs activities on the portal system 204, i.e., selecting certain topics and their corresponding ratings, recommendations are presented to the end-user concerning relevant articles, user guides, 1-click fixes. For example, to recommend a specific 1-click fix to an end-user, historical usage of such 1-click fixes as well as usage of such 1-click fixes by a group of other end-users can be combined according to a weighted average.), and
wherein the success rate is based on the feedback requested from the user on the effectiveness of the solution (¶ 0028-0029: discloses based on their end-user ratings, i.e., from those results that have the most “thumbs up” the recommendation engine is used to provide estimates of most relevant information based on end-user activities on the portal.)
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the system and methods of El-Nakib, to include wherein the format is selected based on a success rate for the format; wherein the success rate is based on the feedback requested from the user on the effectiveness of the solution, as disclosed by Liu to achieve the claimed invention. As disclosed by Liu, the motivation for the combination would have been to provide advantages that facilitate self-help and improve upon current remote technical support systems. (¶ 0005)
The combination of El-Nakib and Liu references does not explicitly disclose the following limitations. However, the Sayko reference teaches:
wherein the feedback is requested using an out-of-band communication channel;
(¶ 0046, 0070: discloses the contact center provides OOB or supplemental communications channels to the customer end-user devices 12. The out-of-band communication channel functionality may be provided by the web server 120. These out-of-band communications channels may include, without limitation, channels that provide web browser-based forms, text chats, video, and other types of media that may typically be presentable by a web server to a customer via a web browser. For example, text-based chat may be used for increased accuracy when communicating addresses, email addresses, and credit card payment information, and to provide URLs to web pages to answer user questions. The OOB channels may also be used for video tutorials, official documentation, and screen sharing for demonstrating usage. OOB channels may also provide some redundancies in the case of poor connections in the real-time communication channel.
Accordingly, the passages from the Sayko reference evidence the use of OOB channels were known in the state of the art and previously used in the industry to provide web browser-based forms, text chats, video, and other types of media that may typically be presentable by a web server to a customer via a web browser by contact centers.
Therefore, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have modified the method and system of El-Nakib and Liu, to wherein the feedback is requested using an out-of-band communication channel, as disclosed by Sayko to achieve the claimed invention. As disclosed by Sayko, the motivation for the combination would have been to provide increased reliability and accuracy when communicating supplemental information to/from customer end-user devices as expressly suggested by Sayko. (¶ 0046)
With respect to claims 2, 3, 11, and 18, the combination of El-Nakib, Liu, and Sayko discloses the method, system, and non-transitory computer readable storage medium,
wherein the solution recommendation computer program provides the custom solution in the format (col. 10:11-25, col. 13:59-67: El-Nakib discloses the automated support engine 207 sends the response 232 to the user device 202 associated with the service request 206. Displaying the automatic initial response to the user includes outputting the automatic initial response to the user in a graphical display, an audio output, a projected image, or any other type of output associated with the user device 202.),
wherein the format comprises one of an article, a video, and a script. (col. 10:11-25, col. 13:59-67: El-Nakib discloses the automatic initial response 500 is a recorded audio response message, a video message, or any other format for presenting suggested solutions and recommended reference materials to a user.)
With respect to claims 4 and 12, the combination of El-Nakib, Liu, and Sayko discloses the method and system,
wherein the format is selected based on a prior custom solution that was provided to the user. (col. 10:11-25, col. 12:50-67, col. 13:59-67: El-Nakib discloses the automated support engine 207 sends the response 232 to the user device 202 associated with the service request 206. Displaying the automatic initial response to the user includes outputting the automatic initial response to the user in a graphical display, an audio output, a projected image, or any other type of output associated with the user device 202. If an issue identified in an open service request is the same or similar to an issue addressed by a pre-generated automatic initial response in the response history data, the automated support engine sends the pre-generated response to the client to assist the user in resolving the currently open service request instead of generating a new automatic initial response to the open service request.)
With respect to claims 7, 15, and 20, the combination of El-Nakib, Liu, and Sayko discloses the method, system, and non-transitory computer readable storage medium,
wherein the solution recommendation computer program determines whether the custom solution was successful by monitoring operation of the user electronic device. (col. 10:26-30, col. 14:1-4: El-Nakib discloses if the suggested solutions and links are sufficient the user closes the service request. After the automatic initial response 500 is sent, if the automatic initial response solves the issues, the service request is closed.)
With respect to claims 8 and 16, the combination of El-Nakib, Liu, and Sayko discloses the method and system, further comprising:
in response to the custom solution being successful, assigning, by the solution recommendation computer program, a neutral sentiment score for the custom solution. (cols 10-11:60-10: El-Nakib discloses the score 240 is a score on a scale ranking the response on a scale indicating whether the information provided in the automatic initial response was helpful, accurate, useful, or relevant to resolving the issues associated with the service request. For example, a score may include a score from one to five, where a high score indicates a relevant response providing information that resolved the issue and a lower score indicates a less relevant response that was not helpful in resolving the issue. The examiner notes a score of three would indicate a neutral sentiment score for the custom solution.)
