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 .
Response to Amendments
This office action responds to the amendments filed on April 24, 2026 for application 18/231,710. Claims 1, 10, 15-16, and 18 are amended, and claims 1-20 remain pending in the application.
Response to Arguments
The Examiner has fully considered the Applicant’s arguments, and the Examiner responds as provided below.
First, the Applicant seemingly failed to address the objection to the drawings. See Office Action, June 9, 2025, at 2-4. The objection is thus maintained as detailed below.
Regarding the Applicant’s response at page 14 of the Remarks that concerns the objection to claim 10, the amendment to claim 10 cures the issue and the objection is withdrawn.
Regarding the Applicant’s response at pages 14-18 of the Remarks that concerns the § 112(b) rejection of claims 18 and 19, the amendments to claim 18 resolve the issue of indefiniteness and the § 112(b) rejection is withdrawn.
Regarding the Applicant’s response at pages 18 and 19 of the Remarks that concerns the § 112(b) rejection of claims 15 and 16, the amendments to the claims resolve the issue of indefiniteness and the § 112(b) rejection is withdrawn.
Regarding the Applicant’s response at pages 14-18 of the Remarks that concerns the § 112(b) rejection of independent claim 1 (and thereby claims 2-17 that depend upon rejected claim 1), the Applicant’s arguments are moot in view of the amendment. In wording the § 112(b) rejection of claim 1, the Examiner stated “the claim limitation of ‘large language model’ invokes 35 U.S.C. 112(f)….” Office Action, June, 9, 2025, at 7. More accurately, the Examiner should have stated “the claim limitation of ‘large language model module’ invokes 35 U.S.C. 112(f)…,” but the comma between “large language model” and “module,” which has since been removed by amendment, introduced an element of ambiguity. Claim 1 now accurately recites a “large language model module,” and this limitation is indefinite under § 112(b) as detailed below. Although Applicant expends significant effort arguing that a large language model is known to those skilled in the art, see Reply at 16-18, the claim limitation that is now indefinite under § 112(b) is “large language model module.” The Applicant further argues that “[t]he structure of a module is well known.” Reply at 17. To the contrary, the use of “module” as a claim limitation typically signals the presence of a means-plus-function limitation under U.S. patent law. Thus, the § 112(b) rejection is maintained as presented below.
Drawings
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they include the following reference character(s) not mentioned in the description:
165 and 175 in Fig. 1;
101A and 101B in Fig. 6; and
Each number in Fig. 15.
Corrected drawing sheets in compliance with 37 CFR 1.121(d), or amendment to the specification to add the reference character(s) in the description in compliance with 37 CFR 1.121(b) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(4) because reference character “100” has been used to designate both “Cyber security appliance” (Fig. 1) and “Detection” (Fig. 5A) (Further noting “Restoration” in Fig. 4 should seemingly be 190 and “Detection” should have some number).
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
The drawings are objected to as failing to comply with 37 CFR 1.84(p)(5) because they do not include the following reference sign(s) mentioned in the description:
Each of the 600 series numbers in ¶¶ [0222]-[0227].
Corrected drawing sheets in compliance with 37 CFR 1.121(d) are required in reply to the Office action to avoid abandonment of the application. Any amended replacement drawing sheet should include all of the figures appearing on the immediate prior version of the sheet, even if only one figure is being amended. Each drawing sheet submitted after the filing date of an application must be labeled in the top margin as either “Replacement Sheet” or “New Sheet” pursuant to 37 CFR 1.121(d). If the changes are not accepted by the examiner, the applicant will be notified and informed of any required corrective action in the next Office action. The objection to the drawings will not be held in abeyance.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitations are: “large language model module” in claim 1.
Because this claim limitation is being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it is being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
Claim Rejections - 35 USC § 112
I. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1 and 18 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention. Claims 1 and 18 introduce via amendment the following limitation: “where the LLM has been trained to identify what function each of the one or more components performs and how to convert the response to the query from the one or more components of the cyber security system into a form that is understandable to the user.” The Applicant failed to provide a citation to the specification that provides support for this amendment, and the Examiner’s text search failed to readily identify the portion of the specification that provides support. Accordingly, the amendment seemingly comprises new matter until Applicant provides evidence that the amendment is not new matter (noting that quoting the specification verbatim is the easiest means to avoid the issue of new matter).
