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 .
Claims 1-13 of this US application are presented for examination.
Response to Amendment
Claims 1-16 are pending in this application.
Claim rejections 35 USC 101 are maintained.
Applicant’s arguments on claim rejections 35 USC 102 and 35 USC 103, filed 5/5/2026, have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new ground(s) of rejection is made in view of Nelson.
Response to Arguments
Applicant argues that claims 1-13 are patent eligible as a technological improvement. The claims utilize AI to allow for no code or low code work support for handling specific electronic data storage problems (Remarks, page 10).
Examiner respectfully submits that MPEP recites that:
“The courts have not provided an explicit test for this consideration, but have instead illustrated how it is evaluated in numerous decisions. These decisions, and a detailed explanation of how examiners should evaluate this consideration are provided in MPEP § 2106.05(a). In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement.” (MPEP 2106.04(d)(1), Emphasis added).
Examiner respectfully submits that the specification explicitly sets forth an improvement but in a conclusory manner such as utilizing AI to allow for no code or low code work support for handling specific electronic data storage problems. In addition, the claims do not provide sufficient details to be apparent to a person of ordinary skill in the art. For example, the claims do not provide sufficient details for a person of ordinary skill in the art how to implement “generation-related processing relating to generation of storage data”. Therefore, the examiner should not determine the claims improve technology.
Applicant’s arguments with respect to claim rejections 35 USC 102 and 35 USC 103 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 § 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 1-16 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1, 12 and 13:
Step 1:
Claim 1 recites “A work support system”. The claim recites the work support system comprising at least one processor and therefore is a machine.
Claim 12 recites “A method”. The claim recites a series of steps and therefore is a process.
Claim 13 recites “A non-transitory information storage medium having stored thereon a program” and therefore is a manufacture.
Step 2A Prong One:
Claims 1, 12 and 13 recite the limitation “execute/executing” which specifically recite “execute/executing, based on the field information, the user prompt, and an Artificial Intelligence (AI), generation-related processing relating to generation of storage data to be stored in the displaying database, the AI being a large language model;” This limitation are processes that, under their broadest reasonable interpretation, covers performance of the limitation in the mind, but for the recitation of generic computer components. That is, other reciting at least one “processor”, a “non-transitory information storage medium” and a generic AI, nothing in the claim element precludes the step from practically being performed in a human mind or with the aid of pen and paper. For example, “execute/executing” in the context of this claim encompasses a user mentally, and with the aid of pen and paper generating a data record base on field information, user prompt and a generic AI. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, then it falls within the “Mental Processes” grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgment and opinion).
Step 2A Prong Two: The judicial exception is not integrated into a practical application. Claims 1, 12 and 13 recite the additional elements “display/displaying a screen that indicates displaying database in a user terminal of the user, the displaying database being selected by the user from among the databases, the screen including an input form that receive an input of a user prompt;” “acquire/acquiring the user prompt that is input in the screen by the user;” “acquire/acquiring field information relating to each field of the displaying database, the field information indicating field settings that is designated by the user when designing the displaying database with no-code or low-code;” and “store/storing the storage data in the displaying database.” The limitations amount to adding insignificant extra-solution activity to the judicial exception, such as data gatheringand outputting (MPEP 2106.05(g)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The claims 1, 12 and 13 recite the limitation “store/storing the storage data in the displaying database.” The limitation amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). As discussed above, the additional elements of using at least one “processor”, a “non-transitory information storage medium” and a generic AI to perform the steps amounts to no more than mere instructions to apply the exception using generic computer components (See MPEP 2106.05(f)). Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. The claims are not patent eligible.
Claim 2 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 2 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire information relating to the displaying database, the information being information other than the field information; and execute the generation-related processing further based on the other information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 3 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 3 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire specification information relating to a specification in the work support system; and execute the generation-related processing further based on the specification information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 4 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 4 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire a default prompt relating to generation of the database, the default prompt being provided in advance; and execute the generation-related processing further based on the default prompt.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 5 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 5 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire user attribute information relating to an attribute of the user; and execute the generation-related processing further based on the user attribute information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 6 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 6 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire related data stored in a related database relating to the database; and execute the generation-related processing further based on the related data.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 7 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 7 recites the same abstract idea of claim 1. The claim recites the additional element “execute the generation-related processing relating to generation of a plurality of pieces of the storage data that are mutually different;” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim also recites the additional element “store the plurality of pieces of the storage data that are mutually different in the database.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The limitation also amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). The claim is not patent eligible.
