DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . This office action is in response to communication filed 3/4/2026.
The instant application having application No. 18/391,803 filed on December 21, 2023, is a continuation of the application having application No. 17/305,027 (now issued patent 11,681,505) filed on June 29, 2021, and claims priority to PCT/US 22/35140 filed on June 27, 2022.
Status of the Claims
Claims 21 and 47-48 are amended, claims 22-23 were previously canceled, claim 49 is added. Accordingly, claims 21, and 24-49 are currently pending in the application.
Response to Amendment
Regarding 112 (b) rejections: Applicant amendments to claims 47-48 appropriately addressed the rejections, the 112 (b) rejections to claims 47-48 are withdrawn.
Regarding art rejection: In regards to pending claims Applicant’s arguments are not persuasive; further, Applicant's amendments necessitated new grounds of rejections presented in the following art rejection.
Examiner Notes
Examiner cites particular columns, paragraphs, figures and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in their entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
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 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.
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 of this title, 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 21, 24-28, 34, 38-43, and 47-48 are rejected under 35 U.S.C. 103 as being unpatentable over Polleri et al (US 20210081848 A1, hereinafter, “Polleri”, cited from IDS of 1/16/2024) in view of Allen et al. (US 20170220327 A1, hereinafter, “Allen”) and Delpech de Frayssinet et al. (US 20220276842 A1, hereinafter, “Frayssinet”).
With respect to claim 21 (Currently Amended), Polleri discloses A method for code or workflow generation using a query-based user interface (UI) implemented on a code generation system (e.g. para [0023], “FIG. 7 illustrates a flowchart for a technique for generating a machine learning application using a chatbot.” Wherein a chatbot reads on a query-based user interface),comprising:
processing a first input natural language response by the user to the initial natural language prompt using the code generation system (e.g. para [0214], “At 706, the technique can include determining an intent of a first input to create a machine learning architecture based at least in part on classifying the one or more text fragments. …”);
displaying the one or more additional bricks comprising the one or more additional natural language prompts on the UI to the user using the code generation system (e.g. para [0216], “At 710, the technique can present the correlated model to the user. The type of machine learning solution can be selected via the intelligent assistant (e.g., chatbot). …. In various embodiments, the model composition engine 132 can present multiple models to the user to select from.”), wherein the one or more additional bricks each comprise (i) a graphical representation presented via the query-based user interface of the code generation system, and (ii) a function or code that is configured to perform at least part of the workflow for the target application (e.g. para [0216], “… The possible machine learning solutions can include but are not limited to a classification model, a recommender model, or a reinforcement learning model. The model composition engine 132 can display the correlated model on an interface 104. …” wherein a classification model or a recommender model suggest a function and “display the correlated model on an interface” indicates graphical representation);
processing one or more additional input natural language responses by the user to the one or more additional natural language prompts using the code generation system, wherein the one or more additional input natural language responses are processed to refine, focus or tailor the generation of the workflow (e.g. para [0217], “At 712, the technique can include receiving a selection of the machine learning model where the selection is chosen from the one or more machine learning models. …. The interface 116 can receive the selection of the model and transfer the selection information to the model composition engine 132. ….”); and
automatically generating a set of code based at least in part on the first input natural language response or the one or more additional input natural language responses using the code generation system, wherein the set of code is associated with the workflow for the target application (e.g. para [0221], “At 718, the technique can include generating a plurality of code for a machine learning architecture. ….” Wherein a machine learning architecture reads on the workflow of the target application).