II. The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
A. Independent claims 1 and 18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. Claims 1 and 18 recite, “what function…,” and the claims are indefinite because “what” is not an acceptable means to establish an antecedent basis. Claims 1 and 18 further recite “a form that is understandable to the user,” and this limitation is indefinite because users vary and what is understandable to one may not be understandable to another (noting that employing “natural language output” can remedy this issue).
B. Claims 1-17 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention. (The Examiner notes that dependent claims 2-17 are rejected because they depend upon independent claim 1.)
Most notably, the claim limitation “large language model module” invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Indeed, the written description describes a “large language model module” without the use of a reference numeral that corresponds to some feature as presented in the figures. Given the amorphous description of “large language model module” in the written description and figures, the claims are indefinite and are rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
Most applicants merely recited a processor, non-transitory memory, and computer instructions that when performed by the processor perform the recited functionality of the means-plus-function limitations. This negates the need to recite any type of module that is susceptible to being rejected under § 112(b).
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
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.
The following conventions apply to the mapping of the prior art to the claims:
Italicized text – claim language.
Parenthetical plain text – Examiner’s citation and explanation.
Citation without an explanation – an explanation has been previously provided for the respective limitation(s).
Quotation marks – language quoted from a prior art reference.
Underlining – language quoted from a claim.
Brackets – material altered from either a prior art reference or a claim, which includes the Examiner’s explanation that relates a claim limitation to the quoted material of a reference.
Braces – a limitation taught by another reference, but the limitation is presented with the mapping of the instant reference for context.
Numbered superscript – a first phrase to be moved upwards to the primary reference analysis.
Lettered superscript – a second phrase to be moved after the movement of the first phrase from which it was lifted, or more succinctly, move numbered material first, lettered material last.
A. Claims 1-6, 11, 16, 18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Lee et al. (US 2023/0315856, “Lee”) in view of Hagen et al. (US 11,882,148, “Hagen”).
Regarding Claim 1
Lee discloses
An interactive cyber security user interface (¶ [0025], “The set of system commands is configured to perform one or more computational tasks associated with a cybersecurity management system operating within the identified context. The processor is configured to receive a sample natural language phrase via an [user] interface. The sample natural language phrase is associated with a user [interactive] request for performing an identified computational task.”) comprising a large language model (LLM) module (¶¶ [0040]-[0041], “The NL analysis device 101 can be configured [and implement a module] to receive training data from the data source 102 and to define, train, and/or prime one or more ML models to auto-complete and/or auto-correct a user's query in a natural language regarding completion of a task via a management system…”; and “The data source 102 can be any suitable source of data providing information associated with a large body of text in any suitable natural language [language model] (e.g., English, Spanish, French, German, etc.) used for human communication.”; see also the title of US 2023/0315856, “Methods and apparatus for augmenting training data using large language models”) configured to:
receive a natural language input from a user (¶ [0078], “In use, the NL analysis device 201 can be used according to the method 400 shown in FIG. 4. At 471, a user approaches the NL interface (e.g., NL interface 550, 650, 950, 1050) implemented by the processor 210, included in the NL analysis device 100. At 472, the user begins typing a natural language query [input] into a text space [for receiving] (e.g., space 572 in NL interface 550 or space 672 in NL interface 650).”;’ and Fig. 11, ¶ [0102], “The method 1100 includes, at 1171, receiving, via an interface, a natural language request for performing an identified task in a management system.”);
analyze the natural language input to determine contextual information from the natural language input (Fig. 11, ¶ [0103], “At 1172, the method 1100 includes extracting, based on the identified [via an analysis] context, a set of features from the natural language [input] request.”);
1 …; and
generate a query in a software code format accepted by the one or more components of the cyber security system (¶ [0095], “For example, as shown in FIG. 5 , the finalized query “HTTP+Windows 10+Ordered by bandwidth+search” can be converted to [generate] a complex SQL query [software code format] or invoke identified functions to execute specific routines or protocols in the cybersecurity management system…”) based on an analysis of the natural language input, the contextual information (Fig. 11, ¶ [0103]),
and {the determined one or more components to be queried (Lee Col. 5:25-34, 5:44-55)};
2 …; and
3 …into a form that is understandable to the user (¶ [0070], “The reference NL phrase [in a form] can be a finalized query that is corrected and approved [and thus understandable] by a user (e.g., after auto-completion, auto-correction, intent inference).”);
where any instructions for the interactive cyber security user interface that includes the large language model module are stored in an executable format on one or more non-transitory computer readable mediums, which are executable by one or more processors (¶ [0126], “Some embodiments described herein relate to a computer storage product with a non-transitory computer-readable medium (also can be referred to as a non-transitory processor-readable medium) having instructions or computer code thereon for performing various computer-implemented operations.”; and ¶ [0128], “Some embodiments and/or methods described herein can be performed by software (executed on hardware), hardware, or a combination thereof. Hardware modules may include, for example, a general-purpose processor…”).