Response to Arguments
Applicant's arguments filed 10/14/2025 have been fully considered but they are not persuasive.
With Respect to Rejections Under 35 USC 101
Applicant argues “Claim 1 (emphasis added). The methods and systems as claimed, disclose removing application swivel chairing, and delivers a scalable, consistent automated self-help experience. As-filed Application, [0024], [0032]-[0033]. Thus, the system is enabled to continuously self-improve by retraining two separate machine learning engines as discussed in the amended claims, providing a user experience that builds upon the sentiments (e.g., the likes and dislikes) of the user interface. Thus, this is not a "method of organizing human activity." “These groupings do not apply here. The claims do not recite any of these activities - as noted above, the claims provide a technical solution directed to retraining a trained machine learning engine using the sentiment score and solution effectiveness. Specification, claim 1. In other words, again, according to the 2019 PEG, improvements to the functioning of a computer or to any other technology or technical field is indicative of integration of a judicial exception into a practical application. Furthermore, under the 2019 PEG, by retraining the trained machine learning engine, the claims disclose applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment. As such, the claims disclose significantly more than a drafting effort designed to monopolize the exception, but instead disclose a method for enabling a technological environment to continuously self-improve over its lifetime based on user feedback. Accordingly, the claims are directed to patentable subject matter, e.g., improving the functioning of a computer and applying the improved functioning in a meaningful way to the computer. Thus, the claims are not directed to "Certain Methods of Organizing Human Activities," and Applicant respectfully submits that the pending claims are directed to statutory subject matter. Under Step 2A, Prong Two, the claims integrate any alleged judicial exception into a practical application.
The Examiner respectfully disagrees.
Contrary to the remarks, the cited passages above from the Specification merely describe purported advantages of the claimed invention and support for steps recited in the claim, however, the cited passages do not make the claim any less abstract or change the previous analysis. See In re Mahapatra, 842 F. App'x 635, 638 (Fed. Cir. 2021) ("[T]he fact that an abstract idea may have beneficial uses does not mean that claims embodying the abstract idea are rendered patent eligible.")The Specification’s disclosure on machine learning is nothing more than a high general explanation of generic technology and applying it to the abstract idea. Referring to MPEP 2106.05(f), the retraining (updating) of the machine learning engines is merely being used to facilitate tasks of the abstract idea, which provides nothing more than a results-oriented solution and it equivalent to the words “apply it”.
In the instant case, the previous and present rejections is/are proper because they identified the limitations in the claim that recite an abstract idea in Step 2A Prong One and explained why the limitations fall within the certain methods of organizing human activity and mental processes groupings enumerated in MPEP 2106.04(a)(II). It is important to note for applicant to note that the scope of the disclosed invention, as presented in the originally filed Specification, is not directed towards improvements in machine learning engines or technology. Thus merely relying on features for retraining machine learning engines to a claim that recites an abstract idea does not lead towards eligibility. For these reasons, the rejections under 101 are being maintained.
Applicant further argues “Here, the claims integrate the alleged abstract idea into a practical application by the computer program receiving user feedback, determining positive, neutral, or negative sentiment score from feedback, retrieving information associated with a custom solution from a knowledge base, monitoring solution success on a user electronic device, generating a custom solution with one machine learning engine, identifying categories with a second machine learning engine, and retraining the custom solution machine learning engine based on the user feedback and solution effectiveness. The claim thus employs the information provided by the user so that the computer program can later determine to change any aspects of the user interface. Thus, these elements together recite a meaningful way of using the alleged judicial exception beyond generally linking the use of the judicial exception to a particular technological environment. Applicant further posits the amended claims amount to significantly more than any alleged abstract idea because the claims recite specific technical details and methods including two trained machine learning engines and a particular method and process for responding to issues within a network." The Examiner respectfully disagrees.
Contrary to the remarks, the claim(s) remain ineligible under Step 2A Prong Two of the analysis. In the instant case, the ordered combination of limitations recited by applicant above merely narrow or further specify how the abstract idea may be performed. Relying upon features of the abstract idea cannot integrate the abstract idea into a practical application or provide an inventive concept.
Now turning to the additional elements of “by the computer program”, “a knowledge base”, “a user electronic device”, “first machine learning engine”, “second machine learning engine”, “retraining the machine learning engine” as mentioned above these features are recited at a high-level of generality in light of the Specification [Fig. 3, ¶ 0025, 0028]. The Applicant’s own Specification evidences the additional elements are generic computing components operating in their ordinary or normal capacity to aid in performing the abstract idea. As such, when viewed as a whole there are no technological improvements to the functioning of computers or machine learning technology recited by the claim. See MPEP 2106.05(a). The user feedback is used to tailor subsequent solutions identified which may improve the abstract idea, however, these are not improvements to a computer or network as disputed by applicant. For these reasons, the rejections under 101 are being maintained.
With Respect to Rejections Under 35 USC 103
Applicant’s arguments with respect to claim(s) 1, 9, and 17 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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.
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/EHRIN L PRATT/Examiner, Art Unit 3629
/LYNDA JASMIN/Supervisory Patent Examiner, Art Unit 3629