DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
All previous objections and rejections directed to the Applicant’s disclosure and claims not discussed in this Office Action have been withdrawn by the Examiner.
Response to Amendments and Arguments
The Applicant’s arguments with respect to the claims have been considered but are not persuasive. The applicant asserts that US 20220366153, hereinafter referred to as LI, does not teach the following limitations from claim 10: “extracting string content from the selected existing slides; receiving a prompt input via a user interface element of the presentation application; combining the prompt input with the extracted string content to form a text query,” and “transmitting the text query as input to a trained large language model (LLM).” The Office Action asserts that LI teaches the concept of receiving the prompt input from the user, and combining of the prompt input with the extracted string content, in paragraph [0034] of Li. (Office Action, at 6.) However, the Applicant disagrees, stating that para [0034] of Li discusses analyses of documents and other items such as historical preferences and that there is no inclusion of a prompt input that is received via a user input in a presentation application. The examiner notes, though, that while para [0034] discusses analyses of documents and other items such as historical preferences, LI, para [0034], also describes inclusion of a prompt input that is received via a user input in a presentation application. Specifically, LI, para [0034], teaches receiving a prompt input via a user interface element of the presentation application: “FIG. 3 illustrates a method 300 for automatically generating script content using, for example, system 100. At step 305, an input document is received. For example, a user system design component user interface may be used to obtain the document selection from the user.” Here the “prompt” may be the document input by the user via the user interface as no further details of the nature of the prompt are specified in the claim language. And, LI, para [0034], also teaches combining the prompt input with the extracted string content to form a text query: “At step 310, an input design modelling component (e.g., input design modelling component 115 of FIG. 1) may generate prompts to use as input to one or more natural language generation models for generating candidate scripts at step 315.” Also, fig. 3(310) and “The input design modelling component 115 may be a design model that is used to parse the input document and generate the appropriate inputs,” LI, para [0021], refer to parsing the input document. Here the “extracted string content” is the parsed text from the document. Together the “input document” (i.e., prompt of LI) and the “extracted string content” (i.e., parsed text from document) form the “prompt” (i.e., text query of LI).
Additionally, the applicant argues that LI also fails to teach "receiving, from the LLM in response to the text query, text output," and "generating multiple prospective slides from the text output," as recited in claim 10. The applicant explains that LI does not teach the generation of new slides. Rather, Li generates a script for any type of content, which may be presentations. However, in the 103 rejection of claim 10, the examiner relies on reference Shahinian, to teach “generating multiple prospective slides from the text output,” - that is, generating a presentation (i.e., new slides) based on existing presentation material. Additionally, the examiner observes that Shahinian employes generative AI technology (i.e., an LLM) to compose new slides from the context provided by slides already added by a user (Shahinian, para [0099]). Therefore, the 103 rejection of claim 10 is maintained. By the same reasoning, the 103 rejection of claim 1 is maintained.
With respect to claim 16, the applicant argues that independent claim 16 is not equivalent to the recited elements of claim 1 and 6. In particular, the applicant notes part, “based on receiving the selection of the content generation option, displaying, by the presentation application, a content generation pane including a plurality of user interface elements for modifying a text query for a large language model (LLM),” and “based on the selected slides, generating a constraint including at least one of a text-length constraint or a slide-number constraint,” which differ from claim 1 and 6. The examiner observes that “One or more queries entered by a user via a graphical question and answer (Q&A) user interface initiates a text search of individual slides and processing of deep learning and generative AI technology to auto-generate answers based on extracted data,” Shahinian et al., para [0007], reads on the limitation “based on receiving the selection of the content generation option, displaying, by the presentation application, a content generation pane including a plurality of user interface elements for modifying a text query for a large language model (LLM)”. As for “based on the selected slides, generating a constraint including at least one of a text-length constraint or a slide-number constraint,” the examiner maintains that Li, col. 9, lines 41-46, reads on this limitation. This passage explains that (selected) slides may be split if an amount of text transgresses a threshold (i.e., a text-length constraint).
Regarding the 101 rejections, the applicant states that training of an LLM is clearly not a mental process that a human can do. Moreover, to the extent that there is any abstract idea involved, the claims have integrated any such abstract idea into a practical application. However, the examiner notes that a human mind encompasses an LLM, and a person could create a slide presentation by modifying existing slides. Therefore, the 101 rejections are maintained.
