Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
2. This communication is responsive to the RCE filed on 05/26/2026.
3. Claims 1-20 are currently pending in this Office action.
Claim Rejections - 35 USC § 112
4. The 35 U.S.C. 112 second paragraph rejections made in the prior Office action are withdrawn. However, new 112 rejections are presented as shown below:
5. 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.
6. Claims 1-8 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.
Regarding claim 1, this claim reciters the newly added limitation of “wherein identify anomalous activity based on the second data query, and perform, by a security application, a remedial action based on the anomalous activity.” It is noted that the specification discloses in paragraphs 0201-0203 that “… Furthermore, query conversion service 2930 can be utilized in a manner that improves security by identifying anomalous activity and presenting it in a manner usable by a security developer user or a security application for implementation of remedial actions.” In view of this disclosure, it appears that the claimed identifying and performing steps are to be performed as part of query conversion. Thus, it is unclear and clarification is required.
Claims not specifically mentioned above are rejected by virtue of their dependency on a rejected claim.
Claim Rejections - 35 USC § 101
7. Applicant’s arguments regarding the 35 U.S.C. 101 rejection made in the prior Office action are persuasive. The previously issued 35 U.S.C. 101 rejection is withdrawn.
Claim Rejections - 35 USC § 103
8. In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
9. 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.
10. Claims 1-7 are rejected under 35 U.S.C. 103 as being unpatentable over
U.S. 2025/0086391 (hereinafter Kempf) in view of U.S. 2024/0354321 (hereinafter Kundel), and further in view of U.S. 10,534,925 (hereinafter Israel).
Regarding claim 1, as far as the claim is understood, Kempf discloses a system comprising:
a processor; and a memory that stores program code structured to cause the processor to [0097]:
receive a first request associated with querying a database, the first request comprising a first natural language question ([0030]; “…The data processing system 200 may convert a user query 245 (also referred to as a natural language input or a plain-text query) into a vector-based object and compare the user query 245 with the token search index and/or the vector search index to identify a correlation between the user query 245 and one or more passages 235…”);
determine a function call of a database engine based at least on the first request ([0040 and 0061]; “At 810, the prediction and execution service 210 may convert a natural language input (such as the user query 245 described with reference to FIG. 2) into a vector by using a text embedding function to process one or more tokens in the natural language input...”),
generate, based at least on the first request and the function call, a first prompt comprising: the first natural language question, and instructions to translate language of the first natural language question into a query language ([0027, 0050 and 0063]; “At 820, the prediction and execution service 210 may generate a prompt that includes tokens from the natural language input, tokens from one or more of the chunks retrieved from the datastore 805, and instructions for generating a response to the natural language input…),
provide the first prompt to a generative artificial intelligence model [i.e., generative AI to formulate search answers], causing the generative artificial intelligence model to:
determine, based at least on the first natural language question, additional information is to be determined to translate the language of the first natural language question into the query language, processing the additional information ([0060-0061 and 0064]; fig. 8; “At 815, the prediction and execution service 210 may retrieve a set of chunks (e.g., passages 235) from the datastore 805 in association with using one or more search indexes (such as a dense search index and a sparse search index) to compare the vector and/or the one or more tokens from the natural language input to vectors and tokens in the set of chunks”; and “…The LLM vendor 615 may communicate with the LLM gateway 610 via a secure communication channel. Once the prompt is successfully received… the LLM 620 may analyze/process the various chunks of tenant-specific information in the prompt and formulate a query response 250 based on the instructions provided by the prediction and execution service 210…”),
responsive to providing the data query to the database engine, receive, from the database engine, determined information comprising the additional information ([0065]; “At 830, the LLM vendor 615 may return the query response 250 to the prediction and execution service 210 (via the LLM gateway 610). At 835, the LLM gateway 610 may verify the contents of the response 250 before it is returned to the user. In particular, the LLM gateway 610 may perform a relevancy check (to ensure the response is relevant to the original query) and confirm that the format of the response 250 and any citations therein conform to the instructions provided by the prediction and execution service 210…”),
provide the determined information to the generative artificial intelligence model, causing the generative artificial intelligence model to: generate an answer based at least on the determined information, and cause the second data query to be executed by the database engine ([0066-0067]; “…For example, the input module 910 may transmit input signals to the query handling manager 920 to support techniques for using generative AI to formulate search answers. In some cases, the input module 910 may be a component of an input/output (I/O) controller 1110 as described with reference to FIG. 11”).
