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 .
Status of Claims
Claims 1-20 are pending and are examined herein.
Claims 1-20 are rejected under 35 U.S.C. 101.
Claims 1-20 are rejected under 35 U.S.C. 103.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
MPEP § 2109(III) sets out steps for evaluating whether a claim is drawn to patent-eligible subject
matter. The analysis of claims 1-20, in accordance with these steps, follows.
Step 1 Analysis:
Step 1 is to determine whether the claim is directed to a statutory category (process, machine,
manufacture, or composition of matter. Claims 1-7 are directed to a process, claims 8-14 are directed to a machine, and claims 15-20 are directed to an article of manufacture. All claims are directed to statutory categories and analysis proceeds.
Step 2A Prong One, Step 2A Prong Two, and Step 2B Analysis:
Step 2A Prong One asks if the claim recites a judicial exception (abstract idea, law of nature, or natural phenomenon). If the claim recites a judicial exception, analysis proceeds to Step 2A Prong Two, which asks if the claim recites additional elements that integrate the abstract idea into a practical application. If the claim does not integrate the judicial exception, analysis proceeds to Step 2B, which asks if the claim amounts to significantly more than the judicial exception. If the claim does not amount to significantly more than the judicial exception, the claim is not eligible subject matter under 35 U.S.C. 101.
None of the claims represent an improvement to technology.
Regarding claim 1, the following are abstract ideas:
determining that an error occurred with respect to the request; (Determining that an error occurred can be practically performed in the human mind. This is a mental process.)
determining an error context for the error, the error context comprising information describing the error and information describing the source system; (Determining an error context can be practically performed in the human mind. This is a mental process.)
… predict a knowledge resource for resolving the error based on the error context; (Predicting a knowledge resource for resolving the error can be practically performed in the human mind. This is a mental process.)
The following claim elements are additional elements which, taken alone or in combination with
the other additional elements, do not integrate the judicial exception into a practical application nor
amount to significantly more than the judicial exception:
A computer-implemented method, comprising: (This recites a generic computer at a high level of generality. This amounts to mere instructions to apply an exception.)
receiving, from a source system, a request to post data to a target system; (Receiving data is an existing process on computers. This amounts to mere instructions to apply an exception.)
providing, as an input to a machine learning model, a representation of the error context, wherein the machine learning model is configured to (This recites an existing and generic machine learning process of inputting data into a model. This amounts to mere instructions to apply an exception.)
receiving, from the machine learning model, a prediction indicating the knowledge resource for resolving the error; and (Receiving data is a generic and existing process on computers. This amounts to mere instructions to apply an exception.)
providing, via a user interface, a recommendation to apply the knowledge resource on the at least one of the source system or the target system based on the prediction. (Outputting data is an insignificant extra-solution activity. See MPEP § 2106.05(d), list 3, ex. iv.)
Regarding claim 2, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein providing the representation of the error context comprises:
initiating a call to an incident solution matching service using a technical user identifier; and (Initiating a call to a service, for example, creating an API call, is a generic and existing process in computing. This amounts to mere instructions to apply an exception.)
providing the representation of the error context in response to authenticating the technical user identifier. (Outputting data is an insignificant extra-solution activity. See MPEP § 2106.05(d), list 3, ex. iv.)
Regarding claim 3, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the machine learning model is trained by:
providing a historical set of incident reports generated for a plurality of source systems to a machine learning algorithm, wherein the historical set of incident reports indicates errors that occurred with respect to the target system; and (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
providing a set of knowledge resources that resolved the errors to the machine learning algorithm, the machine learning algorithm generating the machine learning model based on the historical set of incident reports and the set of knowledge resources. (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
Regarding claim 4, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
receiving a sentiment report indicating whether the recommended knowledge resource was successful; and (Receiving data is a generic and existing process on computers. This amounts to mere instructions to apply an exception.)
re-training the machine learning model based on the sentiment report. (This recites the existing machine learning process of re-training a model at a high level of generality. This amounts to mere instructions to apply an exception.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the recommended knowledge resource comprises at least one of:
a set of instructions for rectifying the error at one or more of the source system or the target system; (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
a software patch to be applied at one or more of the source system or the target system; or (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
knowledge base articles comprising solutions for rectifying the error at one or more of the source system or the target system. (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
Regarding claim 6, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the data comprises a document pertaining to a financial transaction initiated at the source system. (This is the insignificant extra-solution activity of selecting a particular data source or type of data to be manipulated. See MPEP § 2106.05(g), ‘Selecting a particular data source or type of data to be manipulated’, ex. i. – iv.)