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 1-20 are rejected under 35 USC 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1 recites a method that, under the broadest reasonable interpretation, claims limitations that cover performance of the limitations in the human mind with the assistance of physical aids (e.g., pen and paper), but for the recitation of generic computer components on which is implemented an “LLM”. That is, nothing in these claim limitations precludes the steps from practically being performed in the mind. As a whole, claim 1 pertains to steps for training an LLM, which is a mental process that a human can do. Individually, each of the limitations also pertains to a mental process that a human can do. Individually, each of the limitations also pertains to a mental process, for example:
receive a selection of existing slides in a presentation document as a basis for context generation; (a person may physically select slides from a presentation document)
extract string content from the selected existing slides; (a person may, with pen and paper, extract text strings from the selected slides)
combine a prompt input with the extracted string content to form a text query; (a person may mentally or manually combine text to form a text query)
transmit the text query as input to a trained large language model (LLM); (a person may provide text input to a model)
receive, from the LLM in response to the text query, text output having at least one delimiter; (a person may compute the output of a model)
based on the text output and the at least one delimiter, generate multiple prospective slides from the text output; (a person could use the text output of a model and format it to slides)
cause a presentation of a graphical representation of the prospective slides; (a person may display the slides)
receive a selection of one or more of the prospective slides for inclusion in a slide presentation; (a person may select prospective slides) and
generate one or more new slides, for the presentation document, according to the selection of the one or more prospective slides (a person may create new slides based on the selected prospective slides).
The judicial exception is not integrated into a practical application. In particular, the claims only recite generic computing components. Such generic computing components are recited at a high-level of generality (i.e., as a generic processor performing a generic computer function of receiving, determining, or outputting information) such that they amount to no more than mere instructions to apply the exception using generic computer components. Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea.
Claim 1 does not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional limitations of using generic computer components amount to no more than mere instructions to apply the exception using generic computer components. Mere instructions to apply an exception using generic computer components cannot provide an inventive concept. Claim 1 is not patent eligible.
A similar analysis applies to the dependent claims. Thus, none of the claims are patent eligible.
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) 1-3, 6, 9-10, 12, 14, 16, and 18-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220366153, hereinafter referred to as LI et al., in view of US 11423207, hereinafter referred to as Li, and further in view of US 20240126981, support for which is provided by 63416159, hereinafter referred to as Shahinian et al.
Regarding claim 1 (Currently Amended), LI et al. discloses a system for generating slides from a large language model (LLM) by applying a natural language generation model (“Many of the components used to perform the actions in flowchart 200 include artificial intelligence such as neural networks, machine learning, AI modelling, and the like,” LI et al., para [0026]. And, “The inputs are fed into the natural language generation modelling component 125 at step 208, and each natural language generation model generates one or more candidate scripts, which are output at step 210,” LI et al., para [0029]. The candidate scripts are interpreted as slides.), comprising:
a processing system (LI et al., fig. 9(920)); and
memory storing instructions (LI et al., fig. 9(905)) that, when executed by the processing system (LI et al., fig. 9(920)), cause the system to:
receive a selection of existing slides in a presentation document as a basis for context generation (“FIG. 3 illustrates a method 300 for automatically generating script content using, for example, system 100. At step 305, an input document is received. For example, a user system design component user interface may be used to obtain the document selection from the user. In some embodiments, the content generation application 140 user interface may be used to obtain the input document from the user. The input document may be any document that includes information that can be used to generate a script/speech including, for example, a text-based document generated in a word processing application (e.g., MICROSOFT WORD®), a presentation slide deck generated by a presentation application (e.g., MICROSOFT POWERPOINT®), LI et al., para [0032]. And, “In some embodiments, a visual presentation may be used, for example, as the input document. For example, the input document may be a presentation slide deck,” LI et al., para [0034].);
extract string content from the selected existing slides (“The input design modelling component 115 may be a design model that is used to parse the input document and generate the appropriate inputs,” LI et al., [0021].);
combine a prompt input, received as a user input, with the extracted string content to form a text query (“At step 310, an input design modelling component (e.g., input design modelling component 115 of FIG. 1) may generate prompts to use as input to one or more natural language generation models for generating candidate scripts at step 315,” LI et al., para [0034].);
transmit the text query as input to a trained large language model (LLM) (“For example, the input design modelling component may use the input document along with known information about the user (e.g., historical preferences), input examples, and/or an input library to generate the inputs. The input design modelling component may be an AI based component that is trained to generate the best outputs for the given natural language generation models,” LI et al., para [0034]. See also, LI et al., fig. 3(315).);
receive, from the LLM in response to the text query, text output having at least one delimiter (“The inputs are fed into the natural language generation modelling component 125 at step 208, and each natural language generation model generates one or more candidate scripts, which are output at step 210…At step 212, the script selection and modification component 160 displays the scripts to the user for review, selection and/or modification. The user may iterate the generation of the candidate scripts by requesting changes in the user interface that sends the requests back to the text analysis service 204 for generating modified candidate scripts that can be further reviewed,” LI et al., para [0029]-[0030].);
based on the text output and the at least one delimiter, generate multiple prospective slides from the text output (“For example, a final presentation component may analyze the synthesized audio that was selected along with the visual document to synchronize the transition between the visual component (e.g., slides), and the audio component such that the corresponding visual portions are displayed during the appropriate time of the audio component,” LI et al., para [0040]. And, fig. 3(320) shows multiple presentation scripts.);
cause a presentation of a graphical representation of the prospective slides (LI et al, fig. 3(325).);
receive a selection of one or more of the prospective slides for inclusion in a slide presentation (LI et al, fig. 3(330).); and
generate one or more new slides, for the presentation document, according to the selection of the one or more prospective slides (LI et al., fig. 3(345).).
