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 .
Specification
The use of the following trademarks have been noted in this application:
ChatGPT, Gemini, llama at ¶0050
Wherever the trademark appears, each letter of the trademark should be capitalized, or the proper trademark indication should be included. Each instance of the trademark should be accompanied by the generic terminology.
Although the use of trademarks is permissible in patent applications, the proprietary nature of the marks should be respected and every effort made to prevent their use in any manner which might adversely affect their validity as trademarks.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 19 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 19 recites “having information”. However, claims 17 and 19 previously introduce “encapsulated information”. It is unclear whether the “having information” of claim 19 is referring to the previously introduced “encapsulated information” or is introducing new “information”. As a result of this antecedent basis ambiguity, the scope of the claim is rendered indefinite.
Prior Art
Listed herein below are the prior art references relied upon in this Office Action:
Townsend-Last (US Patent Application Publication 2026/0023935), referred to as Townsend-Last herein.
Hykes et al. (US Patent Application Publication 2024/0078101), referred to as Hykes herein.
Sadiq et al. (US Patent Application Publication 2004/0133457), referred to as Sadiq herein.
Cohen (US Patent Application Publication 2024/0291863), referred to as Cohen herein.
AI with Rithesh (“TOOL USE (FUNCTION CALLING) With Anthropic CLAUDE 3 API – A Step by Step Tutorial”, https://www.youtube.com/watch?v=W7z4Zas--20), referred to as Rithesh herein.
DAI et al. (US Patent Application Publication 2025/0378192), referred to as DAI herein.
Sethuraman et al. (US Patent Application Publication 2025/0156632), referred to as Sethuraman herein.
Examiner’s Note
Strikethrough notation in the pending claims has been added by the Examiner.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2, 4-6, 9-10, 12, and 15 is/are rejected under 35 U.S.C. 102(a)(2) as being anticiapted by Townsend-Last.
Regarding claim 1, Townsend-Last discloses a computer-implemented method comprising (Townsend-Last, Fig. 10 with ¶0093, ¶0124, ¶0129 – hardware memory storing instructions executed by a processor):
receiving, from a client device (Townsend-Last, Figs. 3 and 10 with ¶0020-¶0021, ¶0062, ¶0114, and ¶0125 – server computers communicating with a personal or mobile computer user device),
a user request for modifying a digital file (Townsend-Last, Figs. 2C-2D, 3, and 6 with ¶0028, ¶0034, ¶0036, ¶0058, and ¶0095 – instruction to perform a modification of a document);
generating, using a language machine learning model, a task plan having formatted code that indicates one or more application programming interface calls to execute to modify the digital file in accordance with the user request (Townsend-Last, Fig. 6 with ¶0098 – LLM generates computer program code to perform the task. ¶0023, ¶0051-¶0053, ¶0062, ¶0098, ¶0118 – code uses API calls to perform modifications);
generating, via one or more code verifications on the formatted code, an error log that identifies one or more errors in the task plan; generating, from the error log and using the language machine learning model, a corrected task plan that corrects the one or more errors (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0101-¶0107 – AI code output is validated for errors. The detected errors are fed back to the LLM to correct the code); and
providing, for display on the client device, a modified digital file generated through execution of the corrected task plan (Townsend-Last, Figs. 2D-2E, 6, 7, 9C-9D with ¶0021, ¶0026-¶0028, ¶0098-¶0100, ¶0108, ¶0121-¶0123 – modification is performed to the displayed digital file via execution of the corrected code).
Regarding claim 2, Townsend-Last discloses the elements of claim 1 above, and further discloses wherein generating the error log via the one or more code verifications on the formatted code comprises generating the error log via one or more inter-task dependency verifications that check for at least one of dependency hallucination or dependency consistency within the formatted code (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0098, ¶0101-¶0107, ¶0117 – AI code output is validated for errors, including hallucinated API calls or inaccessible APIs or functions. The detected errors are fed back to the LLM to correct the code).
Regarding claim 4, Townsend-Last discloses the elements of claim 1 above, and further discloses wherein generating the error log via the one or more code verifications on the formatted code comprises generating the error log via one or more static composition verifications that check for at least one of syntax hallucination, tool hallucination, or argument validity within the formatted code (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0098, ¶0101-¶0107, ¶0117 – AI code output is validated for errors, including syntax, data type, algorithmic, logic, hallucinated API, or inaccessible API errors. The detected errors are fed back to the LLM to correct the code).
