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 .
Claims 1, 4, 7, 9, 12, 14, and 16 are amended. Claims 2, 3, 8, 11 and 18 are cancelled. Claims 1, 4-7, 9, 10, 12-17, and 19-24 are pending.
The following rejections are withdrawn in view of new grounds of rejection necessitated by applicant’s amendments:
Claims 1, 4-10, 12-17, and 19-22 rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Application: US 20240177084, published: May 30, 2024, filed: Sep. 29, 2023) in view of DeFoor et al (US Patent: 11972223, issued: Apr. 30, 2024, filed: Jul. 31, 2023).
Claims 23 and 24 rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Application: US 20240177084, published: May 30, 2024, filed: Sep. 29, 2023) in view of DeFoor et al (US Patent: 11972223, issued: Apr. 30, 2024, filed: Jul. 31, 2023) in view of Gottlob et al (US Application: US 2025/0045256, published: Feb. 6, 2025, filed: Aug. 2, 2024, EEFD: Aug. 4, 2023).
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.
Claims 1, 4-7, 9, 10, 12-17, and 19-24 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.
With regards to claim 1, the claim first recites determining a plurality of history records as exceeding a predetermined length “in response to determining that the history records exceed a predetermined length”. Subsequently, the claim recites a singular history record into a set of chunks : “dividing the history record into a set of chunks”. This limitation is indefinite, as it references “the history record”, however the limitations occurring prior to “the history record” only reference “the history records” in plural form, and it is unclear where the singular history record has come from or was introduced. For purposes of examination, the examiner will examine the claim based on an interpretation of a singular history record.
With regards to claim 1, the claim recites “generating, for each chunk of the set of chunks, one or more intermediate summaries of the chunk”. The specification (paragraph 0134) explains that each chunk results in a single intermediate summary and it is not clear how a plurality of summaries belong to the chunk (‘intermediate summaries’) as currently claimed in view of the specification. For purposes of examination, the examiner will interpret the applicant intended to claim a plurality of summaries for a specific facet.
With regards to claims 4-7, they are rejected under similar rationale as claim 1, since they do not resolve the deficiencies of claim 1, and are rejected under similar rationale.
With regards to claim 9, it is rejected under similar rationale as claim 1, and is rejected under similar rationale.
With regards to claims 10 and 12-15, they are rejected under similar rationale as claim 1, since they do not resolve the deficiencies of claim 1, and are rejected under similar rationale.
With regards to claim 16, it is rejected under similar rationale as claim 1, and is rejected under similar rationale.
With regards to claim 17 and 19-24, they are rejected under similar rationale as claim 1, since they do not resolve the deficiencies of claim 1, and are rejected under similar rationale.
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.
Claims 1, 4-6, 9, 10, 12-17, and 19-22 are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Application: US 20240177084, published: May 30, 2024, filed: Sep. 29, 2023) in view of DeFoor et al (US Patent: 11972223, issued: Apr. 30, 2024, filed: Jul. 31, 2023) in view of Zhang (US Application: US 2025/0190449, published: Jun. 12, 2025, filed: Dec. 11, 2023).
With regards to claim 1. Liu et al teaches a computer-implemented method (Fig 13, paragraph 0094: a computing device is implemented using a memory, processor which executes programs) comprising:
receiving, at a server system, a free-form query (paragraph 0074 and 0094: a question is received by a user);
identifying history records from a database associated with the free-form query (paragraphs 0065, 0074: two way communications between at least two parties are selected from a transcript); and
aggregating the language responses (paragraph 0078: each information (language response) corresponding to each chunk is then aggregated /merged into a single chunk); and
processing the aggregated language responses using a [particular] large language model to generate a summary of the history records (paragraph 0079: the language responses as the merged chunk is sent to a particular summarizer LLM to generate an answer/summary).
However Liu et al does not expressly teach … wherein the free-form query includes a plurality of facets; … determining a length of the history records; in response to determining that the history records exceed a predetermined length; dividing the history record into a set of chunks; generating, for each chunk of the set of chunks, one or more intermediate summaries of the chunk, wherein each intermediate summary of the one or more intermediate summaries corresponds to a different facet of the plurality of facets; generating, from the free-form query, a plurality of query prompts using the one or more intermediate summaries, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets;
in response to determining the history records are below the predetermined length; generating from the free-form query, the plurality of query prompts, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets; providing the plurality of query prompts and the history records to one or more large language models; receiving, from the one or more language models, language responses for each of the plurality of query prompts; and; … generate a summary of the language responses.