While Kempf discloses the features of the generative AI formulating the search answers as explained above, the reference does not explicitly disclose the features of causing the generative artificial intelligence model to generate a first data query corresponding to the function call based at least on the additional information; and causing the generative artificial intelligence model to generate a second data query based at least on the determined information. However, such features are well known in the art as disclosed by Kundel ([0041-0047]; fig. 5 as shown below; “In some embodiments, block 520 may include operations illustrated in the callout portion of FIG. 5. More specifically, operations of block 520 may include generating a request for contextual data to the first NL generative model or a second generative model (operations 521) and may further include obtaining a response that includes the requested contextual data (operations 525)…For example, if the first NL generative model is lightweight GM 215, the second NL generative model may be GM 120 (or vice versa). In some embodiments, the first NL generative model and/or the second NL generative model may be or include a large language model (LLM)” and it would have been obvious for one with ordinary skill in the art to
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utilize the teachings of Kundel in the system of Kempf in view of the desire to enhance the natural language (NL) query process by utilizing a plurality of artificial intelligence generative models resulting in improving the efficiency of formulating the search answers.
Kempf in view of Kundel does not explicitly disclose the features of wherein identify anomalous activity [based on the second data query], and perform, by a security application, a remedial action based on the anomalous activity. However, Israel discloses that “…providing alerts and/or remedial actions when a suspicious system behavior is detected. For example, artificial intelligence models may be generated (e.g., classification models, anomaly detection models, etc.) using supervised learning techniques, for use in determining anomalous system events, based on events occurring during (distinguished) user active/inactive states… Security alerts may be provided (e.g., via email, text, display, audio alert, etc.) for determinations of compromised devices (e.g., as well as remedial actions).” (col. 2, lns. 49-col. 3, lns. 6) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Israel in the modified system of Kempf in view of the desire to enhance the natural language query process by utilizing the security alerting scheme resulting in improving the prompt generating process. Regarding the feature of identifying anomalous activity based on the second data query, the specific input utilized for identifying anomalous activity has been an obvious design choice to one with ordinary skill in the art depending on the needs of a particular application and involving only routine skill in the art.
Regarding claim 2, Kempf in view of Kundel and Israel disclose the system wherein the program code is further structured to cause the processor to:
receive a second request [i.e., an intermediate query] associated with querying the database, the second request comprising a second natural language question; provide a second prompt comprising the second natural language question to the generative artificial intelligence model, causing the generative artificial intelligence model to: determine the additional information is to be determined to translate language of the second natural language question into the query language, generate, based at least on the determined information, a [third] data query; and causing the third data query to be executed by the database engine (Kempf: [0017 and 0034]) and (Kundel: [0014, 0022, 0035 and 0046-0047]).
While Kempf in view of Kundel discloses the feature of accessing a prompt history log to determine the determined information (Kundel: [0030 and 0042]; “GM 120 may process the intermediate query (operation 218) and generate a response to listing some of the contextual data that GM 120 may find useful for answering user query 212, e.g., “the dates of the Spring Break week (or the school the user is attending), the number of days the user plans to travel, the user's budget, the user's prior travels over the last one, two, etc., years,” and/or the like…”; and “…In some embodiments, the contextual data may include a record of prior activities of the user of a type referenced in the NL query…”), the references do not explicitly disclose the claim limitation of generating a third data query. However, the specific scheme and configuration utilized to process data would have been obvious to one of ordinary skill in the art in view of meeting different design requirements and achieving the particular desired performance.