Claim 8 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 8 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire, based on input of the user, generation number information relating to the number of pieces of the storage data to be generated; and execute the generation-related processing further based on the generation number information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 9 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 9 recites the same abstract idea of claim 1. The claim recites the additional element “execute the generation-related processing relating to the generation of the storage data that is part of a record of the database;” which further elaborates on the abstract idea and therefore, does not amount to significant more. The claim also recites the additional element “store, in the database, the record including the storage data as the part.” The limitation amounts to adding insignificant extra-solution activity to the judicial exception, such as data gathering (MPEP 2106.05(g)). The limitation also amounts to well‐understood, routine, and conventional functions, e.g. storing and retrieving information in memory (See MPEP 2106.05(d)). The claim is not patent eligible.
Claim 10 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 10 recites the same abstract idea of claim 1. The claim recites the additional elements “acquire, based on input of the user, correction content information relating to correction content in the storage data; execute correction-related processing relating to generation of correction portion data, which is post-correction data of a correction portion corresponding to the correction content in the storage data, based on the correction content information and the AI; and correct the storage data by replacing the correction portion in the storage data by the correction portion data.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 11 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 11 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire verification content information relating to verification content of the database; and execute the generation-related processing further based on the verification content information.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
Claim 14 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 14 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire the field information that indicates at least one of: (1) a field name that is a name of each field, (2) a field code that is a code of each field, (3) a field type that is a type of each field, (4) a calculation formula associated with each field, (5) a sequential order of each field, (6) a position of an input form for the user to input a value of each field, (7) a design of each field on the screen, and (8) an access right to each field.” The limitations amount to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 15 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 15 recites the same abstract idea of claim 1. The claim also recites the additional elements “acquire the field information that indicates: (1) a field name that is a name of each field, (2) a field code that is a code of each field, (3) a field type that is a type of each field, (4) a calculation formula associated with each field, (5) a sequential order of each field, (6) a position of an input form for the user to input a value of each field, (7) a design of each field on the screen, and (8) an access right to each field.” The limitations amount to adding insignificant extra-solution activity to the judicial exception, such as selecting a particular data source or type of data to be manipulated (See MPEP 2106.05(g)). The claim is not patent eligible.
Claim 16 is dependent on the claim 1 and includes all the limitations of claim 1. Therefore, claim 16 recites the same abstract idea of claim 1. The claim recites the additional elements “acquire the user prompt that instructs the AI to generate sample data of the displaying database, execute the generation-related processing relating to generation of the sample data, and store the sample data as the storage data in the displaying database.” The limitations amount to a field of use or technological environment in which to apply a judicial exception includes collecting information, analyzing it, and displaying certain results (See MPEP 2106.05 (h)). The claim is not patent eligible.
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.
Claims 1-2, 5-14 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson et al. (US 2020/0092178, hereinafter “Nelson”) in view of Kanner et al. (US 2020/0143258, hereinafter “Kanner”).
