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
In response to communications filed on 25 November 2025, claims 1-16 are presently pending in the application, of which, claims 1, 14, and 15 are presented in independent form. The Examiner acknowledges amended claims 1, 3-5, and 14-15, and newly added claim 16. No claims were cancelled.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application filed in Japan on 06 November 2023. It is noted, however, that applicant has not filed a certified copy of the Japanese Patent Application JP 2023-189430 application as required by 37 CFR 1.55.
Response to Remarks/Arguments
All objections and/or rejections issued in the previous Office Action, mailed 26 August 2025, have been withdrawn, unless otherwise noted in this Office Action.
Applicant’s arguments with respect to claims 1-16 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 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.
Claims 1-16 are rejected under 35 U.S.C. 103 as being unpatentable by Sipe, Richard Vance, et al (U.S. 2025/0022028, filed 27 December 2023, claiming benefit from provisional application no. 63/513,346, filed 12 July 2023 and known hereinafter as Sipe) in view of Goyal, Gaurav, et al (U.S. 2024/0036835 and known hereinafter as Goyal)(newly presented).
As per claim 1, Sipe teaches a setup support system for setting up a database comprising at least one processor, the at least one processor being configured to:
identify a setting operation performed by a user in order to set a setting of a database to be created (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.);
support setting up of the database by the user based on the setting operation and a machine learning model which has learned training data created based on a setting for training (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].); and
generating the database (e.g. Sipe, see paragraph [0058] which discloses generated AI model us used to generate code lookup data, where the generative AI code lookup engine is associated with the machine learning engine. See further paragraph [0085], which discloses machine learning engine can include machine learning models generated using the machine learning engine workflow, where machine learning models can include generative AI models and traditional AI models that can be both employed, where generative AI models are designed to generate new data (e.g. database), often in the form of text, images, or other media, based on patterns and knowledge learned from existing data.).
Sipe does not explicitly disclose low-code or no-code; the setting operation is performed by the user, wherein the user selects the setting operation from a pre-defined list of settings that are displayed to the user, each of the setting in the pre-defined list of settings further defines how the database is set up.
Goyal teaches low-code or no-code (e.g. Goyal, see paragraph [0141], which discloses low-code and/or no-code users might be limited to developing web pages using only static UI components that are deployable using template-based web page editors.);
the setting operation is performed by the user (e.g. Goyal, see Figure 8A, which discloses a client device requests creation of a web page. See further paragraph [0170], which discloses each of the client devices may be a user device through which a user (e.g. low-code/no-code developer) may interact with UI.), wherein the user selects the setting operation from a pre-defined list of settings that are displayed to the user (e.g. Goyal, see paragraphs [0157-0159], which discloses UI templates may be predefined UI component templates to be used in generating UI components, where the predefined UI component template may include one or more default values that define an appearance, behavior, and/or data sources of the UI components and where these default values may be overridden by a developer (e.g. user) at web page design time.),
each of the setting in the pre-defined list of settings further defines how the database is set up (e.g. Goyal, see paragraph [0189-0190], which discloses based on and/or in response to reception of the web pages, the server may be configured to determine that the web page includes the runtime UI component, where the server may determine predefined UI components, runtime UI components, etc. to generate a partial representation of the web page.).
Sipe is directed to generative AI code lookup engine. Goyal is directed to low-code/no-code generation containing predefined user interface component used to generate web pages to be populated by UI components. Both are analogous art because they are directed to code generation and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Sipe with the teachings of Goyal to include the claimed features with the motivation to improve generating low-code or no-code templates.
As per claim 2, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 1, wherein the setting operation is an operation for the user to specify a setting, and wherein the at least one processor is configured to support the setting up based on the setting specified by the setting operation and the machine learning model (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.).
As per claim 3, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 2, wherein the setting is a field setting which is a setting of a field in the database, wherein the machine learning model has learned the training data created based on a field setting for training, and wherein the at least one processor is configured to support the setting up by suggesting to the user the setting to be specified by the next setting operation based on the setting specified by the setting operations and the machine learning model (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].).
As per claim 4, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 3, wherein the setting is a field type which is a type of the field, and wherein the at least one processor is configured to support the setting up by suggesting to the user the field type to be specified by the next setting operation based on the field type specified by the setting operation and the machine learning model (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.).