Regarding claim 3, Kempf in view of Kundel and Israel disclose the system wherein the first prompt further causes the generative artificial intelligence model to generate a query plan comprising the first data query and a variable version of the second data query, the variable version specifying a response of the first data query is to be passed as an argument of the variable version (Kempf: [0017]) and (Kundel: [0046-0047]; fig. 5). Therefore, the limitations of claim 3 are rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claim 4, Kempf in view of Kundel and Israel disclose the system wherein to determine the function call, the program code is further structured to cause the processor to:
determine a schema of the database engine based at least on a configuration setting of the system; and identify the function call based at least on the schema (Kempf: [0027 and 0040]) and (Kundel: [0014]). Therefore, the limitations of claim 4 are rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claim 5, Kempf in view of Kundel and Israel disclose the system wherein to determine the function call, the program code is further structured to cause the processor to:
determine a schema of the database engine based on the first natural language question; and identify the function call based at least on the schema and the first natural language question (Kempf: [0037 and 0040]) and (Kundel: [0024, 0027 and 0029]). Therefore, the limitations of claim 5 are rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claim 6, Kempf in view of Kundel and Israel disclose the system wherein to identify the function call, the program code is further structured to cause the processor to:
identify a plurality of function calls based on the schema, the plurality of function calls comprising the function call; and select the function call from the plurality of function calls based at least on the first natural language question (Kempf: [0040 and 0052]) and (Kundel: [0042-0043]). Therefore, the limitations of claim 6 are rejected in the analysis of claim 1, and the claim is rejected on that basis.
Regarding claim 7, Kempf in view of Kundel and Israel disclose the system wherein the program code is further structured to cause the processor to:
validate the second data query, resulting in a validated version of the second data query, wherein to cause the second data query to be executed, the program code is further structured to cause the processor to cause the validated version to be executed by the database engine (Kempf: [0034 and 0044]) and (Xu: [0082-0083]). Therefore, the limitations of claim 7 are rejected in the analysis of claim 1, and the claim is rejected on that basis.
11. Claims 9-10, 12-15 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Kempf in view of Kundel.
Regarding claim 9, Kempf discloses a method comprising:
receiving a first request associated with querying a service for data or for an operation to be performed with respect to the data, the first request comprising a first natural language question ([0030]; “…The data processing system 200 may convert a user query 245 (also referred to as a natural language input or a plain-text query) into a vector-based object and compare the user query 245 with the token search index and/or the vector search index to identify a correlation between the user query 245 and one or more passages 235…”);
providing a first prompt comprising the first natural language question to a generative artificial intelligence model, causing the generative artificial intelligence model to:
determine, based at least on the first natural language question, additional information is to be determined to translate a language of the first natural language question to a query language ([0060-0061 and 0064]; fig. 8; “At 815, the prediction and execution service 210 may retrieve a set of chunks (e.g., passages 235) from the datastore 805 in association with using one or more search indexes (such as a dense search index and a sparse search index) to compare the vector and/or the one or more tokens from the natural language input to vectors and tokens in the set of chunks”; and “…The LLM vendor 615 may communicate with the LLM gateway 610 via a secure communication channel. Once the prompt is successfully received… the LLM 620 may analyze/process the various chunks of tenant-specific information in the prompt and formulate a query response 250 based on the instructions provided by the prediction and execution service 210…”), and
receiving the first data query from the generative artificial intelligence model ([0064-0065]; “At 830, the LLM vendor 615 may return the query response 250 to the prediction and execution service 210 (via the LLM gateway 610)…”); and
providing the first data query to the service ([0066-0067]; “…For example, the input module 910 may transmit input signals to the query handling manager 920 to support techniques for using generative AI to formulate search answers. In some cases, the input module 910 may be a component of an input/output (I/O) controller 1110 as described with reference to FIG. 11”).