Polleri dos not appear to explicitly disclose
generating a brick comprising an initial natural language prompt configured to solicit a request from a user to generate a workflow for a target application using the code generation system;
determining, based at least in part on an analysis of the first input natural language response by the code generation system, whether additional information is to be obtained from the user;
generating one or more additional bricks comprising one or more additional natural language prompts upon determining that additional information is to be obtained from the user using the code generation system, wherein the one or more additional natural language prompts are configured to elicit the additional information from the user, and wherein additional information from the user helps the code generation system obtain an understanding of the workflow for the target application;
However, in analogous art, Allen discloses
determining, based at least in part on an analysis of the first input natural language response by the code generation system, whether additional information is to be obtained from the user (e.g. Fig. 5, steps 505 - 515, para [0042], “… The data management engine 216 may obtain the knowledgebase data based on the request. …”);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the invention of Polleri with the invention of Allen because it provides techniques that increase the efficiency of software developers as less time may be spent on generating basic code and more time may be spent on evaluating and developing software to handle complex problems. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of providing techniques that increase the efficiency of software developers as less time may be spent on generating basic code and more time may be spent on evaluating and developing software to handle complex problems as suggested by Allen (see [0015]).
Polleri as modified by Allen does not appear to explicitly disclose
generating a brick comprising an initial natural language prompt configured to solicit a request from a user to generate a workflow for a target application using the code generation system;
generating one or more additional bricks comprising one or more additional natural language prompts upon determining that additional information is to be obtained from the user using the code generation system, wherein the one or more additional natural language prompts are configured to elicit the additional information from the user, and wherein additional information from the user helps the code generation system obtain an understanding of the workflow for the target application;
However, in analogous art, Frayssinet discloses
generating a brick comprising an initial natural language prompt configured to solicit a request from a user to generate a workflow for a target application using the code generation system (e.g. para [0052], “The GUI receives the prompt requests and prompts the user per the prompt requests, such as by providing a text entry field and/or a menu of selections. …. The user can be prompted to enter the L1 parameter information in each of these fields. Text fields can be provided for the user to enter text, such as for application or project name. …” Fig. 3, step 302. Para [0072], “At block 302, a request is received from a remote user to generate a software application on a platform based on an identified OGG set.” Generating a software application is analogous to generating a workflow, hence renders the claim feature obvious);
generating one or more additional bricks comprising one or more additional natural language prompts upon determining that additional information is to be obtained from the user using the code generation system, wherein the one or more additional natural language prompts are configured to elicit the additional information from the user, and wherein additional information from the user helps the code generation system obtain an understanding of the workflow for the target application (e.g. Fig. 3, steps 306 and 308, para [0074], “At block 306, user input for template parameters in the selected templates is solicited based on the identified OGG set and business rules. …” para [0076], “At block 308, the selected templates are completed based on the solicited user input. For example, the user input can be entered into parameter fields of the selected templates. …” wherein the parameter fields of the selected templates read on additional natural language prompts, and the parameter data help the system obtain an understanding of software application to be generated which reads on the workflow for the target application);
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of Frayssinet because it provides techniques that reduce the skill and time needed for drafting or modifying applications. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of providing techniques that reduce the skill and time needed for drafting or modifying applications as suggested by Frayssinet (see [0004-0005]).
With respect to claim 24 (Previously Presented), Polleri discloses wherein the set of code is customized for the target application (e.g. para [0221], “At 718, the technique can include generating a plurality of code for a machine learning architecture. ….” Wherein a machine learning architecture reads on the workflow of the target application).
With respect to claim 25 (Previously Presented), Polleri discloses further comprising: deploying the set of code as a software application in an environment in which the workflow for the target application is to be performed (e.g. para [0228], “At 720, the technique can include saving the plurality of code to a memory. …. The plurality of code can be executable code. The plurality of code can be configured to be incorporated into one or more applications.”)
With respect to claim 26 (Previously Presented), Polleri discloses further comprising: running the software application to perform the workflow for the target application (e.g. para [0060], “The model execution engine 108 can use hosted input data 164 to execute and test the machine learning application 112. …”)
With respect to claim 27 (Previously Presented), Polleri discloses further comprising: displaying the workflow on the UI as the workflow for the target application is performed (e.g. para [0216], “… The model composition engine 132 can display the correlated model on an interface 104. The model composition engine 132 can present the correlated model to the user via the intelligent assistant (e.g., the chatbot). In various embodiments, the model composition engine 132 can present multiple models to the user to select from.”)