LI et al., though, does not disclose text output having at least one delimiter. Li is cited to disclose text output having at least one delimiter (Li, col. 9, lines 9-25. This passage notes that text is divided according to paragraphs, new line, etc.). Li benefits LI et al. by incorporating script formatting to the presentation output of LI et al., thereby providing the audience with a readable presentation. Therefore, it would be obvious for one skilled in the art to combine the teachings of LI et al. with those of Li to improve the final presentation of LI et al.
Neither LI et al. nor Li teach that the machine learning model is an LLM. Shahinian et al. is cited to disclose that the machine learning model is an LLM (“The slide auto-creation module 1154 assembles a slide based on questions posed by a user via the user responsive module and answers found through a search of processed content. The slide auto-creation module 1154 uses an ensemble of deep learning algorithms to auto-generate a new slide based on extracted data relevant to the question posed by a user. The slide auto-creation module 1154 also composes a new slide from the context provided by slides already added by a user by applying natural language processing and generative AI technology,” Shahinian et al., para [0099]. The examiner notes that the generative AI model is an LLM.) Shahinian et al. recites using a generative AI model (i.e., an LLM) to produce a slide presentation. An LLM is a well-known type of machine learning model trained on large amounts of text to auto-generate results, such as a slide presentation, based on extracted data. Therefore, it would be obvious to one skilled in the art to substitute the machine learning model of LI et al. with the LLM of Shahinian et al., and the results of the substitution to have been predictable.
As to claim 10, method claim 10 and system claim 1 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 10 is similarly rejected under the same rationale as applied above with respect to system claim. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
Regarding claim 2 (Original), LI et al., as modified by Li and Shahinian et al, discloses the system of claim 1, wherein in combining the prompt input with the extracted string content, the instructions cause the system to generate a context object including the extracted string content prepended by a context statement string (“The artificial intelligence assistant computing facility comprises logic to perform an ensemble of deep learning models to auto-generate a new presentation. Each slide in the suggested slides for inclusion in an auto-generated presentation deck is contextual to the user's actions and relation/similarity context. The artificial intelligence assistant computing facility includes logic to display text and image recommendations to tailor the slide based on an ensemble of machine learning models. The artificial intelligence assistant computing facility includes logic adapted to auto-create new slides from the context provided by slides already added to the presentation by applying machine learning and generative AI technology ,” Shahinian et al., para [0013]. Here, the extracted text of the slide may be altered according to contextual information provided by the user.).
Regarding claim 3 (Original), LI et al., as modified by Li and Shahinian et al., discloses the system of claim 1, wherein in combining the prompt input with the extracted string content, the instructions cause the system to prepend the prompt input with an instruction string that indicates an action to be taken (Shahinian et al., para [0013]. This excerpt shows that the user’s actions provide instructions to tailor the slide generation.).
Regarding claim 6 (Original), LI et al., as modified by Li and Shahinian et al., discloses the system of claim 1, wherein the instructions further cause the system to receive a constraint selection corresponding to a slide count constraint, where the slide count is included in the text query (“With all the candidates, user input segments (e.g., the segments from the text document), user preferences, other features, and constraints (e.g., number of slides to be created, number of text boxes in one slide) a candidate ranker 416 ranks the candidates to determine the best candidates for use in segmentation of the overloaded text document,” Li, col. 9, lines 41-46.).