While Kempf discloses the features of the generative AI formulating the search answers as explained above, the reference does not explicitly disclose the features of utilizing a first function call of the service to determine determined information comprising the additional information; and generating a first data query based at least on the determined information. However, such features are well known in the art as disclosed by Kundel ([0041-0047]; fig. 5 as shown below; “In some embodiments, block 520 may include operations illustrated in the callout portion of FIG. 5. More specifically, operations of block 520 may include generating a request for contextual data to the first NL generative model or a second generative model (operations 521) and may further include obtaining a response that includes the requested contextual data (operations 525)…For example, if the first NL generative model is lightweight GM 215, the second NL generative model may be GM 120 (or vice versa). In some embodiments, the first NL generative model and/or the second NL generative model may be or include a large language model (LLM)” and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Kundel in the system of Kempf in view of the desire to enhance the natural language (NL) query process by utilizing a plurality of artificial intelligence generative models resulting in improving the efficiency of formulating the search answers.
Regarding claim 10, Kempf in view of Kundel discloses the method wherein said providing the first prompt to the generative artificial intelligence model to cause the generative artificial intelligence model to generate the first data query comprises: causing the generative artificial intelligence model to generate the first data query as an unstructured string argument of a second function call of the service (Kempf: [0030 and 0071]).
Regarding claim 12, Kempf in view of Kundel discloses the method further comprising:
receiving a second request [i.e., an intermediate query] associated with querying the service, the second request comprising a second natural language question; provide a second prompt comprising the second natural language question to the generative artificial intelligence model, causing the generative artificial intelligence model to: determine, based at least on the second natural language question, accessing a prompt history log to determine the determined information, and generate a second data query based at least on the access of the prompt history log; receiving the second data query from the generative artificial intelligence model; and
causing the second data query to be provided to the service (Kempf: [0017 and 0034]) and (Kundel: [0014, 0022, 0035, 0046-0047 and 0065]). Therefore, the limitations of claim 12 are rejected in the analysis of claim 9, and the claim is rejected on that basis.
Regarding claim 13, Kempf in view of Kundel discloses the method wherein said providing the first prompt further causes the generative artificial intelligence model to generate a query plan comprising the first data query and a variable version of the second data query, the variable version specifying a response of the first data query is to be passed as an argument of the variable version (Kempf: [0017]) and (Kundel: [0046-0047]; fig. 5). Therefore, the limitations of claim 13 are rejected in the analysis of claim 9, and the claim is rejected on that basis.
Regarding claim 14, Kempf in view of Kundel discloses the method wherein the service comprises a database engine and said providing the first prompt to the generative artificial intelligence model further comprises:
causing the generative artificial intelligence model to determine a schema of the database engine based at least on the first natural language question; and causing the generative artificial intelligence model to identify the function call based at least on the schema (Kempf: [0027 and 0040]) and (Kundel: [0014]). Therefore, the limitations of claim 14 are rejected in the analysis of claim 9, and the claim is rejected on that basis.
Regarding claim 15, Kempf in view of Kundel discloses the method wherein said causing the generative artificial intelligence model to identify the function call further comprises:
causing the generative artificial intelligence model to identify a plurality of function calls based on the schema, the plurality of function calls comprising the function call; and
select the function call from the plurality of function calls based at least on the first natural language (Kempf: [0037 and 0040]) and (Kundel: [0024, 0027 and 0029]). Therefore, the limitations of claim 15 are rejected in the analysis of claim 9, and the claim is rejected on that basis.
Regarding claim 17, Kempf in view of Kundel discloses the method wherein the first data query corresponds to a translation of the language of the first natural language question to the query language (Kempf: [0027]).