Regarding claim 5, Townsend-Last discloses the elements of claim 1 above, and further discloses wherein providing, for display on the client device, the corrected task plan (Townsend-Last, ¶0111, ¶0121-¶0122 – preview); and
generating, using the language machine learning model, a modified task plan based on user feedback on the corrected task plan received via the client device, wherein providing, for display on the client device, the modified digital file generated through execution of the corrected task plan comprises providing, for display on the client device, the modified digital file generated through execution of the modified task plan (Townsend-Last, ¶0100 – subsequent natural language inputs can trigger correction of task errors or changes to the tasks).
Regarding claim 6, Townsend-Last discloses the elements of claim 1 above, and further discloses using at least one language machine learning model, executable code for modifying the digital file from the corrected task plan, wherein providing the modified digital file generated through execution of the corrected task plan comprises providing the modified digital file generated through execution of the executable code (Townsend-Last, Figs. 2D-2E, 6, 7, 9C-9D with ¶0021, ¶0026-¶0028, ¶0100, ¶0108, ¶0121-¶0123 – modification is performed to the displayed digital file via execution of the code).
Regarding claim 9, Townsend-Last discloses the elements of claim 6 above, and further discloses generating, using the at least one language machine learning model, corrected executable code that corrects at least one error identified in the executable code, wherein providing the modified digital file generated through execution of the executable code comprises providing the modified digital file generated through execution of the corrected executable code (Townsend-Last, Figs. 2D-2E, 6, 7, 9C-9D with ¶0021, ¶0026-¶0028, ¶0098-¶0100, ¶0108, ¶0121-¶123 – modification is performed to the displayed digital file via execution of the corrected code).
Regarding claim 10, Townsend-Last a system comprising: one or more memory devices; and one or more processors configured to cause the system to (Townsend-Last, Fig. 10 with ¶0093, ¶0124, ¶0129 – hardware memory storing instructions executed by a processor):
determine, using a language machine learning model, a task plan having formatted code that indicates one or more application programming interface calls to execute to modify a digital file in accordance with a user request (Townsend-Last, Figs. 2C-2D, 3, and 6 with ¶0028, ¶0034, ¶0036, ¶0058, and ¶0095 – instruction to perform a modification of a document. Fig. 6 with ¶0098 – LLM generates computer program code to perform the task. ¶0023, ¶0051-¶0053, ¶0062, ¶0098, ¶0118 – code uses API calls to perform modifications);
determine, using the language machine learning model, a corrected task plan that corrects one or more errors identified in the task plan; generate, using at least one language machine learning model, executable code for modifying the digital file from the corrected task plan (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0101-¶0107 – AI code output is validated for errors. The detected errors are fed back to the LLM to correct the code);
generate, using the at least one language machine learning model, corrected executable code that corrects at least one error identified in the executable code; and modify the digital file in accordance with the user request by executing the corrected executable code (Townsend-Last, Figs. 2D-2E, 6, 7, 9C-9D with ¶0021, ¶0026-¶0028, ¶0098-¶0100, ¶0108, ¶0121-¶123 – modification is performed to the displayed digital file via execution of the corrected code).
Regarding claim 12, Townsend-Last discloses the elements of claim 10 above, and further discloses generate a task planning prompt having at least one sample pair comprising a sample user request and a sample task plan that corresponds to the sample user request; and determine, using the language machine learning model, the task plan by generating, using the language machine learning model, the task plan from the task planning prompt (Townsend-Last, Figs. 2C-2D, 3, and 6 with ¶0028, ¶0034, ¶0036, ¶0058, and ¶0095 – instruction to perform a modification of a document. Fig. 6 with ¶0098 – LLM generates computer program code to perform the task. ¶0111, ¶0121-¶0122 – preview. ¶0100 – subsequent natural language inputs can trigger correction of task errors or changes to the tasks).
Regarding claim 15, Townsend-Last discloses the elements of claim 10 above, and further discloses wherein the one or more processors are configured to cause the system to determine the one or more errors in the task plan by performing a plurality of code verifications on the formatted code, the plurality of code verifications including a set of inter-task dependency verifications and a set of static composition verifications (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0098, ¶0101-¶0107, ¶0117 – AI code output is validated for errors, including syntax, data type, algorithmic, logic, hallucinated API, or inaccessible API errors. The detected errors are fed back to the LLM to correct the code).