Regarding claim 7, the rejection of claim 1 is incorporated herein. The following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
wherein the machine learning model is an unsupervised machine learning model. (This recites the existing machine learning concept of unsupervised learning at a high level of generality. This amounts to mere instructions to apply an exception.)
Regarding claim 8, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A system, comprising: (This recites a generic system at a high level of generality. This amounts to mere instructions to apply an exception.)
a memory; and (This recites a generic computer component at a high level of generality. This amounts to mere instructions to apply an exception.)
at least one processor coupled to the memory and configured to: (This recites a generic computer component at a high level of generality. This amounts to mere instructions to apply an exception.)
The remainder of claim 8 recites substantially similar subject matter to claim 1, and is rejected with the same rationale, mutatis mutandis.
Claims 9-14 recite substantially similar subject matter to claims 2-7 respectively, and are rejected with the same rationales, mutatis mutandis.
Regarding claim 15, the following claim elements are additional elements which, taken alone or in combination with the other additional elements, do not integrate the judicial exception into a practical application nor amount to significantly more than the judicial exception:
A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations, the operations comprising: (This recites a generic computer component and generic computer processes at a high level of generality. This amounts to mere instructions to apply an exception.)
The remainder of claim 15 recites substantially similar subject matter to claim 1, and is rejected with the same rationale, mutatis mutandis.
Claims 16-20 recite substantially similar subject matter to claims 2-6 respectively, and are rejected with the same rationales, mutatis mutandis.
Claim Rejections - 35 USC § 103
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.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 1, 3, 5, 8, 10, 12, 15, 17, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over US 2019/0163594 A1, hereinafter “Hayden”.
Regarding claim 1, Hayden teaches
A computer-implemented method, comprising: ([0004] states "In one illustrative embodiment, a method is provided in a data processing system comprising at least one processor and a memory comprising instructions, for identifying and resolving issues in a distributed infrastructure.")
receiving, from a source system, a request to post data to a target system; ([0089] states "Many log monitoring tools exist. An agent runs on the nodes within the environment and monitors the log files. The agent notifies an application whenever a log file changes or whenever an error is detected in a log. In accordance with the illustrative embodiment, the log monitoring agent communicates with the system whenever an error message is identified, along with the log name and location, node name and Internet protocol (IP) address, the error message found, the time and date that it was recorded, etc." The nodes that the agent are running on are interpreted as the target systems, and the applications that communicate with the node are interpreted as the source systems. As one of ordinary skill in the art would infer, the logs monitor communication between the applications and the nodes, meaning that the logs monitor the applications’ requests to post data to the nodes. Fig. 5 shows an example of a log, where an SQL error occurs as a result of an application attempting to post data to the target system (the node hosting the SQL server).)
determining that an error occurred with respect to the request; ([0135] states "The system then determines whether an error is encountered (block 1205). Any log monitoring tool can be used. The log monitoring agent running on the target nodes will communicate with the system after errors are identified, providing information like node name, log name, log location, time of occurrence, etc. There may be a single error on a single node or many cascading errors across logs throughout the system. This data will be used as input to the model (or collection of models)."
determining an error context for the error, the error context comprising information describing the error and information describing the source system; ([0135 states "The log monitoring agent running on the target nodes will communicate with the system after errors are identified, providing information like node name, log name, log location, time of occurrence, etc. There may be a single error on a single node or many cascading errors across logs throughout the system. This data will be used as input to the model (or collection of models)." These data points are interpreted as the error context.)