Regarding claim 18, Kempf discloses a non-transitory computer-readable storage medium having programming instructions encoded thereon, the programming instructions structured to cause a processor to perform a method comprising:
receiving a first request associated with querying a service, the first request comprising a first natural language question ([0030]; “…The data processing system 200 may convert a user query 245 (also referred to as a natural language input or a plain-text query) into a vector-based object and compare the user query 245 with the token search index and/or the vector search index to identify a correlation between the user query 245 and one or more passages 235…”);
generating, based at least on the first request, a first prompt comprising the first natural language question and instructions to translate language of the first natural language question into query language ([0027, 0050 and 0063]; “At 820, the prediction and execution service 210 may generate a prompt that includes tokens from the natural language input, tokens from one or more of the chunks retrieved from the datastore 805, and instructions for generating a response to the natural language input…);
providing the first prompt to a generative artificial intelligence model, causing the generative artificial intelligence model to:
determine, based at least on the first natural language question, first additional information is to be determined to translate the language of the first natural language question ([0060-0061 and 0064]; fig. 8; “At 815, the prediction and execution service 210 may retrieve a set of chunks (e.g., passages 235) from the datastore 805 in association with using one or more search indexes (such as a dense search index and a sparse search index) to compare the vector and/or the one or more tokens from the natural language input to vectors and tokens in the set of chunks”; and “…The LLM vendor 615 may communicate with the LLM gateway 610 via a secure communication channel. Once the prompt is successfully received… the LLM 620 may analyze/process the various chunks of tenant-specific information in the prompt and formulate a query response 250 based on the instructions provided by the prediction and execution service 210…”),
access a prompt [history log comprising a historic prompt] provided to the generative artificial intelligence model prior to the first prompt ([0065]; “At 830, the LLM vendor 615 may return the query response 250 to the prediction and execution service 210 (via the LLM gateway 610). At 835, the LLM gateway 610 may verify the contents of the response 250 before it is returned to the user. In particular, the LLM gateway 610 may perform a relevancy check (to ensure the response is relevant to the original query) and confirm that the format of the response 250 and any citations therein conform to the instructions provided by the prediction and execution service 210…”),
determine, based at least on the first natural language question, second additional information is to be determined to translate the language of the first natural language question ([0065]; “At 830, the LLM vendor 615 may return the query response 250 to the prediction and execution service 210 (via the LLM gateway 610). At 835, the LLM gateway 610 may verify the contents of the response 250 before it is returned to the user. In particular, the LLM gateway 610 may perform a relevancy check (to ensure the response is relevant to the original query) and confirm that the format of the response 250 and any citations therein conform to the instructions provided by the prediction and execution service 210…”),
receiving the first data query from the generative artificial intelligence model ([0064-0065]; “At 830, the LLM vendor 615 may return the query response 250 to the prediction and execution service 210 (via the LLM gateway 610)…”); and
causing the first data query to be provided to the service ([0066-0067]; “…For example, the input module 910 may transmit input signals to the query handling manager 920 to support techniques for using generative AI to formulate search answers. In some cases, the input module 910 may be a component of an input/output (I/O) controller 1110 as described with reference to FIG. 11”).
While Kempf discloses the features of the generative AI formulating the search answers as explained above, the reference does not explicitly disclose the features of accessing a prompt history log comprising a historic prompt; determining, based at least on the historic prompt, the first additional information; utilizing a function call of the service to determine the second additional information; and generating a first data query based at least on the first additional information and the second additional information. However, such features are well known in the art as disclosed by Kundel ([0021 and 0041-0047]; fig. 5 as shown below; “In some embodiments, block 520 may include operations illustrated in the callout portion of FIG. 5. More specifically, operations of block 520 may include generating a request for contextual data to the first NL generative model or a second generative model (operations 521) and may further include obtaining a response that includes the requested contextual data (operations 525)…For example, if the first NL generative model is lightweight GM 215, the second NL generative model may be GM 120 (or vice versa). In some embodiments, the first NL generative model and/or the second NL generative model may be or include a large language model (LLM)” and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Kundel in the system of Kempf in view of the desire to enhance the natural language (NL) query process by utilizing a plurality of artificial intelligence generative models resulting in improving the efficiency of formulating the search answers.
12. Claims 11 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kempf in view of Kundel, and further in view of U.S. 2024/0404687 (hereinafter Bell).