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) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend-Last in view of Hykes in further view of Sadiq.
Regarding claim 3, Townsend-Last discloses the elements of claim 2 above. However, Townsend-Last appears not to expressly disclose wherein generating the error log via the one or more inter-task dependency verifications that check for dependency consistency within the formatted code comprises: generating a dependency graph from the task plan, the dependency graph having a set of nodes corresponding to the one or more application programming interface calls indicated by the formatted code and a set of edges corresponding to interdependencies for the one or more application programming interface calls; and determining whether the dependency graph includes a directed acyclic graph.
However, in the same field of endeavor, Hykes discloses automated software application generation (Hykes, Abstract), including
generating a dependency graph from the task plan, the dependency graph having a set of nodes corresponding to the one or more application programming interface calls indicated by the formatted code and a set of edges corresponding to interdependencies for the one or more application programming interface calls (Hykes, Abstract with ¶0047, ¶0062, ¶0162 – automation processor generates a DAG to reflect dependencies in script including API calls. DAG nodes and edges correspond to dependencies).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the dependency verification of Townsend-Last to include application dependency DAG generation based on the teachings of Hykes. The motivation for doing so would have been to more effectively manage script dependencies, enabling more effective and efficient software generation automation, modifications and reduction of deployment errors and performance problems (Hykes, ¶0005-¶0009).
However, Townsend-Last as modified appears not to expressly disclose determining whether the dependency graph includes a directed acyclic graph. However, in the same field of endeavor, Sadiq discloses identification of task dependencies (Sadiq, Abstract – Abstract with ¶0003, ¶0104-¶0105), including
determining whether the dependency graph includes a directed acyclic graph (Sadiq, Fig.17 with ¶0166-¶0167, ¶0174 – identification of closed-loop fragments that precludes incorporation in a DAG).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the dependency DAG of Townsend-Last as modified to include checking for validity based on the teachings of Sadiq. The motivation for doing so would have been to more effectively identify dependency conflicts (Sadiq, ¶0167, ¶0174).
Claim(s) 7-8 and 17-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend-Last in view of Cohen.
Regarding claim 7, Townsend-Last discloses the elements of claim 6 above, and further discloses wherein generating the executable code using the at least one language machine learning model comprises: generating an
generating, using the at least one language machine learning model, the executable code from the
However, Townsend-Last appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Cohen discloses generating computer executable instructions for API calls (Cohen, Abstract with ¶0122), including
encapsulated code generation (Cohen, ¶0057, ¶0122-¶0127 – API call encapsulation).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the code generation prompt of Townsend-Last to include encapsulation based on the teachings of Cohen. The motivation for doing so would have been to more effectively perform validation and integrity checks, as well as to protect against cybersecurity threats (Cohen, ¶0127).
Regarding claim 8, Townsend-Last as modified discloses the elements of claim 7 above, and further discloses wherein generating the encapsulated code generation prompt that includes the encapsulated information comprises generating the encapsulated code generation prompt that includes the encapsulated information and one or more guardrails that correspond to at least one of code generation syntax, software import compatibility, or data privacy related to file handling (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0098, ¶0101-¶0107, ¶0117 – AI code output is validated for errors, including syntax, data type, algorithmic, logic, hallucinated API, or inaccessible API errors. The detected errors are fed back to the LLM to correct the code. Cohen, ¶0098 – barrier for access authorization).
Regarding claim 17, Townsend-Last discloses a non-transitory computer-readable medium storing executable instructions which, when executed by a processing device, cause the processing device to perform operations comprising (Townsend-Last, Fig. 10 with ¶0093, ¶0124, ¶0129 – hardware memory storing instructions executed by a processor):
receiving a digital file and a user request for modifying the digital file (Townsend-Last, Figs. 2C-2D, 3, and 6 with ¶0028, ¶0034, ¶0036, ¶0058, and ¶0095 – instruction to perform a modification of a document);
generating, using a language machine learning model, a task plan having formatted code that indicates one or more application programming interface calls to execute to modify the digital file in accordance with the user request (Townsend-Last, Fig. 6 with ¶0098 – LLM generates computer program code to perform the task. ¶0023, ¶0051-¶0053, ¶0062, ¶0098, ¶0118 – code uses API calls to perform modifications);
generating an
and modifying the digital file in accordance with the user request by executing the executable code (Townsend-Last, Figs. 2D-2E, 6, 7, 9C-9D with ¶0021, ¶0026-¶0028, ¶0098-¶0100, ¶0108, ¶0121-¶123 – modification is performed to the displayed digital file via execution of the corrected code).