Regarding claim 1, Nelson teaches A work support system, which is configured to support work of a user through use of databases designed with no-code or low-code (Nelson, [0071]: Decision table user interface 325 may include standard platform forms and lists for enabling a user with appropriate privileges (e.g., enterprise decision maker or policy logic manager) to create, update, activate/deactivate, delete, or manage decision tables. [0086]: Returning to FIG. 3, client instance 315 also includes flow plan development platform 370 for creating, modifying, managing, and executing flow plans that consume decision tables (e.g., decision tables shown in FIGS. 4-9) in a low code/no code, natural language process automation environment on aPaaS platform 310.), the work support system comprising at least one processor configured to:
display a screen that indicates displaying database in a user terminal of the user, the displaying database being selected by the user from among the databases, the screen including an input form that receive an input of a user prompt; acquire the user prompt that is input in the screen by the user (Nelson, [0072]: The user may provide various attributes 410 to create and save the decision table. For example, the attributes 410 may include decision table Name, Answer Table name which stores decision answers corresponding to the decision table, Application, and the like. [0080]: As shown in FIG. 10, a user can utilize enterprise rule creation user interface 345 of enterprise rule engine 340 to create a new enterprise rule by setting attributes 1010 and 1015 of the new enterprise rule. Attributes 1010 may include a Name for the enterprise rule; selection of a Table (e.g., any table of client instance 315 on aPaaS platform 310) that the enterprise rule runs on; selection of an application that contains the enterprise rule; a checkbox to enable/disable the enterprise rule; a checkbox to enable/disable advanced features for the enterprise rule; and the like.);
acquire field information relating to each field of the displaying database, the field information indicating field settings that is designated by the user when designing the displaying database with no-code or low-code (Nelson, [0072]: Further, as shown in FIG. 4, the user may also set one or more fields (e.g., attributes or column names 440 of a particular selected database table) as decision inputs 430. In the example shown in FIG. 4, the user sets the Impact and Urgency (column names “u_urgency” and “u_impact”) columns of an incident table as decision inputs 430 to the Priority Calculation decision table. [0081]: Action elements 1150 that may be set by the user for execution when trigger conditions of the enterprise rule are satisfied include setting field values of the selected Table to a specific value, or to be the same as another specified field value, or to a value relative to the user configuring the enterprise rule or a user with a specific role, and the like;).
Nelson does not explicitly teach execute, based on the field information, the user prompt, and an Artificial Intelligence (AI), generation-related processing relating to generation of storage data to be stored in the displaying database, the AI being a large language model; and store the storage data in the displaying database.
Kanner teaches execute, based on the field information, the user prompt, and an Artificial Intelligence (AI), generation-related processing relating to generation of storage data to be stored in the displaying database, the AI being a large language model (Kanner, [0057] and Fig. 1: In process 12, for each item of information in the received set of items, the server system obtains, as a result of parsing the received set of items, new information including an information type and a set of data fields pertinent to the information type. In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.); and
store the storage data in the displaying database (Kanner, [0057] and Fig. 1: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 2, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire information relating to the displaying database, the information being information other than the field information (Kanner, [0057] and Fig. 1: In process 12, for each item of information in the received set of items, the server system obtains, as a result of parsing the received set of items, new information including an information type and a set of data fields pertinent to the information type. [0058]: The artificial intelligence engine 23 (located on the computing device or on the server system) receives a set of items 24 and determines an information type and a set of data fields 25.); and
execute the generation-related processing further based on the other information (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 5, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire user information relating to an attribute of the user (Kanner, [0075] and Fig. 7: FIG. 7 is a diagram showing operation of the processes of FIG. 1, in accordance with an embodiment of the present invention, in processing information extracted from the driver's license of FIGS. 5A through 5F. In process 12, in this example, the new information obtained 71 includes the information type “US-CA driver's license” and the data fields consisting of the name, the license number, the address, the date of birth and the expiration date.); and