Regarding claim 11, Kempf in view of Kundel discloses the method wherein said providing the first prompt to the generative artificial intelligence model to utilize a function call of the service comprises:
causing the generative artificial intelligence model to generate an application programming interface (API) call of the service based at least on the additional information to be determined, the API call corresponding to the function call; responsive to receiving the API call from the generative artificial intelligence model, providing the [modified] API call to the service; receiving, from the service, the determined information; and providing the determined information to the generative artificial intelligence model, causing the generative artificial intelligence model to generate the first data query (Kempf: [0040 and 0061]) and (Kundel: [0024 and 0041-0047]; fig. 1; “…Communications between QT 101 and AI server 122 may be facilitated by GM API 102. Communications between QT 101 and data store 110/DM 160 may be facilitated by DM API 104. Additionally, GM API 102 may translate various queries generated by QT 101 into unstructured natural-language format and, conversely, translate responses received from generative model 120 into any suitable form (including any structured proprietary format as may be used by QT 101)…”).
The references do not explicitly disclose the features of modifying the API call to generate a modified API call comprising a reference to a uniform resource identifier of the service; and providing the modified API call to the service. However, such features are well known in the art as disclosed by Bell ([0122, 0314, 0317, 0349 and 0379]; using conversation history as an optional basis, function calls as we all as user prompt guidance) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Bell in the modified system of Kempf in view of the desire to enhance the natural language (NL) query process by utilizing task-specific machine learning agents resulting in improving the efficiency of formulating the search answers.
Regarding claim 19, Kempf in view of Kundel discloses the non-transitory computer-readable storage medium wherein said providing the first prompt to the generative artificial model causing the generative artificial intelligence model to utilize a function call of the service to determine the second additional information comprises:
causing the generative artificial intelligence model to: determine the function call of the service suitable to determine the second additional information, and generate an application programming interface (API) call of the service based at least on the function call; responsive to receiving the API call from the generative artificial intelligence model, receiving, from the service, determined information comprising the second additional information; and providing the determined information to the generative artificial intelligence model (Kempf: [0040, 0061 and 0066]) and (Kundel: [0024 and 0041-0047]; fig. 1).
The references do not explicitly disclose the features of repairing an error in the API call to generate a repaired API call; and validating the repaired API call; subsequent to validating the repaired API call, providing the repaired API call to the service. However, such features are well known in the art as disclosed by Bell ([0109-0110, 0314, 0317, 0349 and 0379]; using conversation history as an optional basis, function calls as we all as user prompt guidance) and it would have been obvious for one with ordinary skill in the art to utilize the teachings of Bell in the modified system of Kempf in view of the desire to enhance the natural language (NL) query process by utilizing task-specific machine learning agents resulting in improving the efficiency of formulating the search answers.
Allowable Subject Matter
13. Claims 8, 16 and 20 are objected to as being dependent upon a rejected base claim, but would be allowable (1) if rewritten in independent form including all of the limitations of the base claim and any intervening claims; and (2) if the claim is amended to overcome the 35 U.S.C. 112 rejections as stated above.
Regarding claims 8, 16 and 20, although the prior art (Kempf et al., US 2025/0086391) discloses the system of utilizing the generative artificial intelligence for formulating search answers, it fails to disclose or make obvious the method comprising, in addition to the other recited features of the claim, the limitations of “determine the second data query comprises an error; generate a second prompt comprising an indication of the error and correction instructions; and the generative artificial intelligence model to generate a repaired version of the second data query based at least on the error message and the correction instructions in the manner recited in claims 8, 16 or 20.
Response to Arguments
14. Applicant’s arguments have been considered but are deemed to be moot in view of new grounds of rejection presented in this Office action. Kempf in view of Kundel and further in view of Israel or Bell disclose the applicant’s claimed invention as explained in the rejection above.
Conclusion
15. Any inquiry concerning this communication or earlier communications from the examiner should be directed to MONICA M PYO whose telephone number is (571)272-8192. The examiner can normally be reached Monday-Friday 8am-4pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, APU MOFIZ can be reached at 571-272-4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MONICA M PYO/ Primary Examiner, Art Unit 2161
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