However, Townsend-Last appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Cohen discloses generating computer executable instructions for API calls (Cohen, Abstract with ¶0122), including
encapsulated code generation (Cohen, ¶0057, ¶0122-¶0127 – API call encapsulation).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the prompt of Townsend-Last to include encapsulation based on the teachings of Cohen. The motivation for doing so would have been to more effectively perform validation and integrity checks, as well as to protect against cybersecurity threats (Cohen, ¶0127).
Regarding claim 18, Townsend-Last as modified discloses the elements of claim 17 above, and further discloses wherein modifying the digital file in accordance with the user request includes converting the digital file from a first format to a second format (Townsend-Last, ¶0090, ¶0113 – changing text format).
Regarding claim 19, Townsend-Last as modified discloses the elements of claim 17 above, and further discloses wherein generating the encapsulated code generation prompt that includes the encapsulated information for the one or more application programming interface calls comprises generating the encapsulated code generation prompt having information limited to a function name, one or more input arguments, and one or more returned values corresponding to the one or more application programming interface calls (Townsend-Last, ¶0117 – expected number of parameters input to a given function are verified. Cohen, ¶0122-¶0123 – arguments input/returned by the API. API input parameters and output values. See also Fig. 4-3 with ¶0138 – function calls using names, return parameters, and input parameters. See also Fig. 4-6 with ¶0126).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the function calls of Townsend-Last as modified to include returned values based on the teachings of Cohen. The motivation for doing so would have been to enable further downstream processing of output from the wrapper function and realize modification output to the document.
Regarding claim 20, Townsend-Last as modified discloses the elements of claim 17 above, and further discloses wherein: the operations further comprise generating a corrected task plan by iteratively updating the task plan to correct one or more errors identified in the task plan; and generating the encapsulated code generation prompt that includes the encapsulated information for the one or more application programming interface calls of the task plan comprises generating the encapsulated code generation prompt that includes the encapsulated information for at least one application programming interface call of the corrected task plan (Townsend-Last, Fig. 7 with ¶0039-¶0040, ¶0101-¶0107 – AI code output is validated for errors. The detected errors are fed back to the LLM to correct the code. ¶0100 – subsequent iterative natural language inputs can trigger correction of task errors or changes to the tasks).
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend-Last in view of Rithesh.
Regarding claim 11, Townsend-Last discloses the elements of claim 10 above, and further discloses generate a task planning prompt having information for a set of associated with at least one t
However, Townsend-Last appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor, Rithesh discloses LLM code generation (Rithesh, Pages 1-3 @ 0:05, 0:17, 0:21 in to the video), including
generate a task planning prompt having information for a set of tools of an application that are available, the code indicates at least one application programming interface call associated with at least one tool from the set of tools of the (Rithesh, Pages 1, 4-23 @ 0:05, 0:26, 0:51, 0:55, 1:20, 1:40, 1:49, 1:55, 1:58, 2:07, 2:12, 2:16, 2:25, 2:29, 2:34, 2:37, 2:43, 2:52, 2:54, 2:58, 3:03 into the video – indicating to the LLM the available tools. Requests for output are compared against the available tools to select an appropriate tool. The LLM selects the appropriate tool to generate output).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the API calls of Townsend-Last to include tools based on the teachings of Rithesh. The motivation for doing so would have been to more effectively generate reliable and accurate output (Rithesh, Page 6).
Claim(s) 13 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend-Last in view of Sethuraman.
Regarding claim 13, Townsend-Last discloses the elements of claim 12 above, and further discloses generating a request embedding from the user request; generating a plurality of sample request embeddings from a plurality of sample user requests corresponding to a plurality of sample pairs; and ¶0036, ¶0058, ¶0095, ¶0121 – instruction to perform a modification of a document. Fig. 6 with ¶0098 – LLM generates computer program code to perform the task. ¶0111, ¶0121-¶0122 – preview. ¶0100 – subsequent natural language inputs can trigger correction of task errors or changes to the tasks).