providing, as an input to a machine learning model, a representation of the error context, wherein the machine learning model is configured to predict a knowledge resource for resolving the error based on the error context; ([0134] states "The goal of the model is to take receive as input an error scenario present in the system and to return a list of potential solution documents along with a confidence of how likely that solution is correct for the given error state.")
receiving, from the machine learning model, a prediction indicating the knowledge resource for resolving the error; and ([0135] states "The model returns a number of potential solutions. Each potential solution may have an associated confidence score.")
providing, via a user interface, a recommendation to apply the knowledge resource on the at least one of the source system or the target system based on the prediction. ([0137] states "In response to the system determining not to automatically resolve the error, the system provides the potential solutions to a user, such as a system administrator (bock 1209)." A user interface is necessary for the potential solutions to be provided to a user. Example solutions are given in fig. 8, which include solutions (knowledge resources) that are applied to the source system/node.)
Regarding claim 3, the rejection of claim 1 is incorporated herein. Hayden teaches
wherein the machine learning model is trained by:
providing a historical set of incident reports generated for a plurality of source systems to a machine learning algorithm, wherein the historical set of incident reports indicates errors that occurred with respect to the target system; and ([0087] states "Machine learning (ML) model 610 is trained based on summarized problem descriptions 601 and summarized solutions 602." [0066] states "Document preprocessor 410 receives data sources including runbooks 401, user problem reports 402, resolution records 403, error logs 404, and technical reference material 405. Problem reports 402, resolution documents 403, and error logs 404 are sources for the system, providing the user problem reports and linked error logs 404 and resolution document 403. Runbooks 401 are documents provided by the development team of an application running within the deployment environment. Runbooks 401 describe potential errors and their associated solutions. Technical reference material 405 may include user manuals for the application running in the environment or the servers hosting those applications." The error logs are interpreted as the historical set of incident reports. The nodes that the agent are running on are interpreted as the target systems, and the applications that communicate with the node are interpreted as the source systems. As one of ordinary skill in the art would infer, the logs monitor communication between the applications and the nodes, meaning that the logs monitor the applications’ requests to post data to the nodes. Fig. 5 shows an example of a log, where an SQL error occurs as a result of an application attempting to post data to the target system (the node hosting the SQL server.)
providing a set of knowledge resources that resolved the errors to the machine learning algorithm, the machine learning algorithm generating the machine learning model based on the historical set of incident reports and the set of knowledge resources. ([0087] states "Machine learning (ML) model 610 is trained based on summarized problem descriptions 601 and summarized solutions 602." [0066] states "Document preprocessor 410 receives data sources including runbooks 401, user problem reports 402, resolution records 403, error logs 404, and technical reference material 405. Problem reports 402, resolution documents 403, and error logs 404 are sources for the system, providing the user problem reports and linked error logs 404 and resolution document 403. Runbooks 401 are documents provided by the development team of an application running within the deployment environment. Runbooks 401 describe potential errors and their associated solutions. Technical reference material 405 may include user manuals for the application running in the environment or the servers hosting those applications." The resolution documents are interpreted as the set of knowledge resources that resolved the errors. As the model is trained on the summarized documents, the machine learning algorithm generates the model using the summarized documents, including the incident reports and knowledge records.)
Regarding claim 5, the rejection of claim 1 is incorporated herein. Hayden teaches
wherein the recommended knowledge resource comprises at least one of:
a set of instructions for rectifying the error at one or more of the source system or the target system; a software patch to be applied at one or more of the source system or the target system; or knowledge base articles comprising solutions for rectifying the error at one or more of the source system or the target system. (Fig. 8 shows phrases from the potential knowledge resource, which comprise a set of instructions for rectifying the error at the node, which is the target system.)
Regarding claim 8, Hayden teaches
A system, comprising: ([0006] states "In yet another illustrative embodiment, a system / apparatus is provided. The system / apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.")
a memory; and ([0006] states "In yet another illustrative embodiment, a system / apparatus is provided. The system / apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.")
at least one processor coupled to the memory and configured to: ([0006] states "In yet another illustrative embodiment, a system / apparatus is provided. The system / apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.")