Lee doesn’t disclose
1 determine one or more components of a cyber security system to query based on the contextual information and the natural language input;
2 wherein the interactive cyber security user interface is configured to query the determined one or more components of the cyber security system using the generated query and receive a response to the query from the one or more components of the cyber security system;
3 … where the LLM has been trained to identify what function each of the one or more components performs and how to convert the response to the query from the one or more components of the cyber security system;
Hagen, however, discloses
1 determine one or more components of a cyber security system to query based on the contextual information and the natural language input (Col. 5:25-34, “A pronounceable node may be resolved to a corresponding command node by querying the database [one cyber security component] 163. In one embodiment, queries performed on the database 163 are in accordance with the SPARQL RDF Query Language.”; and Col. 5:44-55, “For example, the “DashboardCreate” command of the command node 206 implements [based on the contextual information and natural language input] the actions of: (a) fetching data for the last X months as specified in the action node 209 (see edge 222); (b) filtering data as specified in the action node 210 (see edge 223); and (c) displaying the report as specified in the action node 211 (see edge 229). A command node may be resolved [and thereby determining to query the component/“database”] to corresponding action nodes by querying the database 163.”);
2 wherein the {interactive cyber security user interface (Lee ¶ [0025])} is configured to query the determined one or more components of the cyber security system using the generated query (Col. 5:25-34, 5:44-55) and receive a response to the query from the one or more components of the cyber security system (Col. 5:44-55, “For example, the “DashboardCreate” command of the command node 206 implements the actions of: (a) fetching data for the last X months as specified in the action node 209 (see edge 222) [as one response]; (b) filtering data as specified in the action node 210 (see edge 223) [as one response]; and (c) displaying the report as specified in the action node 211 (see edge 229). A command node may be resolved to corresponding action nodes by querying the database [component] 163.”);
3 … where the LLM has been trained to identify what function each of the one or more components performs (Col. 6:49-7:4, “In the example of FIG. 3 , the NLP system 322 is trained to identify, from a command utterance in a natural language, a closest matching pronounceable node [cybersecurity component] in the database 163 (see arrow 303).”; and “For example, the resolver 323 may query the database 163 to find one or more action [with a function] nodes [cybersecurity component] that are connected to the command node by an ‘implements’ predicate.”) and how to convert the response to the query from the one or more components of the cyber security system (Col. 6:49-7:4, “The resolver 323 is further configured to resolve [identifies how] each of the action nodes [is converted] to one or more corresponding parameter nodes that are included in the database 163 (see arrow 309). For example, the resolver 323 may query the database 163 to find [identify] one or more parameter nodes that are connected to the action node by a “requires” predicate.”);
Regarding the combination of Lee and Hagen, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee, upon which the claimed invention can be seen as an “improvement” through the use of a database-query feature;
2) the prior art contained a “comparable” system, namely the security system of Hagen, that has been improved in the same way as the claimed invention through the database-query feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the database-query feature to the base security system of Lee, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 2
Lee in view of Hagen (“Lee-Hagen”) discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) is further configured to collate and/or summarize information received in the response from the one or more components of the cyber security system (¶ [0057], “For example, the NL interface 550 can be used to display summary information that can be obtained as results from executing system commands by providing natural language requests [to one component of the cyber security system that responds with the “summary information”].”; and “Each of these summaries, reports, and/or the information used to generate the summaries/reports can be populated using natural language phrases via the NL interface 550 by a user input of a natural language request at the editable text space 572.”).
Regarding Claim 3
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) is further configured to convert the response to the query received from the cyber security appliance into a natural language response and output the natural language response to the user (¶ [0059], “The NL interface manager 211 can also provide a query builder interface [output the natural language response to the user] 674 presenting a template query that was generated by one or more ML models 212. The template query is presented in a parameterized form including fillable text sections 681 corresponding to parameters 682, 684, 686, and 688 generated based on a natural language query provided by a user. Each parameter can further include other control tools such as, for example, drop down menu items 683 and/or a selectable/activatable button 685 that invokes execution of a finalized query generated using the query builder 674.”).