execute the generation-related processing further based on the user attribute information (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system. [0075] and Fig. 7: In process 13, still in this example of the driver's license, the derived information 72 includes a new derived event “Expiration”; a new derived contact “Tom Smith”; a new derived residence and the derived associations between the contact, the residence and the driver's license, as represented also by FIG. 4.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 6, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire related data stored in a related database relating to the displaying database (Kanner, [0058]: FIG. 2 is a block diagram showing the information flow, in accordance with an embodiment of the present invention, of the task 12 of FIG. 1, wherein the information type and set of data fields are obtained by parsing by an artificial intelligence engine, which is executed either on the computing device or the server system. Information type definitions 22 stored in the taxonomy database 21 are used to train an artificial intelligence engine 23. The artificial intelligence engine 23 (located on the computing device or on the server system) receives a set of items 24 and determines an information type and a set of data fields 25.); and
execute the generation-related processing further based on the related data (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 7, Nelson in view of Kanner teaches wherein the at least one processor is configured to: execute the generation-related processing relating to generation of a plurality of pieces of the storage data that are mutually different (Kanner, [0075] and Fig. 7: FIG. 7 is a diagram showing operation of the processes of FIG. 1, in accordance with an embodiment of the present invention, in processing information extracted from the driver's license of FIGS. 5A through 5F. In process 12, in this example, the new information obtained 71 includes the information type “US-CA driver's license” and the data fields consisting of the name, the license number, the address, the date of birth and the expiration date.); and
store the plurality of pieces of the storage data that are mutually different in the displaying database (Kanner, [0057]: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 8, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire, based on input of the user, generation number information relating to the number of pieces of the storage data to be generated (Kanner, [0075] and Fig. 7: FIG. 7 is a diagram showing operation of the processes of FIG. 1, in accordance with an embodiment of the present invention, in processing information extracted from the driver's license of FIGS. 5A through 5F. In process 12, in this example, the new information obtained 71 includes the information type “US-CA driver's license” and the data fields consisting of the name, the license number, the address, the date of birth and the expiration date. [0099]: The “Membership Card” information screen of FIG. 19A includes a list 1902 of parameters associated with the specific membership card of the user Frank, such as “Card Number,” “Effective Date,” “Expiration Date,” etc.); and
execute the generation-related processing further based on the generation number information (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 9, Kanner teaches wherein the at least one processor is configured to: execute the generation-related processing relating to the generation of the storage data that is part of a record of the database (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.); and
store, in the displaying database, the record including the storage data as the part (Kanner, [0057] and Fig. 1: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 10, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire, based on input of the user, correction content information relating to correction content in the storage; execute correction-related processing relating to generation of correction portion data, which is post-correction data of a correction portion corresponding to the correction content in the storage data, based on the correction content information and the AI data (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system. [0075] and Fig. 7: In process 13, still in this example of the driver's license, the derived information 72 includes a new derived event “Expiration”; a new derived contact “Tom Smith”; a new derived residence and the derived associations between the contact, the residence and the driver's license, as represented also by FIG. 4.); and
correct the storage data by replacing the correction portion in the storage data by the correction portion data (Kanner, [0057]: In process 14, the server system prompts the user to confirm or change the values of the new and derived information. [0066]: In this FIG. 5E, the user confirms the information type and the set of data fields by clicking on the prompt “The info is correct” 56. The user can also change the information type or some of the data fields by clicking “Edit” on this screen.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Regarding claim 11, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire verification content information relating to verification content of the displaying database (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.); and
execute the generation-related processing further based on the verification content information (Kanner, [0057]: In process 14, the server system prompts the user to confirm or change the values of the new and derived information. [0066]: In this FIG. 5E, the user confirms the information type and the set of data fields by clicking on the prompt “The info is correct” 56. The user can also change the information type or some of the data fields by clicking “Edit” on this screen.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Claim 12 is rejected under the same rationale as claim 1.
Claim 13 is rejected under the same rationale as claim 1. Nelson also teaches A non-transitory information storage medium having stored thereon a program for causing a computer configured to support work of a user through use of a database designed with no-code or low-code (Nelson, [0071]: Decision table user interface 325 may include standard platform forms and lists for enabling a user with appropriate privileges (e.g., enterprise decision maker or policy logic manager) to create, update, activate/deactivate, delete, or manage decision tables. [0136]: FIG. 24 illustrates that memory 2410 may be operatively and communicatively coupled to processor 2405.).