The remainder of claim 8 recites substantially similar subject matter to claim 1, and is rejected with the same rationale, mutatis mutandis.
Claims 10 and 12 recite substantially similar subject matter to claims 3 and 5 respectively, and are rejected with the same rationales, mutatis mutandis.
Regarding claim 15, Hayden teaches
A non-transitory computer-readable device having instructions stored thereon that, when executed by at least one computing device, causes the at least one computing device to perform operations, the operations comprising: ([0006] states "In yet another illustrative embodiment, a system / apparatus is provided. The system / apparatus may comprise one or more processors and a memory coupled to the one or more processors. The memory may comprise instructions which, when executed by the one or more processors, cause the one or more processors to perform various ones of, and combinations of, the operations outlined above with regard to the method illustrative embodiment.")
The remainder of claim 15 recites substantially similar subject matter to claim 1, and is rejected with the same rationale, mutatis mutandis.
Claims 17 and 19 recite substantially similar subject matter to claims 3 and 5 respectively, and are rejected with the same rationales, mutatis mutandis.
Claim(s) 2, 6, 9, 12, 16, and 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayden as applied to claim 1 above, and further in view of US 2025/0094476 A1, hereinafter “Ledley”.
Regarding claim 2, the rejection of claim 1 is incorporated herein. Hayden does not appear to explicitly teach
wherein providing the representation of the error context comprises:
initiating a call to an incident solution matching service using a technical user identifier; and
providing the representation of the error context in response to authenticating the technical user identifier
However, Ledley—directed to analogous art—teaches
wherein providing the representation of the error context comprises:
initiating a call to an incident solution matching service using a technical user identifier; and (Fig. 3A shows different systems involved in the process. The secure server and the computing server are interpreted as the incident solution matching service. Reference number 316 shows that the incident solution matching service matches the documentation to real-time transactions. [0072] states "The third-party server 180, having received the automatically generated documentation record from the transaction terminal 150, transmits 334 a communication payload to the secure server 190. The communication payload may include the automatically generated documentation record received by the third-party server 180 from the transaction terminal 150 but may also include other irrelevant information." As the communication payload is transferred, a person of ordinary skill in the art would infer that a call is initiated. [0073] states "Having previously obtained 322 access privilege from the third-party server 180, the secure server 190 receives 324 the communication payload. Access privilege includes the rights and abilities assigned to a specific authorized user account." Thus, one of ordinary skill in the art would infer that a user identifier of the user account was used in the call.)
providing the representation of the error context in response to authenticating the technical user identifier. ([0073] states "Having previously obtained 322 access privilege from the third-party server 180, the secure server 190 receives 324 the communication payload. Access privilege includes the rights and abilities assigned to a specific authorized user account." Thus, after authenticating the account and obtaining the access privilege, the secure server is provided the representation of the error context (the communication payload.))
It would have been obvious to a person having ordinary skill in the art before the effective filing date of this application to combine the teachings of Hayden and Ledley because, as Ledley states in [0055], "A secure server 190 may serve as an intermediate server that perform filtering of data sent from a third-party server 180 before the data is provided to the computing server 110. For example, an organization client of computing server 110 may grant unlimited access privilege of certain data of the organization to the computing server 110. To secure and safeguard sensitive data of the organization, the computing server 110 may set up a separate secure server 190 to conduct certain types of filtering so that more relevant data are sent to the computing server 110. Sensitive yet irrelevant data may be filtered by the secure server 190. In some embodiments, a secure server 190 may not be present and any actions or capabilities of the secure server 190 may also be performed by the computing server 110." Additionally, one of ordinary skill in the art would understand that authentication is necessary to protect client data.
Regarding claim 6, the rejection of claim 1 is incorporated herein. Hayden does not appear to explicitly teach
wherein the data comprises a document pertaining to a financial transaction initiated at the source system.