Regarding Claim 4
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the natural language input is received as part of a multistage communication (¶ [0067], “In some implementations, the ML models [as part of a multistage communication] 212 can include a first ML model configured to receive natural language data in the form of a natural language [input] query and evaluate if the first ML model can auto-complete based on training data indicating frequently used natural language phrases.”); and
wherein the LLM module (¶¶ [0040]-[0041]) is further configured to determine contextual information from previously received natural language inputs and associated responses within the multistage communication (¶ [0067], “The first ML model [of previously received natural language inputs and associated responses] 212 can be trained [using contextual information] to identify an incomplete natural language phrase and based on the incomplete natural language phrase, predict the next characters and/or words as the user types the natural language phrase. The first ML model can be configured to provide a list of options that can be potentially used to complete the natural language query with each options in the list of options having an identified likelihood of being the correct choice.”).
Regarding Claim 5
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the interactive cybersecurity user interface (¶ [0025]) is configured to enable the cyber security system to initiate an interaction by requesting the natural language input from the user to obtain additional contextual information related to the natural language input (¶ [0059], “The NL interface manager 211 can also provide a query builder interface [to obtain additional contextual information related to the natural language input] 674 presenting a template query that was generated by one or more ML models 212. The template query is presented in a parameterized form including fillable text sections [that initiates an interaction by requesting the natural language input from the user] 681 corresponding to parameters 682, 684, 686, and 688 generated based on a natural language query provided by a user. Each parameter can further include other control tools such as, for example, drop down menu items 683 and/or a selectable/activatable button 685 that invokes execution of a finalized query generated using the query builder 674.”).
Regarding Claim 6
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the interactive cyber security user interface (¶ [0025]) is configured to enable the cyber security system to initiate an interaction by requesting the natural language input from the user based on information associated with a user (¶ [0042], “The compute devices 103-105 can include a user device configured to connect to the [enabled] NL analysis device [to initiate an interaction with the user] 101 and/or the data source 102, as desired by an authorized or authenticated [thereby associated with a] user. In some implementations, the compute device 105 can be configured to implement and/or present the interfaces [for requesting the natural language input] described herein, for example the interface 550, 650, 950, or 1050.”) or internet link identified by an end user, or based on information associated with unusual behavior on an endpoint computing device identified by the cyber security appliance.
Regarding Claim 11
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) has a query builder module configured to determine one or more {components (Hagen Col. 5:25-34)} of the cyber security system to query from a group of components comprising one or more of a cyber security restoration engine, a prediction engine, an autonomous response engine, and a cyber threat detection engine (¶ [0048], “For example, the data 226 can include data [associated with the query] associated with English language associated with the context of cybersecurity and malware threat detection [via the use of an engine]/threat mitigation using a cybersecurity management system.”), and
to communicate with the determined one or more components via one or more APIs…1 (¶ [0098], “In some implementations, the methods, processes, or functions carried out by the NL analysis device and its components described herein can be implemented via an application programming interface (API) that is configured to support execution of the methods, processes, or functions, for example, on a backend server.”)
Hagen further discloses
1 …to obtain data from the {component being queried (Lee ¶ [0048])} and/or to generate the query, and subsequently to generate the response to the query and/or command (Col. 5:44-55, “For example, the “DashboardCreate” command of the command node 206 implements the actions of: (a) fetching data for the last X months as specified in the action node 209 (see edge 222) [as one response]; (b) filtering data as specified in the action node 210 (see edge 223) [as one response]; and (c) displaying the report as specified in the action node 211 (see edge 229). A command node may be resolved to corresponding action nodes by querying the database [component] 163.”).
Regarding the combination of Lee and Hagen, the rationale to combine is the same as provided for claim 1 due to the overlapping subject matter of claims 1 and 11.