Yet DeFoor et al teaches … wherein the free-form query includes a plurality of facets (column 10, lines 32-39: a natural language query is received and the query includes natural language text and one or more instructions in a query language); … determining a length of the history records (column 13, lines 35-45: a text size is determined and assessed against a chunk size for the stored text (interpreted as ‘history record(s)’); in response to determining that the history records exceed a predetermined length; dividing the history record into a set of chunks (column 13, lines 45-56: a determination is made identifying that the text exceed a predetermined chunk size/length), generating, for each chunk of the set of chunks … … and generating, from the free-form query, a plurality of facet-specific query prompts using [each chunk] ] … (column 22, lines 4-12: each chunk can be considered an intermediate summary and prompts obtained using template(s) are generated by filling the templates with chunk text (see “A document relevance prompt is determined for the selected chunk at 912 based on the document relevance prompt template. In some implementations, determining the document relevance prompt may involve adding the chunk text to the document relevance prompt template in the fillable document portion. In addition, all or a portion of the query, topic, or question may be added to the document relevance prompt template in a fillable query portion”);
in response to determining the history records are below the predetermined length, generating from the free-form query, the plurality of query prompts … (column 14, lines 4-15: “An updated text portion that does not exceed the maximum text chunk size is identified”. Also in column 25, lines 40-50: “The document chunks and the questions to answer are used to generate document review prompts at 1210 through 1212. A document review prompt may be created by combining a document chunk with one or more questions and a document review prompt template. The document review prompt template may have one or more fillable portions that may be filled with text determined based on the document chunk and questions. The document review prompt may include one or more instructions to a large language model” ); providing the plurality of query prompts and the history records to one or more large language models (column 25, lines 40-50, 60-67: a question has different parameters for task(s), and the task(s) have different parameter(s)/instruction(s) (claimed facet(s)). “The document chunks and the questions to answer are used to generate document review prompts at 1210 through 1212. A document review prompt may be created by combining a document chunk with one or more questions and a document review prompt template. The document review prompt template may have one or more fillable portions that may be filled with text determined based on the document chunk and questions. The document review prompt may include one or more instructions to a large language mode”); receiving, from the one or more language models, language responses for each of the plurality of query prompts (column 27, lines 56-35: “prompts are sent to the text generation modeling system 270 via one or more API calls at 1216 through 1218, where they are individually analyzed and completed by the text generation model 276. At 1218 through 1220, the text generation modeling system 270 sends one or more document review response messages to the text generation interface system 210”); and; … generate a summary of the language responses (column 27, lines 50-63: a summary of language responses is generated through consolidation).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Liu et al’s ability to process an aggregate of obtained language responses using a summarizing type LLM (interpreted as the claimed ‘second’) based upon a provided query and historical data, such length of historical/saved data is determined prior to prompt(s) that are generated and provided to a language model for summary generation associated with facets (of the provided query), such that the summary generation is based applying generated prompts to one or more language models, as taught by DeFoor et al. as taught by DeFoor et al. The combination would have allowed Liu et al to have efficiently processed input documents using Large Language Models. .
However the combination Liu et al and DeFoor et al does not expressly teach … wherein each intermediate summary of the one or more intermediate summaries corresponds to a different facet of the plurality of facets; generating, from the free-form query, a plurality of query prompts using the one or more intermediate summaries, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets; … generating from the free-form query, the plurality of query prompts, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets.
Yet Zhang et al teaches wherein each intermediate summary of the one or more intermediate summaries corresponds to a different facet of the plurality of facets (semantic metadata is generated for each grouping of data (paragraph 0049: such as grouping of columns and/or rows which is interpreted as a chunk of column and/or row data) ); generating, from the free-form query, a plurality of query prompts using the one or more intermediate summaries, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets (paragraph 0049: using the semantic grouping metadata for each group (interpreted as the claim chunk that is specific to a facet) of data, an optimized prompt is generated specific to facets of the row(s)/column(s) ); … generating from the free-form query, the plurality of query prompts, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets (paragraphs 0049 and 0052: each prompt generated includes a specific terms that are specific to a specific facet of a plurality of facets and the one or more AI Agents will be selected to process the optimized/generated prompt).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Liu et al and DeFoor et al’s ability to perform conditional chunk processing based on length of data, such that the data could have been records having different facets of data (such as different fields) and also chunks could been associated with their own summary of a facet, as taught by Zhang et al. The combination would have allowed Liu et al and DeFoor et al to have improved generated output and flexibly have allowed LLMs to produce output in analytical applications with decreased chance of hallucinations.