However, Ledley—directed to analogous art—teaches
wherein the data comprises a document pertaining to a financial transaction initiated at the source system. ([0028] states "In various embodiments, a third-party named entity 170 may automatically generate a documentation record to document an occurred transaction. The documentation record, which may also simply be referred to as a record, may be generated by the transaction terminal 150 or a server of the named entity. A documentation record serves as a record of a transaction between a named entity and an end user.")
It would have been obvious to a person having ordinary skill in the art before the effective filing date of this application to combine the teachings of Hayden and Ledley for the reasons given above in regards to claim 2.
Claims 9 and 13 recite substantially similar subject matter to claims 2 and 6 respectively, and are rejected with the same rationales, mutatis mutandis.
Claims 16 and 20 recite substantially similar subject matter to claims 2-7 respectively, and are rejected with the same rationales, mutatis mutandis.
Claim(s) 4, 11, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayden as applied to claim 1 above, and further in view of US 2024/0154877 A1, hereinafter “Ajayaghosh”.
Regarding claim 4, the rejection of claim 1 is incorporated herein. Hayden does not appear to explicitly
receiving a sentiment report indicating whether the recommended knowledge resource was successful; and
re-training the machine learning model based on the sentiment report.
However, Ajayaghosh—directed to analogous art—teaches
receiving a sentiment report indicating whether the recommended knowledge resource was successful; and ([0051] – [0054] state "If there is an incident ticket in the queue, in step 320, the computer program may provide details from the incident ticket to a trained incident response machine learning engine, which may predict a solution to the incident. [0052] In step 325, the computer program may retrieve information for the solution from, for example, a knowledge base. The information for the predicted solution may include solution instructions, scripts, patches, configurations, solution articles, etc. [0053] In step 330, the computer program may update incident ticket with the solution information for the predicted solution, and may provide the solution information to the incident ticket submitter. [0054] In step 335, the computer program may receive feedback from the incident ticket submitter as to the effectiveness of the solution. The feedback may be provided manually by the ticket submitter, may be automatically captured (e.g., metrics from the computer program or system indicate that the solution was successful), etc.." The feedback is interpreted as the sentiment report.)
re-training the machine learning model based on the sentiment report. ([0055] states "The feedback may be used to re-train or update the incident response machine learning engine as needed.")
It would have been obvious to a person having ordinary skill in the art before the effective filing date of this application to combine the teachings of Hayden and Ajayaghosh because, as stated by Ajayaghosh in [0031] "Embodiments may monitor the performance of the model to proactively improve the accuracy rate of the solutions identified by the model."
Claims 11 and 18 recite substantially similar subject matter to claim 4 and are rejected with the same rationale, mutatis mutandis.
Claim(s) 7 and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hayden as applied to claim 1 above, and further in view of US 2024/0202530 A1, hereinafter “Meng”.
Regarding claim 7, the rejection of claim 1 is incorporated herein. Hayden does not appear to explicitly teach
wherein the machine learning model is an unsupervised machine learning model.
However, Meng—directed to analogous art—teaches
wherein the machine learning model is an unsupervised machine learning model. ([0017] states "In view of the need for systems and methods for unsupervised training in text retrieval tasks, embodiments described herein provide systems and methods for an unsupervised training mechanism for dense retrievers.")
It would have been obvious to a person having ordinary skill in the art before the effective filing date of this application to combine the teachings of Hayden and Meng because, as Meng states in [0003]-[0004], "Machine learning systems have been widely used for text retrieval tasks, often referred to as dense retrievers. Existing methods for training dense retriever models mostly rely on training with a large amount of annotated data consisting of documents and associated queries generated by human annotators, which is prohibitively costly. On the other hand, many datasets with potential search interests are not annotated with search queries corresponding to its documents. [0004] Therefore, there is a need for systems and methods for unsupervised training in text retrieval tasks."
Claim 14 recites substantially similar subject matter to claim 7, and is rejected with the same rationale, mutatis mutandis.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JESSICA THUY PHAM whose telephone number is (571)272-2605. The examiner can normally be reached Monday - Friday, 9 A.M. - 5:00 P.M..
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/J.T.P./ Examiner, Art Unit 2121
/Li B. Zhen/ Supervisory Patent Examiner, Art Unit 2121
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