Regarding Claim 16
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) comprises an LLM (¶¶ [0040]-[0041]) that is fine tuned using historical queries input by a user to a cybersecurity appliance and responses provided by human analysist (¶ [0066], “Each ML model included in the ML models 212 can be any suitable model including supervised, semi-supervised [with responses provided by human analysts], unsupervised, self-supervised, multi-instance, and/or reinforcement models. Each of the ML models 212 can be a language model trained to receive natural language data and interpret the natural language data based on user needs and context. The ML models 212 can be implemented using any suitable method, process, or framework.”; and ¶ [0073], “The data manager 213 can be configured to receive the output of the ML models 212 (e.g., outputs associated with auto-completion, auto-correction, and/or intent inference) and compare the outputs against user made corrections or selections. The data manager 213 can be configured to assess the amount, degree and/or frequency of user selections or corrections and, based on the assessment, evaluate one or more ML models and their current performance. The data manager 213 can be configured to keep a record of performance of ML models 212 and can be used to compare a current performance of an ML model against a past performance or a predetermined standard (e.g., user provided threshold of desired performance). The data manager 213 can be configured to use the evaluation to decide if one or more ML models 212 need ret[r]aining [fine tuning] and if a particular type or kind of supplemental training data can be used to obtain targeted improvements in performance of the one or more ML models.”; and ¶ [0075], “The NL analysis device then parses the complete natural language query and returns [based on historical queries] a template query. The user makes suitable corrections to the template query to generate a finalized query. The NL analysis device receives the finalized query and generates a system command based on the finalized query such that the tasks may be performed to return results. The user can view the results and adjust the natural language phrase, option selections, and/or the corrections to the template query to generate an updated finalized query, any number of times, that can be run to obtain better results.”).
Regarding Independent Claim 18
With respect to independent claim 18, a corresponding reasoning as given earlier for independent claim 1 and dependent claim 11 applies, mutatis mutandis, to the subject matter of claim 18. The Examiner notes that the limitations involving a non-transitory medium, processor, and computer instructions are taught by Lee at ¶ [0145]. Therefore, claim 18 is rejected, for similar reasons, under the grounds set forth for claims 1 and 11.
Regarding Independent Claim 20
With respect to independent claim 20, a corresponding reasoning as given earlier for independent claim 1 applies, mutatis mutandis, to the subject matter of claim 20. Therefore, claim 20 is rejected, for similar reasons, under the grounds set forth for claim 1.
B. Claims 7 and 8 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hagen, and further in view of Lange (US 2023/0259714, “Lange”).
Regarding Claim 7
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee-Hagen doesn’t disclose
1….
Lange, however, discloses
1 further comprising a speech-to-text module configured to receive a speech input from the user, convert the voice input into the natural language input in a text format, and provide the natural language input to the LLM module (¶ [0052], “The LM can be a large language model, for example initially trained and then fine-tuned or retrained with session log data, as described herein with reference to FIG. 4.”; and ¶ [0081], “In other examples, the LM 120 can perform a speech-to-text operation to convert [received] audio [speech input] into text [the previously disclosed natural language input], before processing the text.”).
Regarding the combination of Lee-Hagen and Lange, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee-Hagen, upon which the claimed invention can be seen as an “improvement” through the use of a speech-to-text feature;
2) the prior art contained a “comparable” system, namely the security system of Lange, that has been improved in the same way as the claimed invention through the speech-to-text feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the speech-to-text feature to the base security system of Lee-Hagen, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 8
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
further comprising a text-to-speech module configured to convert the {received response from the cyber security system (Hagen Col. 5:44-55)} from a text format into speech for output to the user (¶ [0055], “The state handler 115 generates text or speech that will be sent to the user through the user frontend. The state handler 115 sends predetermined text or speech depending on the API call received from the LM 120. The current state of the conversation may determine what API calls can be invoked on the state handler 115. The current state of the conversation is based on which node of the conversation graph the state handler 115 is currently on, which is associated with a number of predetermined actions that the state handler 115 can perform.”).
Regarding the combination of Lee-Hagen and Lange, the rationale to combine is the same as provided for claim 7 due to the overlapping subject matter of claims 7 and 8.
C. Claims 9 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hagen, and further in view of Sloane et al. (US 2019/0114369 “Sloane”).
Regarding Claim 9
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
further comprising a user authentication module for determining an authorization of the user providing the natural language input (¶ [0042], “The compute devices 103-105 can include a user device configured to connect to the NL analysis device [for the provided natural language input] 101 and/or the data source 102, as desired by an authorized or authenticated user.”); and
1 ….
Lee-Hagen doesn’t disclose
1 wherein determining one or more components of the cyber security system to query is further based on the authorization of the user.
Sloane, however, discloses
1 wherein determining one or more components of the cyber security system to query is further based on the authorization of the user (¶ [0035], “In some embodiments, the user may be an individual, such as a customer of the entity, an administrator [authorized user] or employee of the entity, or a member of the public.”; and “The system may further authorize the user to construct and execute search queries on the graph database [component] according to a query language that supports the retrieval of the multidimensional properties and relationships of the multidimensional graph database.”).