With regards to claim 4. The computer-implemented method of claim 1, Liu et al teaches further comprising:
selecting an additional number of history records from a database associated with the free-form query to identify second selected history records (paragraph 0075, paragraph 0080, Fig. 12: based on the query/question, when there is not just a single transcript of records, but multiple transcript of records, other transcripts (such as a second transcript among a plurality of transcripts) will be accessed to obtain/select from it, it’s history record data));
generating additional individual summaries of individual history records of the second selected history records by:
processing additional set of corresponding individual history records using the chunking algorithm (Fig. 12, each transcript , such as a second transcript is processed to produce a summarized output (language response). The summarization for each transcript is detailed in Fig. 11 (as also explained in the rejection of claim 1 above), which includes chunking the transcript currently undergoing processing (as explained above in paragraph 0074 of Liu et al));
constructing additional language responses from second outputs of the chunking algorithm using the one or more large language models (Fig. 12, paragraph 0075 of Liu et al: the language responses for each transcript (such as for a second transcript) results in language responses (such as second language responses for a second transcript) where an LLM is used to process each chunk to identify information (language responses) for each chunk deemed relevant to the question);
aggregating the additional language responses (as explained above in paragraph 0078 of Liu et al: each information (language response) corresponding to each chunk is then aggregated /merged into a single chunk);
processing the additional language responses using the one or more large language models to generate an additional individual summary for additional corresponding individual history records (paragraph 0079 of Liu et al: the language responses as the merged chunk is sent to an LLM to generate an answer/summary); and
generating, using the one or more large language models, an aggregate summary using the individual summary and the additional individual summary (Fig. 12, the summary from the first individual summary (corresponding to a first transcript) and summary for additional transcript (second transcript) are combined (ref 1206) and then sent to the LLM to generate an aggregate summary (such as a short summary or a long summary in ref 1212 and 1214 respectively)).
The examiner further notes, as explained in the rejection of claim 1, Liu et al’s summarization of aggregated response data was modified/combined with the teachings of Austin et al, such that the response data is sourced from one or more LLMs based one or more historical records (interpreted to also encompass ‘second additional’ history records) to produce the aggregate summar(ies).
With regards to claim 5. The computer-implemented method of claim 1, Liu et al teaches further comprising:
generating a plurality of individual summaries for sets of history records numbering less than or equal to a threshold number (paragraph 0074: any number of meeting/communications records having a size less than or equal to the maximum token size for an LLM , will result in generating summaries (and if there are a plurality of transcripts, then a plurality of summaries are generated as shown in Fig. 12, ref 1204));
processing the plurality of individual summaries using an additional large language model to generate an aggregated summary for data of the sets of history records (Fig. 12, ref 1212 and ref 1214: a second instance of LLM can be used to generate a different version of the aggregated summary (such as a longer summary being generated by an instance of an LLM different than the earlier instance of the LLM referenced in 1212 for a short summary)).
With regards to claim 6. The computer-implemented method of claim 1, Liu et al teaches further comprising:
generating a summary query (Fig. 12, ref 1204: a request for each transcript is dispatched to an LLM) and an aggregation query from the free-form query (Fig. 12, ref a step to initiate combination of summarized transcripts leads to step ref 1206);
generating a plurality of individual summaries for sets of history records including the individual summaries using the summary query (Fig. 12, ref 1206: based upon the queries to initiate a summarization of each transcript, the summaries are aggregated ); and processing the plurality of individual summaries using the aggregation query and an additional large language model (Fig. 12: Using the plurality of individual summaries in aggregated/combined form, processing them using another/second instance of the LLM that is specialized to either generate a short or a long summary).
The examiner further notes, as explained in the rejection of claim 1, Liu et al’s summarization of aggregated response data was modified/combined with the teachings of Austin et al, such that the response data is sourced from one or more LLMs based one or more historical records (interpreted to also encompass ‘second additional’ history records) to produce the aggregate summar(ies).
With regards to claim 9, Liu et al, DeFoor et al and Zhang et al teaches a system comprising: a memory; and one or more processors coupled to the memory and configured to perform operations comprising: receiving, at a server system, a free-form query, wherein the free-form query includes a plurality of facets; corresponds to a different facet of the plurality of facets; and generating, from the free-form query, a plurality of query prompts using the one or more intermediate summaries, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets; ; in response to determining the history records are below the predetermined length; generating from the free-form query, the plurality of query prompts, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets; providing the plurality of query prompts and the history records to one or more large language models; receiving, from the one or more large language models, language responses for each of the plurality of facet specific query prompts; and ; aggregating the language responses using a second large language model to generate a summary of the language , as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 10. The system of claim 9, Liu et al teaches wherein the history records are selected from the database either randomly or using a search algorithm (paragraph 0074: the transcript having the history of meeting records are obtained/searched based upon its association with a specific knowledge transfer plan).