As to claim 12, method claim 12 and system claim 6 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 12 is similarly rejected under the same rationale as applied above with respect to system claim. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
As to claim 16, method claim 16 and system claim 1+6 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 16 is similarly rejected under the same rationale as applied above with respect to system claims. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
Regarding claim 9 (Original), LI et al., as modified by Li and Shahinian et al., discloses the system of claim 1, wherein the instructions further cause the system to:
receive a selection of one or more of the prospective slides for expansion (“The user 1810 can accept or further modify the slide content. The user 1810 can also request the system to auto-generate new slides based on the context provided by slides already added to the Your Presentation edit palette,” Shahinian et al., para [0135]. The request to auto-generate new slides is interpreted as a request for slide expansion.);
combine an expansion instruction with string content included in the one or more selected prospective slides to form a second text query (“One or more queries entered by a user via a graphical question and answer (Q&A) user interface initiates a text search of individual slides and processing of deep learning and generative AI technology to auto-generate answers based on extracted data,” Shahinian et al., para [0007].);
transmit the second text query as input to the LLM (“Generative AI technology is employed to interpret and process the intent of the user,” Shahinian et al., para [0104].);
receive, from the LLM in response to the second text query, second text output having at least one delimiter (Li, col. 9, lines 9-25. This passage notes that text is divided according to paragraphs, new line, etc.);
based on the second text output and the at least one delimiter, generate additional prospective slides from the text output (“The artificial intelligence assistant computing facility includes logic adapted to auto-create new slides from the context provided by slides already added to the presentation by applying machine learning and generative AI technology,” Shahinian et al., para 0013].); and
present a graphical representation of the prospective slides (LI et al, fig. 3(325).).
As to claim 14, method claim 14 and system claim 9+6 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 14 is similarly rejected under the same rationale as applied above with respect to system claims. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
Regarding claim 17 (Currently Amended), LI et al., as modified by Li and Shahinian et al, discloses the method of claim 16, wherein the text-length constraint is automatically generated based on text lengths in the selected existing slides (“In operation 606, the constraint module 208 obtains design constraints or targets for the transition templates. For example, design constraints can include a number of boxes; minimum or maximum size of each box; text size and length, duration of display, and/or word range in each box on a template ,” Li, col. 11, lines 46-51.).
Regarding claim 18 (Original), LI et al., as modified by Li and Shahinian et al., discloses the method of claim 16, wherein the slide-number constraint is generated based on user input received in one of the user interface elements of the content generation pane (Shahinian et al., col. 9, lines 41-46.).
Regarding claim 19 (Original), LI et al., as modified by Li and Shahinian et al., discloses the method of claim 16, wherein the output from the LLM includes both text output and an image (Li, figs. 8-9 show examples with both text and image output.).
Regarding claim 20 (Original), LI et al., as modified by Li and Shahinian et al., discloses the method of claim 16, further comprising:
receiving a selection to split one of the prospective slides (“A candidate access module 414 accesses the candidates from a data store. The candidates can include the candidates generated by the offline document system 300 and/or 400. In various embodiments, the candidates will each fulfill different sets of constraints. For example, some candidates are only for splitting into two slides, while others are for three slides. In another example, some candidates are only good for under a predetermined number of words (e.g., 20 words), while other are good for more numbers of words,” Li, p. 9, lines 32-40.); and
splitting the selected prospective slide into two prospective slides (Li, p. 9, lines 32-40.).
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220366153, hereinafter referred to as LI et al., in view of US 11423207, hereinafter referred to as Li, and further in view of US 20240126981, support for which is provided by 63416159, hereinafter referred to as Shahinian et al., and further in view of US 20240111960, hereinafter referred to as Earle et al.
Regarding claim 4 (Original), LI et al., as modified by Li and Shahinian et al., discloses the system of claim 1, but not wherein the instructions further cause the system to receive a new-content option selection corresponding to a hyperparameter value of the LLM, wherein the new-content option selection is included in the text query. Earle et al. is cited to disclose wherein the instructions further cause the system to receive a new-content option selection corresponding to a hyperparameter value of the LLM, wherein the new-content option selection is included in the text query (“Large language models (LLMs) are trained using an abundance of training data such that billions of hyperparameters that define the LLM are tuned. In operation, LLMs are trained using any text on the internet as training data. Training on such large training data allows the LLM to identify tone and style in a received input text and mimic such tone and style in a response. The LLM learns how to extract meaningful features (e.g., underlying patterns, characteristics, processes, etc.) of human language. As such, LLMs are able to mimic human language by generating responses that are coherent and contextualized. These models are well suited to form conversations (e.g., taking turns asking questions and providing responses) by predicting words (or sequences of words) that are tailored to the style and context of the conversation. LLMs can also be trained to extract information (or otherwise summarize) received text inputs. Such summary (or paraphrase) of the received text input is coherent with respect to the input and contextualized with respect to style and tone,” Earle et al., para [0016]. Thus, the hyperparameters are updated with new text query content.). Earle et al. benefits LI et al. by adjusting the hyperparameters of an LLM to improve the results of the LLM (Earle et al., para [0074]). Therefore, it would be obvious to one skilled in the art to combine the teachings of LI et al. with those of Earle et al. to improve the final presentation of LI et al.