Regarding the combination of Lee-Hagen and Sloane, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee-Hagen, upon which the claimed invention can be seen as an “improvement” through the use of an authorization feature;
2) the prior art contained a “comparable” system, namely the security system of Sloane, that has been improved in the same way as the claimed invention through the authorization feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the authentication feature to the base security system of Lee-Hagen, and the results would have been predictable to one of ordinary skill in the art.
D. Claims 10 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hagen and Sloan, and further in view of Reiser et al. (US 2019/0190921 “Reiser”).
Regarding Claim 10
Lee in view of Hagen, and further in view of Sloane (“Lee-Hagen-Sloane”) discloses the interactive cyber security user interface of claim 1, and Hagen further discloses
further comprising:
1 …provided in the {received response from the cyber security system (Col. 5:44-55) based on {the authorization of the user (Sloane ¶ [0035])}.
Lee-Hagen-Sloane doesn’t disclose
1 a data sanitizing module for sanitizing data…
Reiser, however, discloses
1 a data sanitizing module for sanitizing data… (¶ [0050], “Further, the analytics node may be configured to vet or sanitize sensitive information from any generated results before transmitting the results to a user that initiated the analytic request.”)
Regarding the combination of Lee-Hagen-Sloane and Reiser, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee-Hagen-Sloane, upon which the claimed invention can be seen as an “improvement” through the use of a data sanitizing feature;
2) the prior art contained a “comparable” system, namely the data system of Reiser, that has been improved in the same way as the claimed invention through the data sanitizing feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the data sanitizing feature to the base security system of Lee-Hagen-Sloane, and the results would have been predictable to one of ordinary skill in the art.
E. Claims 12-14 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hagen, and further in view of Heinemeyer et al. (US 2021/0194924 “Heinemeyer”).
Regarding Claim 12
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) is configured to…1
Lee-Hagen doesn’t disclose
1 …determine one or more components of the cyber security system to query from a cyber security appliance within the cyber security system, the cyber security appliance being associated with an organization to which the user belongs.
Heinemeyer, however, discloses
1 …determine one or more components of the cyber security system to query from a cyber security appliance within the cyber security system (¶ [0120], “In other examples, one or more modules may be configured to search and query: generally all of, but at least two or more of i) data stores (e.g., public OS data, ingested data observed by any cyber security appliances, and so on), ii) other modules, and iii) one or more AI models and classifiers making up such AI red team simulator used to pentest and then train and identify any vulnerabilities of the actual network under analysis from any actual cyber threats, based on what those searched and queried data stores, other modules/appliances/probes, etc., and AI models already know about that network and those entities under analysis to generate the simulated graph.”),
the cyber security appliance being associated with an organization to which the user belongs (¶ [0024], “For example, as used herein, the AI cyber-threat defense system may implement the AI adversary red team as an attack module as well as a training module depending on the desired goals of the respective organization [possessing users] (e.g., as shown with the AI adversary red team 105 in FIG. 1), while a cyber security appliance may be implemented as a cyber threat detection and protection module (e.g., as shown with the cyber security appliance 120 in FIG. 1).”; and ¶ [0057], “In some embodiments, the collections module may be configured to cooperate with the communication module as well as the analyzer module to gather external data from the OS database server 122 (and/or the like), which allows the collection module to thereby collect and gather specific data for that organization, its entities, and its users.”).
Regarding the combination of Lee-Hagen and Heinemeyer, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee-Hagen, upon which the claimed invention can be seen as an “improvement” through the use of an appliance feature;
2) the prior art contained a “comparable” system, namely the security system of Heinemeyer, that has been improved in the same way as the claimed invention through the appliance feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the appliance feature to the base security system of Lee-Hagen, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 13
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) is further configured to determine one or more components of the cybersecurity system to query external to a cyber security appliance within the cyber security system (¶ [0098], “The gather module (or the collections module) may comprise of multiple automatic data gatherers that each look at different aspects of the data depending on the particular hypothesis formed for the analyzed event and/or alert. The data relevant to each type of possible hypothesis will be automatically pulled from additional external and internal sources [appliances].”; and ¶ [0105], “The cyber security appliance 120 may supplement the data provided to the users and cyber professionals using a researcher module. The researcher module may use one or more artificial intelligence algorithms to assess whether the anomalous activity has previously appeared in other published threat research or known lists of malicious files or Internet addresses. The researcher module may consult internal threat databases or external public sources of threat data.”),
the cyber security appliance being associated with an organization to which the user belongs (¶ [0024], “For example, as used herein, the AI cyber-threat defense system may implement the AI adversary red team as an attack module as well as a training module depending on the desired goals of the respective organization [possessing users] (e.g., as shown with the AI adversary red team 105 in FIG. 1), while a cyber security appliance may be implemented as a cyber threat detection and protection module (e.g., as shown with the cyber security appliance 120 in FIG. 1).”; and ¶ [0057], “In some embodiments, the collections module may be configured to cooperate with the communication module as well as the analyzer module to gather external data from the OS database server 122 (and/or the like), which allows the collection module to thereby collect and gather specific data for that organization, its entities, and its users.”).