With regards to claim 12, The system of claim 9, Liu et al, DeFoor et al and Zhang et al teaches wherein the one or more processors are further configured to perform operations comprising: selecting an additional number of history records from a database associated with the free-form query to identify additional selected history records; generating additional individual summaries of individual history records of the
With regards to claim 13. The system of claim 9, Liu et al, Defoor et al and Zhang et al teaches wherein the one or more processors are further configured to perform operations comprising: generating a plurality of individual summaries for sets of history records numbering less than or equal to a threshold number; and processing the plurality of individual summaries using an additional large language model different than the one or more large language models to generate an aggregated summary for data of the sets of history records, as similarly explained in the rejection of claim 5, and is rejected under similar rationale.
With regards to claim 14. The system of claim 9, Liu et al, Defoor et al and Zhang et al teaches wherein the one or more processors are further configured to perform operations comprising: generating a plurality of individual summaries for sets of history records numbering less than or equal to a threshold number; and processing the plurality of individual summaries using an additional large language model different than the two or more large language models to generate an aggregated summary for data of the sets of history records, as similarly explained in the rejection of claim 5, and is rejected under similar rationale.
With regards to claim 15. The system of claim 9, Liu et al, Defoor et al and Zhang et al teaches wherein the one or more processors are further configured to perform operations comprising: generating a summary query and an aggregation query from the free-form query; generating a plurality of individual summaries for sets of history records including the individual summaries using the summary query; and processing the plurality of individual summaries using the aggregation query and an additional large language model, as similarly explained in the rejection of claim 6, and is rejected under similar rationale.
With regards to claim 16, the combination of Liu et al, DeFoor et al and Zhang et al teaches a non-transitory computer-readable storage medium comprising instructions that, when executed by one or more processors of a computing system, cause the computing system to perform operations comprising: receiving, at a server system, a free-form query, wherein the free-form query includes a plurality of facets; identifying history records from a database associated with the free-form query to identify selected history records; determining a length of the history records; in response to determining that the history records exceed a predetermined length; dividing the history record into a set of chunks; generating, for each chunk of the set of chunks, one or more intermediate summaries of the chunk, wherein each intermediate summary of the one or more intermediate summaries corresponds to a different facet of the plurality of facets; ; and generating, from the free-form query, a plurality of query prompts using the one or more summaries, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets; in response to determining the history records are below the predetermined length: generating from the free-form query, the plurality of query prompts, wherein each query prompt of the plurality of query prompts is specific to a facet of the plurality of facets; providing the plurality of query prompts and the history records to one or more large language models; receiving, from the one or more large language models, language responses for each of the plurality of query prompts; and aggregating the language responses using a second large language model to generate a summary of the language responses , as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 17. The non-transitory computer-readable storage medium of claim 16, Liu et al, Defoor et al and Zhang et al teaches wherein the selected history records are selected from the database either randomly or using a search algorithm, as similarly explained in the rejection of claim 10, and is rejected under similar rationale.
With regards to claim 19. The non-transitory computer-readable storage medium of claim 16, Liu et al, Defoor et al and Zhang et al teaches wherein when executed by one or more processors of a computing system, the instructions cause the computing system to perform operations comprising: generating a plurality of individual summaries for sets of history records numbering less than or equal to a threshold number; processing the plurality of individual summaries using an additional large language model different than the one or more large language models to generate an aggregated summary for data of the sets of history records, as similarly explained in the rejection of claim 5, and is rejected under similar rationale.
With regards to claim 20. The non-transitory computer-readable storage medium of claim 16, Liu et al, Defoor et al and Zhang et al teaches wherein when executed by one or more processors of a computing system, the instructions cause the computing system to perform operations comprising: generating a summary query and an aggregation query from the free-form query; generating a plurality of individual summaries for sets of history records including the individual summaries using the summary query; and processing the plurality of individual summaries using the aggregation query and an additional large language model, as similarly explained in the rejection of claim 6, and is rejected under similar rationale.
With regards to claim 21, which depends on claim 1, the combination of Liu et al, Defoor et al and Zhang et al teaches identifying the plurality of facets from [associated stored data], as similarly explained in the rejection of claim 1, and rejected under similar rationale.
However although the combination teaches facets could be identified from associated stored data, the combination does not expressly teach the stored data comprises data of a two-way conversation including completed or resolved conversations.