Claim(s) 5 and 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220366153, hereinafter referred to as LI et al., in view of US 11423207, hereinafter referred to as Li, and further in view of US 20240126981, support for which is provided by 63416159, hereinafter referred to as Shahinian et al., and further in view of US 20230153460, hereinafter referred to as Song et al.
Regarding claim 5 (Currently Amended), LI et al., as modified by Li and Shahinian et al., discloses the system of claim [[1]]4, but not wherein the hyperparameter value is for a temperature property. Song et al. is cited to disclose wherein hyperparameter value is for a temperature property (Song et al., para [0067].). Song et al. benefits LI et al. by assigning a temperature hyperparameter to control the randomness of the model’s output. Therefore, it would be obvious to one skilled in the art to combine the teachings of LI et al. with those of Song et al. to improve the model’s final output of LI et al.
As to claim 11, method claims 4+5 and system claim 11 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 11 is similarly rejected under the same rationale as applied above with respect to system claims. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
Claim(s) 7-8 and 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 20220366153, hereinafter referred to as LI et al., in view of US 11423207, hereinafter referred to as Li, and further in view of US 20240126981, support for which is provided by 63416159, hereinafter referred to as Shahinian et al., and further in view of US 11769017, hereinafter referred to as Gray et al.
Regarding claim 7 (Original), LI et al., as modified by Li and Shahinian et al., discloses the system of claim 1, wherein the instructions further cause the system to:
extract image content from the selected existing slides (“In such an example, the system can perform ASR to convert the voice input to text form, can perform image processing on the image to recognize an avocado is present in the image, and can perform co-reference resolution to replace “this” with “an avocado”, resulting in a textual format query of “is an avocado healthy”, Gray et al., para [0031].);
generate string content representative of the image content (“In such an example, the system can perform ASR to convert the voice input to text form, can perform image processing on the image to recognize an avocado is present in the image, and can perform co-reference resolution to replace “this” with “an avocado”, resulting in a textual format query of “is an avocado healthy”,” Gray et al., para [0031].); and
include the generated string content in the text query (“As a working example, assume the candidate generative models include: an informational LLM for generating informational summaries for a query; a creative LLM for generating creative poems, essays, or other prose based on a query; and a text-to-image diffusion model for creating a synthetic image based on a query,” Gray et al., para [0097].). Gray et al. benefits LI et al. by allowing textual description to be generated corresponding to extracted images, thereby enhancing auto-creation of presentations. Therefore, it would be obvious to one skilled in the art to combine the teachings of LI et al. with those of Song et al. to improve the final presentation of LI et al.
As to claim 13, method claim 13 and system claim 7 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 13 is similarly rejected under the same rationale as applied above with respect to system claim. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
Regarding claim 8 (Original), LI et al., as modified by Li and Shahinian et al., discloses the system of claim 1, wherein the instructions further cause the system to:
insert the image in one or more of the multiple prospective slides (Shahinianet al., para [0013].).
However, none of the aforementioned prior art disclose generating a second text query including at least a portion of the text output (Gray et al., para [0031].);
transmitting the second text query as input to an image generation machine learning (ML) model (Gray et al., para [0031].).
Gray et al. is cited to disclose generating a second text query including at least a portion of the text output (Gray et al., para [0031].);
transmitting the second text query as input to an image generation machine learning (ML) model (Gray et al., para [0031].); and
receive an image as output from the image generation ML model in response to the second text query (Gray et al., para [0097].). Gray et al. benefits LI et al. by allowing textual description to be generated corresponding to extracted images, thereby enhancing auto-creation of presentations. Therefore, it would be obvious to one skilled in the art to combine the teachings of LI et al. with those of Song et al. to improve the final presentation of LI et al.
As to claim 15, method claim 15 and system claim 8+6 are related as system and method of using same, with each claimed element’s function corresponding to the system step. Accordingly claim 15 is similarly rejected under the same rationale as applied above with respect to system claims. Also, LI et al., fig. 9, teaches processor, memory, CRM, and instructions.
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 extension fee 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 date of this final action.
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/ANNE L THOMAS-HOMESCU/Primary Examiner, Art Unit 2656