However, Townsend-Last appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor Sethuraman discloses LLM hallucination reduction (Sethuraman, Abstract with ¶0014), including
generating a request embedding from the user request; generating a plurality of sample request embeddings from a plurality of sample user requests corresponding to a plurality of sample pairs; and determining the at least one sample pair based on comparing the request embedding to the plurality of sample request embeddings (Sethuraman, ¶0010, ¶0013, ¶0023, ¶0027 – generating embeddings representative of the prompt and comparing against verified prompt-response pairs to determine similar pairs).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the prompts and responses of Townsend-Last to include verified prompt-response pairs for comparison based on the teachings of Sethuraman. The motivation for doing so would have been to more effectively prevent hallucinations by validating against known similar prompt/response pairs (Sethuraman, Abstract with ¶0027).
Regarding claim 14, Townsend-Last discloses the elements of claim 12 above, and further discloses prompt (Townsend-Last, Figs. 2C-2D, 3, 6, 9D with ¶0028, ¶0034, ¶0036, ¶0058, ¶0095, ¶0121 – instruction to perform a modification of a document. Fig. 6 with ¶0098 – LLM generates computer program code to perform the task. ¶0111, ¶0121-¶0122 – preview. ¶0100 – subsequent natural language inputs can trigger correction of task errors or changes to the tasks).
However, Townsend-Last appears not to expressly disclose the limitations in strikethrough above. However, in the same field of endeavor Sethuraman discloses LLM hallucination reduction (Sethuraman, Abstract with ¶0014), including
generate a prompt having at least one additional sample pair comprising the sample prompt and sample response (Sethuraman, ¶0010, ¶0013, ¶0023, ¶0027 – generating embeddings representative of the prompt and comparing against verified prompt-response pairs to determine similar pairs).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the prompts and responses of Townsend-Last to include verified prompt-response pairs for comparison based on the teachings of Sethuraman. The motivation for doing so would have been to more effectively prevent hallucinations by validating against known similar prompt/response pairs (Sethuraman, Abstract with ¶0027).
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over Townsend-Last in view of DAI.
Regarding claim 16, Townsend-Last appears discloses the elements of claim 10 above. However, Townsend-Last appears not to expressly disclose wherein the one or more processors are configured to cause the system to modify the digital file in accordance with the user request by extracting one or more pages from the digital file, deleting one or more pages from the digital file, redacting one or more pages from the digital file, or redacting text from the digital file.
However, in the same field of endeavor, DAI discloses use of an LLM to operate on a document (DAI, Abstract with ¶0001, ¶0031), including
wherein the one or more processors are configured to cause the system to modify the digital file in accordance with the user request by extracting one or more pages from the digital file, deleting one or more pages from the digital file, redacting one or more pages from the digital file, or redacting text from the digital file (DAI, ¶0026-¶0029- redaction of identifiable text).
Therefore, it would have been obvious to one of ordinary skill in the art, before the effective filing date of the claimed invention, to have modified the document modification of Townsend-Last to include redaction based on the teachings of DAI. The motivation for doing so would have been to provide more effectively prevent information compromise for large numbers of files consistently and rapidly (DIA, ¶0002-¶0003).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. References are at least relevant as indicated in the corresponding summary.
CUI et al. (US Patent Application Publication 2025/0335773) – generating prompt response pairs and matching prompts against the stored pairs.
Karmakar et al. (US Patent Application Publication 2026/0010963) – generating prompt response pairs and matching prompts against the stored pairs.
Medford (US Patent Application Publication 2025/0165890) – automated software generation via LLMs including error checking.
O’Hara (US Patent Application Publication 2025/0355631) – automated software generation via LLMs including error checking.
Palladino et al. (US Patent Application Publication 2025/0258938) – LLM document redaction.
Rambow et al. (US Patent Number 12,619,399) – Software generation for task completion via LLM.
Schaefer et al. (US Patent Application Publication 2024/0311582) – automated software generation via LLMs including error checking.
Schornig et al. (US Patent Application Publication 2026/0037840) – LLM code generation for executing a task.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DANIEL W PARCHER whose telephone number is (303)297-4281. The examiner can normally be reached Monday - Friday, 9:00am - 5:00pm, Mountain Time.
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/DANIEL W PARCHER/Primary Examiner, Art Unit 2174
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