With respect to claim 28 (Previously Presented), Polleri discloses wherein the workflow for the target application is graphically displayed on the UI (e.g. para [0334], “… The output of the process is a product graph which is a composition of the model, the pipelines, the features, and the metrics for to generate a machine learning solution….”)
With respect to claim 34 (Previously Presented), Polleri discloses wherein the data comprises real data and/or synthetic data (e.g. para [0060], “The model execution engine 108 can use hosted input data 164 to execute and test the machine learning application 112. ….”)
With respect to claim 38 (Previously Presented), Polleri discloses wherein the workflow further comprises modifying the data or synthetically generating additional data (e.g. para [0294], “… where particular pieces of the input data may be modified and the corresponding predictive outcomes may be analyzed to determine which pieces of input data may be the key (e.g., outcome determinative) factors. ….”)
With respect to claim 39 (Previously Presented), Polleri discloses wherein the workflow further comprises using one or more machine learning models to process the data (e.g. Fig. 7 step 718, which indicates that the workflow comprises machine learning models to process data.)
With respect to claim 40 (Previously Presented), Polleri discloses further comprising: executing the set of code in a test environment, and using testing results from the test environment to optimize the workflow for the target application (e.g. para [0043], “… The machine learning application with the new model can be tested off-line with the results being compared with ground truth data. If the machine learning application with the new model outperforms the previously used model, the machine learning application can be upgraded and auto-promoted to production.” Also see para [0046, 0053, 0368]).
With respect to claim 41 (Previously Presented), Polleri discloses further comprising: generating a visual representation comprising at least one of (1) a sequence of steps in the workflow (e.g. para [0334], “… The output of the process is a product graph which is a composition of the model, the pipelines, the features, and the metrics for to generate a machine learning solution….”) and (2) a result, analysis, byproduct or outcome achieved by the target application.
With respect to claim 42 (Previously Presented), Polleri discloses wherein the initial natural language prompt comprises a request to the user to pre-select one or more data bricks, wherein the one or more data bricks comprises a list of parameters or values that are to be utilized in the workflow for the target application (e.g. Fig. 7, steps 702 and 704. These steps suggest the initial prompt to a user. para [0215], “At 708, the technique can include correlating the one or more text fragments to one or more machine learning models of the plurality of machine learning models stored in the library components 168, shown in FIG. 1. Each of the one or more machine learning models can have associated metadata. The associated metadata can be compared with the one more text fragments. ...” wherein the machine learning models read on data bricks and associated metadata read on a list of parameters or values. This paragraph suggests that the initial prompt comprises a request to the user to pre-select data bricks with list of parameters or values).
With respect to claim 43 (Previously Presented), Polleri discloses wherein the one or more data bricks are used for generating the set of code (e.g. Fig. 7, step 718, wherein the machine learning models read on data bricks).
With respect to claim 47 (Currently Amended), Polleri discloses wherein the one or more additional bricks comprise a data brick (e.g. Fig. 7, step 718, wherein the machine learning models read on data bricks).
With respect to claim 48 (Currently Amended), Polleri discloses wherein the one or more additional bricks comprise[[s]] data that is useable by the code generation system to produce executable code (e.g. Fig. 2, step 208, para [0077], “At 208, the functionality includes determining one or more library components to be selected for generating a machine learning model to prototype the machine learning application to comply with the performance requirements.. …” wherein the library components read on the brick comprises data, and is useable to produce executable code, i.e. machine learning model).
Claims 29-33 are rejected under 35 U.S.C. 103 as being unpatentable over Polleri in view of Allen and Frayssinet as applied to claim 21, in further view of ZEILER et al. (US 20180089592 A1– hereinafter “ZEILER”, cited from IDS of 1/16/2024).