Regarding the combination of Lee-Hagen and Heinemeyer, the rationale to combine is the same as provided for claim 12 due to the overlapping subject matter of claims 12 and 13.
Regarding Claim 14
Lee in view Hagen, and further in view of Heinemeyer (“Lee-Hagen-Heinemeyer”) discloses the interactive cyber security user interface of claim 13, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) is configured to {query one or more components of the cybersecurity system (Hagen Col. 5:25-34, 5:44-55)}…1 (),
2 ….
Heinemeyer further discloses
1 …external to the cyber security appliance associated with an organization to which the user belongs (¶ [0024], “For example, as used herein, the AI cyber-threat defense system may implement the AI adversary red team as an attack module as well as a training module depending on the desired goals of the respective organization [possessing users] (e.g., as shown with the AI adversary red team 105 in FIG. 1), while a cyber security appliance may be implemented as a cyber threat detection and protection module (e.g., as shown with the cyber security appliance 120 in FIG. 1).”; and ¶ [0057], “In some embodiments, the collections module may be configured to cooperate with the communication module as well as the analyzer module to gather external data from the OS database server 122 (and/or the like), which allows the collection module to thereby collect and gather specific data for that organization, its entities, and its users.”)
that collates, processes or otherwise stores data received from a plurality of cybersecurity appliances within the cyber security system (¶¶ [0115]-[0117], “The AI adversary red team may be configured to search and query i) ingested network traffic data as well as ii) analysis on that network traffic data from a data store, from one or more modules, and from one or more AI models within the cyber security appliance. The AI adversary red team has access to and obtains a wealth of actual network data from the network under analysis from, for example, the data store, modules, and the AI models of normal pattern of life for entities in the network under analysis, which means thousands of paths of least resistance through possible routes in this network may be computed during the simulation even when one or more of those possible routes of least resistance that are not previously known or that have not been identified by a human before to determine a spread of the cyber threat from device-to-device.”),
2 the plurality of cyber security appliances comprising the cyber security appliance associated with the organization to which the user belongs, and one or more further cyber security appliances not associated with the organization to which the user belongs (¶¶ [0117]-[0119], “However, as discussed before, by having access to a wealth of network data from the data store and other components inside [and associated with the organization to which the user belongs] that the cyber security appliance, then the AI adversary red team may impliedly figure out restricted subnets for each device on the network and pathways unknown to human cyber professionals operating this network.”; and ¶ [0098], “The data relevant to each type of possible hypothesis will be automatically pulled from additional external [unassociated] and internal [associated] sources [associated with respective appliance]. Some data is pulled or retrieved by the gather module for each possible hypothesis from the data store.”).
Regarding the combination of Lee-Hagen and Heinemeyer, the rationale to combine is the same as provided for claim 12 due to the overlapping subject matter of claims 12 and 14.
Regarding Dependent Claim 19
With respect to dependent claim 19, a corresponding reasoning as given earlier for dependent claim 13 applies, mutatis mutandis, to the subject matter of claim 19. Therefore, claim 19 is rejected, for similar reasons, under the grounds set forth for claim 13.
F. Claims 15 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hagen, and further in view of Garcia et al. (US 2023/0274100 “Garcia”).
Regarding Claim 15
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) comprises an LLM (¶¶ [0040]-[0041]) that is…1
Lee-Hagen doesn’t disclose
1 …fine tuned using input and output pairs stochastically generated using a grammar rule.