As per claim 5, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 3, wherein the setting is a field layout which is a layout of the field, and wherein the at least one processor is configured to support the setting up by suggesting to the user the field layout to be specified by the next setting operation based on the field layout specified by the setting operation and the machine learning model (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].).
As per claim 6, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 2, wherein the machine learning model has learned the training data created based on a database for training, and wherein the at least one processor is configured to support the setting up by suggesting the database for training to the user based on the setting specified by the setting operation and the machine learning model (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.).
As per claim 7, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 2, wherein the machine learning model has learned the training data created based on a database for training, and wherein the at least one processor is configured to execute clustering of the database being set based on the setting specified by the setting operation and the machine learning model, and to support the setting up based on a result of execution of the clustering (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].).
As per claim 8, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 2, wherein the machine learning model has learned the training data, which indicates settings specified by setting operations for training and an order in which the setting operations for training are performed, and wherein the at least one processor is configured to support the setting up based on the settings specified by the setting operations, the order in which the setting operations are performed, and the machine learning model (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.).
As per claim 9, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 1, wherein the machine learning model is an interactive model, wherein the setting operation is an operation in which the user inputs a prompt to the machine learning model in order to set the setting, and wherein the at least one processor is configured to support the setting up by acquiring input data having a format inputtable to a database creation program for creating the database based on the prompt input by the setting operation and the machine learning model (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].).
As per claim 10, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 9, wherein the at least one processor is configured to support the setting up by inputting output data output from the machine learning model to the database creation program as the input data, and causing the database creation program to output processing result data indicating a processing result of the database creation program (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.).
As per claim 11, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 9, wherein the at least one processor is configured to:
determine whether output data output from the machine learning model has the format (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].);
support, when it is determined that the output data has the format, the setting up by acquiring the output data as the input data (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].);
support, when it is not determined that the output data has the format, the setting up by presenting the user with a response to the prompt based on the output data (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].); and
complete the setting through repeated interactions between the user and the machine learning model (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].).
As per claim 12, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 1, wherein the at least one processor is configured to: acquire user attribute data indicating an attribute of the user; and
support the setting up based on the setting operation, the machine learning model, and the user attribute data (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.).
As per claim 13, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 1, wherein the at least one processor is configured to:
acquire past setting data indicating a past setting which is a setting performed by the user in a past setting operation (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].); and
support the setting up based on the setting operation, the machine learning model, and the past setting data (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].).
As per claim 14, Sipe teaches a setup support method, comprising:
identify a setting operation performed by a user in order to set a setting of a database to be created (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.);
support setting up of the database by the user based on the setting operation and a machine learning model which has learned training data created based on a setting for training (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].); and
generating the database (e.g. Sipe, see paragraph [0058] which discloses generated AI model us used to generate code lookup data, where the generative AI code lookup engine is associated with the machine learning engine. See further paragraph [0085], which discloses machine learning engine can include machine learning models generated using the machine learning engine workflow, where machine learning models can include generative AI models and traditional AI models that can be both employed, where generative AI models are designed to generate new data (e.g. database), often in the form of text, images, or other media, based on patterns and knowledge learned from existing data.).
Sipe does not explicitly disclose low-code or no-code; the setting operation is performed by the user, wherein the user selects the setting operation from a pre-defined list of settings that are displayed to the user, each of the setting in the pre-defined list of settings further defines how the database is set up.
Goyal teaches low-code or no-code (e.g. Goyal, see paragraph [0141], which discloses low-code and/or no-code users might be limited to developing web pages using only static UI components that are deployable using template-based web page editors.);
the setting operation is performed by the user (e.g. Goyal, see Figure 8A, which discloses a client device requests creation of a web page. See further paragraph [0170], which discloses each of the client devices may be a user device through which a user (e.g. low-code/no-code developer) may interact with UI.), wherein the user selects the setting operation from a pre-defined list of settings that are displayed to the user (e.g. Goyal, see paragraphs [0157-0159], which discloses UI templates may be predefined UI component templates to be used in generating UI components, where the predefined UI component template may include one or more default values that define an appearance, behavior, and/or data sources of the UI components and where these default values may be overridden by a developer (e.g. user) at web page design time.),
each of the setting in the pre-defined list of settings further defines how the database is set up (e.g. Goyal, see paragraph [0189-0190], which discloses based on and/or in response to reception of the web pages, the server may be configured to determine that the web page includes the runtime UI component, where the server may determine predefined UI components, runtime UI components, etc. to generate a partial representation of the web page.).