With respect to claim 29 (Previously Presented), Polleri as modified by Allen and Frayssinet discloses The method of claim 28, but does not appear to explicitly disclose further comprising: displaying a set of graphical elements on the UI to enable the user to modify one or more portions of the workflow directly through the UI. However, this is taught in analogous art, ZEILER (e.g. para [0035], “… As an example, a graph of arbitrary data workflow methods that incorporate artificial intelligence blocks with other processing operations may be built via the service platform's user interface to meet an entire application or enterprise need. … In some embodiments, the service platform may facilitate collaboration among multiple users by enabling multiple users to supplement or otherwise modify the same workflow ….”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of ZEILER because it provides a system facilitating development of neural networks, other machine learning models that perform significantly better as compared to traditional computer programs. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of developing better performing machine learning models as suggested by ZEILER (see [0018]).
With respect to claim 30 (Previously Presented), Polleri as modified by Allen, Frayssinet and ZEILER discloses The method of claim 29, ZEILER further discloses wherein the one or more additional bricks are movable and connectable to one another within the UI. (e.g. para [0035], “… In some embodiments, the service platform may facilitate collaboration among multiple users by enabling multiple users to supplement or otherwise modify the same workflow, including (i) adding prediction models or other processing operations to a workflow, (ii) removing prediction models or other processing operations from the workflow, …, (iv) setting inputs and outputs for prediction models and other processing operations of the workflow, or (v) other operations.….” wherein the prediction models read on the bricks. For motivation to combine, please refer to office action regarding claim 29.)
With respect to claim 31 (Previously Presented), Polleri as modified by Allen, Frayssinet and ZEILER discloses The method of claim 29, ZEILER further discloses further comprising: modifying the one or more portions of the workflow when the user moves, connects or disconnects the one or more additional bricks through the UI. (e.g. para [0035], “… In some embodiments, the service platform may facilitate collaboration among multiple users by enabling multiple users to supplement or otherwise modify the same workflow, including (i) adding prediction models or other processing operations to a workflow, (ii) removing prediction models or other processing operations from the workflow, …, (iv) setting inputs and outputs for prediction models and other processing operations of the workflow, or (v) other operations.….” wherein the prediction models read on the bricks. For motivation to combine, please refer to office action regarding claim 29.)
With respect to claim 32 (Previously Presented), Polleri as modified by Allen and Frayssinet discloses The method of claim 21, but does not appear to explicitly disclose wherein the workflow comprises obtaining data for the target application. However, this is taught in analogous art, ZEILER (e.g. Fig. 3A, Input 314. For motivation to combine, please refer to office action regarding claim 29.)
With respect to claim 33 (Previously Presented), Polleri as modified by Allen, Frayssinet and ZEILER discloses The method of claim 32, ZEILER further discloses wherein the workflow comprises integrating or connecting with one or more data providers through one or more application programmable interfaces (APIs) to obtain the data (e.g. para [0037], “In some embodiments, a user may download and integrate an API client into the user's application. In some embodiments, the application may utilize the API client to make calls to one or more workflows created via system 100's service platform or user interface thereof. …” For motivation to combine, please refer to office action regarding claim 29.)
Claims 35-37 are rejected under 35 U.S.C. 103 as being unpatentable over Polleri in view of Allen, Frayssinet and ZEILER as applied to claim 32, in further view of SALMAN et al (US 20210406644 A1– hereinafter SALMAN).
With respect to claim 35 (Previously Presented), Polleri as modified by Allen, Frayssinet and ZEILER discloses The method of claim 32, but does not appear to explicitly disclose wherein the workflow further comprises selecting a subset of the data for labeling. However, this is taught in analogous art, SALMAN (e.g. para [0064], “… The metrics associated with the predictions are then used to rank unlabeled observations (block 123), and the ranking of the unlabeled observations is used to control sampling or selection of the unlabeled observations (block 125) for labeling (block 127).”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of SALMAN because it provides techniques for efficient sampling of observations for labeling. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of efficient sampling of observations for labeling as suggested by SALMAN (see Abstract).