Garcia, however, discloses
1 …fine tuned using input and output pairs stochastically generated using a formal grammar rule (¶ [0049], “One or more stochastic tuning ranges 310 can be used to provide extra conditioning for the decoder 304, and enable fine-grained [fine tuning] control of inference. This can include different “add” and “delete” rates. By way of example, for every input/output pair during training, the system can calculate the proportions of tokens that were added and deleted.”; ¶ [0088], “The style vector is added to the outputs of encoder model, and that combination is input to the decoder model (in which or more stochastic tuning ranges provide extra conditioning). The resultant multilingual model can handle not only sentences, but even random spans of text. It consumes exemplars in one language and extracts results in a different language.”; and ¶ [0081], “Native speaker consultants confirmed that, while not every translation is perfectly natural, they are all understandable, and the formality level clearly increases going from informal to neutral to formal. Interestingly, these differences manifest in both lexical and grammatical choices, including the use of formal vs. informal pronouns in Chinese, Russian and Spanish, despite this distinction being absent in English.”).
Regarding the combination of Lee-Hagen and Garcia, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee-Hagen, upon which the claimed invention can be seen as an “improvement” through the use of an language modeling feature;
2) the prior art contained a “comparable” system, namely the security system of Garcia, that has been improved in the same way as the claimed invention through the language modeling feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the language modeling feature to the base security system of Lee-Hagen, and the results would have been predictable to one of ordinary skill in the art.
G. Claims 17 is rejected under 35 U.S.C. 103 as being unpatentable over Lee in view of Hagen, and further in view of Peng et al. (US 2022/0084510 “Peng”).
Regarding Claim 17
Lee-Hagen discloses the interactive cyber security user interface of claim 1, and Lee further discloses
wherein the LLM module (¶¶ [0040]-[0041]) comprises a first LLM and a second LLM, each of the first LLM and the second LLM trained…1 specific to the context and content that that specific LLM will be queried on (¶¶ [0062]-[0064], “In some implementations, the NL analysis device 201 can be configured to include multiple [first and second] ML models [LLM] 212. Each ML model can be trained on corpuses [content] of text [associated with queries], and/or primed on text associated with an identified context, to perform on or more of auto-completion, auto-correction, intent inference, or generation of NL phrases based on a reference phrase.”), and
2 ….
Lee-Hagen doesn’t disclose
1 … using labelled training data…
2 wherein the context and content of labelled training data of a first LLM is different than context and content of labelled training data of a second LLM ().
Peng, however, discloses
1 … using labelled training data… (¶ [0023], “Machine learning models for natural language processing include natural language understanding models, which aim to infer information from natural language, and natural language generation or “generative” models, which aim to produce natural language based on some input. Training examples for natural language understanding models can be oriented to a particular task. For instance, to train a natural language understanding model to understand user utterances requesting travel to different destinations, a task-specific corpus of labeled training examples can be used.”)
2 wherein the context and content of labelled training data of a first LLM is different than context and content of labelled training data of a second LLM (¶¶ [0028]-[0030], “The disclosed implementations offer mechanisms that can be used to generate synthetic training data for natural language understanding [first] models. The approaches disclosed herein utilize generative [second] models that are trained in a manner that adapts the generative models for a particular task. Once adapted to the task, such a generative model may be suitable for generating synthetic utterances that can be used alone or in conjunction with human-generated utterances to train a natural language understanding model.”; and “As discussed more below, the disclosed implementations can start by performing a first pretraining stage on a generative model using a first training data set, such as large, general-purpose corpus of unlabeled natural language examples. For instance, the first training data set can include Wikipedia articles, books, web articles, or other documents from a range of subject domains. This trains the generative model to produce natural language that may be reasonably understandable to a human being, but not necessarily suitable for conducting a dialog with a human user. Next, a second pretraining stage can be employed on a second training data set, such as a corpus of natural language examples with corresponding labeled dialog acts (e.g., intents and slots) for a broad range of task domains. Here, the generative model learns how to conduct a general dialog with a user, e.g., by responding to a question with an answer, confirming information received from a user, asking clarifications, performing an action requested by the user, and so on.”).
Regarding the combination of Lee-Hagen and Peng, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the security system of Lee to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the security system of Lee-Hagen, upon which the claimed invention can be seen as an “improvement” through the use of a training label feature;
2) the prior art contained a “comparable” system, namely the language model system of Peng, that has been improved in the same way as the claimed invention through the training feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the training label feature to the base security system of Lee-Hagen, and the results would have been predictable to one of ordinary skill in the art.
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 D'ARCY WINSTON STRAUB whose telephone number is (303)297-4405. The examiner can normally be reached Monday-Friday 9:00-5:00 Mountain Time.
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/D'Arcy Winston Straub/Primary Examiner, Art Unit 2491
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