Sipe is directed to generative AI code lookup engine. Goyal is directed to low-code/no-code generation containing predefined user interface component used to generate web pages to be populated by UI components. Both are analogous art because they are directed to code generation and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Sipe with the teachings of Goyal to include the claimed features with the motivation to improve generating low-code or no-code templates.
As per claim 15, Sipe teaches a non-transitory information storage medium having stored thereon a program for causing a computer to:
identify a setting operation performed by a user in order to set a setting of a database to be created (e.g. Sipe, see paragraphs [0038-0042], which discloses an artificial intelligence system that includes generative AI code lookup engine, training data, generative AI code lookup engine operations, etc, where the generative AI code lookup provides an operation state to create generative AI code in the generative AI application.);
support setting up of the database by the user based on the setting operation and a machine learning model which has learned training data created based on a setting for training (e.g. Sipe, see paragraphs [0027-0030, 0035-0045], which discloses providing configuration to generate personalized data including personalized copy, etc. in the generative AI model, where the code logic is implemented by generative AI machine learning models that employ collaborative filtering algorithms or deep learning models to make code data selections, as further described in paragraphs [0051-0054].); and
generating the database (e.g. Sipe, see paragraph [0058] which discloses generated AI model us used to generate code lookup data, where the generative AI code lookup engine is associated with the machine learning engine. See further paragraph [0085], which discloses machine learning engine can include machine learning models generated using the machine learning engine workflow, where machine learning models can include generative AI models and traditional AI models that can be both employed, where generative AI models are designed to generate new data (e.g. database), often in the form of text, images, or other media, based on patterns and knowledge learned from existing data.).
Sipe does not explicitly disclose low-code or no-code; the setting operation is performed by the user, wherein the user selects the setting operation from a pre-defined list of settings that are displayed to the user, each of the setting in the pre-defined list of settings further defines how the database is set up.
Goyal teaches low-code or no-code (e.g. Goyal, see paragraph [0141], which discloses low-code and/or no-code users might be limited to developing web pages using only static UI components that are deployable using template-based web page editors.);
the setting operation is performed by the user (e.g. Goyal, see Figure 8A, which discloses a client device requests creation of a web page. See further paragraph [0170], which discloses each of the client devices may be a user device through which a user (e.g. low-code/no-code developer) may interact with UI.), wherein the user selects the setting operation from a pre-defined list of settings that are displayed to the user (e.g. Goyal, see paragraphs [0157-0159], which discloses UI templates may be predefined UI component templates to be used in generating UI components, where the predefined UI component template may include one or more default values that define an appearance, behavior, and/or data sources of the UI components and where these default values may be overridden by a developer (e.g. user) at web page design time.),
each of the setting in the pre-defined list of settings further defines how the database is set up (e.g. Goyal, see paragraph [0189-0190], which discloses based on and/or in response to reception of the web pages, the server may be configured to determine that the web page includes the runtime UI component, where the server may determine predefined UI components, runtime UI components, etc. to generate a partial representation of the web page.).
Sipe is directed to generative AI code lookup engine. Goyal is directed to low-code/no-code generation containing predefined user interface component used to generate web pages to be populated by UI components. Both are analogous art because they are directed to code generation and therefore it would have been obvious to one of ordinary skilled in the art at the time the invention was filed to modify the teachings of Sipe with the teachings of Goyal to include the claimed features with the motivation to improve generating low-code or no-code templates.
As per claim 16, the modified teachings of Sipe with Goyal teaches the setup support system according to claim 1, wherein the database is generated with no-code when the user does not input code in a database language or a programming language (e.g. Goyal, see paragraph [0141], which discloses low-code and/or no-code users might be limited to developing web pages using only static UI components that are deployable using template-based web page editors.);
wherein the database is generated with low-code when the user inputs a minimum amount of input code in the database language or the programming language (e.g. Goyal, see paragraph [0141], which discloses low-code and/or no-code users might be limited to developing web pages using only static UI components that are deployable using template-based web page editors.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure. See attached PTO-892 that includes additional prior art of record describing the general state of the art in which the invention is directed to.
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.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to FARHAN M SYED whose telephone number is (571)272-7191. The examiner can normally be reached M-F 8:30AM-5:30PM.
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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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/FARHAN M SYED/Primary Examiner, Art Unit 2161 February 24, 2026