With respect to claim 36 (Previously Presented), Polleri as modified by Allen, Frayssinet, ZEILER and SALMAN discloses The method of claim 35, SALMAN further discloses wherein the subset of the data is selected through active learning (e.g. para [0064], “… The active learning phase of blocks 119 to 127 uses the trained machine learning system to generate a prediction for an unlabeled observation (block 119) and compute a metric (such as an uncertainty score) associated with the prediction (block (121). …”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of SALMAN because it provides techniques for efficient sampling of observations for labeling. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of efficient sampling of observations for labeling as suggested by SALMAN (see Abstract).
With respect to claim 37 (Previously Presented), Polleri as modified by Allen, Frayssinet, ZEILER and SALMAN discloses The method of claim 35, SALMAN further discloses wherein the labeling comprises human-in-the-loop labeling (e.g. Fig. 1B, step 125, “SELECT BATCH OF INFORMATIVE OBSERVATION FOR MANUAL ANNOTATION”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of SALMAN because it provides techniques for efficient sampling of observations for labeling. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of efficient sampling of observations for labeling as suggested by SALMAN (see Abstract).
Claim 44 is rejected under 35 U.S.C. 103 as being unpatentable over Polleri in view of Allen and Frayssinet as applied to claim 21, in further view of REGINALDO et al (BR PI1105350 A2 – hereinafter REGINALDO, refer to the attached NPL English translation copy).
With respect to claim 44 (Previously Presented), Polleri as modified by Allen and Frayssinet discloses The method of claim 21, but does not appear to explicitly disclose wherein the target application relates to DNA sequence labeling. However, this is taught in analogous art, REGINALDO (e.g. p18, third paragraph, “Step 19 - Define the DNA sequence labeling and show where the differences occurred, …”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of REGINALDO because it provides techniques for generating and reproducing DNA sequences with considerable reduction of extensive laboratory experiments. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of generating and reproducing DNA sequences with considerable reduction of extensive laboratory experiments as suggested by REGINALDO (see p4 paragraph 5).
Claims 45-46 are rejected under 35 U.S.C. 103 as being unpatentable over Polleri in view of Allen and Frayssinet as applied to claim 21, in further view of Ghosh et al (US 20220004366 A1 – hereinafter Ghosh).
With respect to claim 45 (Previously Presented), Polleri as modified by Allen and Frayssinet discloses The method of claim 21, but does not appear to explicitly disclose wherein the initial natural language prompt comprises one or more questions generated and presented to the user within the query-based UI. However, this is taught in analogous art, Ghosh (e.g. Fig. 14, “What problem are you trying to solve?”)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of Ghosh because it provides software development techniques that improve the design and development process and provide greater efficiencies for vendors and customers. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of providing software development techniques that improve the design and development process and provide greater efficiencies for vendors and customers as suggested by Ghosh (see para [0004-0008]).
With respect to claim 46 (Previously Presented), Polleri as modified by Allen, Frayssinet and Ghosh discloses The method of claim 45, Ghosh further discloses wherein the one or more additional natural language prompts comprises one or more additional questions generated and presented to the user within the query-based UI. (e.g. para [0130], “… the client device 410 may prompt the user with various questions for specifying the type of software application and broad set of features desired, …” For motivation to combine, please refer to office action regarding claim 45 above.)
Claim 49 is rejected under 35 U.S.C. 103 as being unpatentable over Polleri in view of Allen and Frayssinet as applied to claim 21, in further view of Dmitriev et al (US 20210383289 A1 – hereinafter Dmitriev).
With respect to claim 49 (New), Polleri as modified by Allen and Frayssinet discloses The method of claim 21, Polleri discloses further comprising:
automatically generating a second set of code that implements the improvement by at least one of: (1) redefining a data workflow of the target application (e.g. Fig. 7, step 718, “Generating a plurality of code for the machine learning architecture based at least in part on the one or more selected machine learning models”), (2) redefining a training workflow of the target application, or (3) redefining a machine learning model of the target application.
but does not appear to explicitly disclose further comprising:
processing the workflow to determine an improvement in the target application; However, this is taught in analogous art, Dmitriev (e.g. para [0064], “… The workflow management system selects 1050 a candidate change associated with a highest expected impact. The workflow management system 130 applies 1060 the selected candidate change to the target workflow.” Wherein expected impact reads on an improvement.)