Yet Liu et al teaches … it is known that stored data can comprise two-way conversation including completed or resolved conversations (paragraph 0074: prior stored transcript data includes conversation/meeting data between two or more participants (and are interpreted as the claimed ‘completed’ since it has already happened/occurred).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Liu et al, Defoor et al and Zhang et al’s ability to identify a query to be associated with facets/categories of data, such that the type of categories would have included completed/occurred two-way conversation data, as also taught by Liu et al. The combination would have flexibly allowed additional recognition conversation data to be subject to response data.
With regards to claim 22, which depends on claim 1, the combination of Liu et al, Defoor et al and Zhang et al teaches processing the language responses … aggregating …, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination explained in the rejection of claim 1 does not specifically address processing the language responses using a chunking algorithm, … aggregating the chunked language responses.
Yet Liu et al teaches processing the language responses using a chunking algorithm, … aggregating the chunked language responses (paragraph 0078: each information (language response) corresponding to each chunk is then aggregated /merged into a single chunk).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Liu et al, Defoor et al and Zhang et al’s ability to process the language responses from an aggregation, such that the responses could have been chunked, as also taught by Liu et al. The combination would have allowed a more fine tooth approach for selectively identifying specific responses to designate for subsequent processing.
Claims 23 and 24 is/are rejected under 35 U.S.C. 103 as being unpatentable over Liu et al (US Application: US 20240177084, published: May 30, 2024, filed: Sep. 29, 2023) in view of DeFoor et al (US Patent: 11972223, issued: Apr. 30, 2024, filed: Jul. 31, 2023) in view of Zhang (US Application: US 2025/0190449, published: Jun. 12, 2025, filed: Dec. 11, 2023) in view of Gottlob et al (US Application: US 2025/0045256, published: Feb. 6, 2025, filed: Aug. 2, 2024, EEFD: Aug. 4, 2023).
With regards to claim 23, which depends on claim 1, the combination of Liu et al, Defoor et al and Zhang et al teaches one or more summaries … the second large language model, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach identifying, from one or more summaries, one or more conflicts, and generating, using … large language model, a summary resolution that resolves the conflicts.
Yet Gottlob et al teaches identifying, from one or more [results/answers], one or more conflicts, and generating, using … large language model, a summary resolution that resolves the conflicts (paragraphs 0085, 0180: answers are checked and conflicts can be resolved through filtering to produce remaining answer(s). Furthermore the LLM is adaptive to the results (LLM behavior is updated)).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Liu et al, Defoor et al and Zhang et al’s ability to generate one or more output summaries of LLM responses using a summarizing-output LLM (second LLM), such that the outputs are further checked for conflicts and resolves conflicts through result filtering and update of the output-LLM accordingly ( based on the results), as taught by Gottlob et al. The combination would have allowed Liu et al and DeFoor et al to have produced accurate factual information by identifying incorrect data (Gottlob et al, Abstract, paragraph 0028).
With regards to claim 24, which depends on claim 23, the combination of Liu et al, DeFoor et al, Zhang et al and Gottlob et al teaches further comprising updating the second large language model based on the one or more conflicts, as similarly explained in the rejection of claim 23, and is rejected under similar rationale.
Response to Arguments
Applicant's arguments filed 07/24/2026 have been fully considered but they are not persuasive.
The applicant argues that claims are now in condition for allowance in view of the latest amendments to the claims. This argument is not persuasive, since the amendments have changed the scope of the invention and a new reference is applied to address the claims of the instant application. The examiner respectfully directs applicant’s attention to the new grounds of prior art rejection applied above and also the 35 USC 112 rejections that are introduced as a result of the latest amendments to the independent claims.
NOTE
The examiner suggests an interview to discuss: 1) identifying a facet first such that one or more chunks belong to the identified facet, 2) when there are a plurality of summaries belonging to a specific facet, they are merged prior to being used in generating/modifying the free form query (see paragraph 0134), 3) generation of intermediate prompts to generate the intermediate summar(ies), such that content associated with the prompts are each summarized to produce each of the intermediate summaries, 4) the conditional claim recitation(s) in the method claim, resulting in ‘contingent limitations’ /step(s) that only need to be executed/performed when a condition is met or satisfied and thus affecting BRI. In other words, the BRI of a process claim reciting actions that are performed only when a condition precedent is met requires only those steps that must be performed and does not include steps that are not required to be performed because the condition precedent is not met.
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 WILSON W TSUI whose telephone number is (571)272-7596. The examiner can normally be reached Monday - Friday 9 am -6 pm.
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/WILSON W TSUI/Primary Examiner, Art Unit 2172