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to incorporate the invention of Dmitriev because it provides techniques for addressing problems relating to analysis of historical workflow data in a scalable manner and selecting a change that is expected to have the most positive impact on the performance of a target workflow from large pools of historical data in a computationally efficient manner that preserves memory, processing power, and network usage. A person having ordinary skill in the art would have been motivated to make this combination, with a reasonable expectation of success, for the purpose of providing techniques for addressing problems relating to analysis of historical workflow data in a scalable manner and selecting a change that is expected to have the most positive impact on the performance of a target workflow from large pools of historical data in a computationally efficient manner that preserves memory, processing power, and network usage as suggested by Dmitriev (see para [0003-0004]).
Response to Arguments
Applicant's arguments with respect to art rejections filed 3/4/2026 have been fully considered but they are not persuasive.
At p8 second paragraph of the Remarks, Applicant argued that “Allen does not disclose "determining ... whether additional information is to be obtained from the user." (Emphasis added). Instead, Allen merely describes in paragraph [0042], as cited by the Office, that the "data management engine ... may obtain the knowledgebase data based on the request," where the knowledgebase data is merely data of mappings between natural language and correlated source code. See Allen at [0016]. Allen does not disclose determining that additional information is to be obtained from a user.”
Examiner respectfully disagrees, because, as set forth in the office action above, Allen (e.g. para [0042] as cited in the office action above) teaches, “The data management engine 216 may obtain the knowledgebase data based on the request. For example, the data management engine 216 may obtain knowledgebase data using the indication received in the request for a specific programming language.” Wherein the request is from the user. So Allen teaches the claim feature under discussion.
At p8 fourth paragraph of the Remarks, Applicant argued that “Frayssinet does not disclose "bricks", but merely "a block," a term that Frayssinet uses to describe that a textbox is used to illustrate a portion of a figure. See Office Action at p. 6 and Frayssinet at [0072] ("At block 302 ..."). By "block," Frayssinet is merely referring to the fact that a textbox was used to illustrate a flowchart (in the methods described in Frayssinet), and does not equate to a "block" that a user of Frayssinet's system would use or interact with. See, e.g., FIG. 3 and paragraph [0024]. Accordingly, the "block" as described in Frayssinet does not read on the claimed "bricks" as recited in claim 21 of the present Application.”
Examiner respectfully disagrees, because, as set forth in the office action above, Frayssinet teaches (e.g. para [0052]) “The GUI receives the prompt requests and prompts the user per the prompt requests, such as by providing a text entry field and/or a menu of selections….” Wherein the text entry field or a menu of selections read on bricks that a user interact with. Further, the selected templates in para [0074, 0076] of Frayssinet as cited in the office action above, read on the bricks in the claim.
At p8 last paragraph of the Remarks, Applicant argued with respect to newly added amendment features, this argument is moot upon new ground of rejections made in the office above.
At p9 first three paragraphs of the Remarks, Applicant argued that art rejections to the claims 21, and 24-48 be withdraw because the cited references do not reach and suggest each and every feature recited in amended claim 21.
Examiner respectfully disagrees, because, as set forth in the office action, and as explained above, amended claim 21 is obvious over the cited references and is rejected, the dependent claims are similarly rejected.
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 Zengpu Wei whose telephone number is 571-270-1302. The examiner can normally be reached on Monday to Friday from 8:00AM to 5:00 PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Bradley Teets, can be reached on 571-272-3338. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Zengpu Wei/
Examiner, Art Unit 2197
/BRADLEY A TEETS/Supervisory Patent Examiner, Art Unit 2197