Regarding claim 14, Nelson in view of Kanner teaches wherein the at least one processor is configured to acquire the field information that indicates at least one of: (1) a field name that is a name of each field (Nelson, [0072]: Further, as shown in FIG. 4, the user may also set one or more fields (e.g., attributes or column names 440 of a particular selected database table) as decision inputs 430. In the example shown in FIG. 4, the user sets the Impact and Urgency (column names “u_urgency” and “u_impact”) columns of an incident table as decision inputs 430 to the Priority Calculation decision table.), (2) a field code that is a code of each field, (3) a field type that is a type of each field (Nelson, Fig. 4: discussing about column “Type”), (4) a calculation formula associated with each field (Nelson, [0076]: Thus, based on the complex condition logic 510 specified by the user, the decision table calculates a particular type of contract template record (template object) as a decision answer.), (5) a sequential order of each field (Nelson, Fig. 4: discussing about column “Order”), (6) a position of an input form for the user to input a value of each field, (7) a design of each field on the screen (Nelson, [0073]: Further, as shown in FIG. 5, decision table user interface 325 allows the policy logic setting user to specify complex condition logic 510 based on values for decision inputs 430 to resolve to a particular decision answer 520 when the one or more conditions 530 specified in the condition logic 510 are determined to be true for a given value 540 of the decision inputs.), and (8) an access right to each field (Nelson, [0041]: By setting a field value of a triggering record to a path that references a particular application object based on the returned decision answer, the triggering record can be linked to another rich application object, thereby coupling (and allowing access from) the triggering record in a table to any other record (and corresponding field or column values and associated metadata) in any other table in the aPaaS environment.).
Regarding claim 16, Nelson in view of Kanner teaches wherein the at least one processor is configured to: acquire the user prompt that instructs the AI to generate sample data of the displaying database (Kanner, [0057] and Fig. 1: In process 12, for each item of information in the received set of items, the server system obtains, as a result of parsing the received set of items, new information including an information type and a set of data fields pertinent to the information type. [0058]: The artificial intelligence engine 23 (located on the computing device or on the server system) receives a set of items 24 and determines an information type and a set of data fields 25.);
execute the generation-related processing relating to generation of the sample data (Kanner, [0057] and Fig. 1: In process 13, the server system feeds to an artificial intelligence engine the new information and other user information stored in association with an internal account of the specific user, in order to produce, from the artificial intelligence engine, derived information selected from the group consisting of contact information, event information, inferred information, and relationships between the new information and the other user information. The artificial intelligence engine in this embodiment is a component of the server system.), and
store the sample data as the storage data in the displaying database (Kanner, [0057] and Fig. 1: In process 15, the server system stores, with respect to each item of information, the (confirmed or changed) new information and the derived information, in a storage system in communication with the server system, in an encrypted format, and associates such stored item of information with an internal account of the specific user and with the corresponding information type and set of data fields.).
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 decision table user interface of Nelson with the teaching about the artificial intelligence engine of Kanner because it would offer distinct advantages in processing power, efficiency, and scalability by automating complex tasks, analyzing vast amounts of data at unprecedented speeds, and significantly reducing operational costs.
Claims 3-4 are rejected under 35 U.S.C. 103 as being unpatentable over Nelson in view of Kanner and further in view of Dsouza (US 2017/0220323).
Regarding claim 3, Nelson in view of Kanner teaches the system of claim 1 as discussed above. Nelson in view of Kanner does not explicitly teach wherein the at least one processor is configured to: acquire specification information relating to a specification in the work support system; and execute the generation-related processing further based on the specification information.
Dsouza teaches wherein the at least one processor is configured to: acquire specification information relating to a specification in the work support system; and execute the generation-related processing further based on the specification information (Dsouza, [0023]: In such case, the design retrieval system 101 extracts one or more attributes from the business requirement and technical specification documents. The one or more attributes entered by the users and/or extracted by the design retrieval system 101 comprises design attributes, domain specific attributes and system specific attributes… The design retrieval system 101 identifies one or more patterns for the keywords by applying at least one of artificial intelligence, machine learning, and default prompt pattern analysis and recognition techniques. The design retrieval system 101 generates a query string for searching the design database 105 based on the one more patterns identified from the keywords, in order to determine the architectural designs associated with the user inputs provided by the users.).
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 decision table user interface of Nelson and Kanner with the teaching about the design retrieval system of Dsouza because it helps in improving the productivity, reducing the inconsistency with an organisation and also reduces re-work and maintenance efforts (Dsouza, [0064]).
Regarding claim 4, Nelson in view of Kanner teaches the system of claim 1 as discussed above. Nelson in view of Kanner does not explicitly teach wherein the at least one processor is configured to: acquire a default prompt relating to generation of the database, the default prompt being provided in advance; and execute the generation-related processing further based on the default prompt.
Dsouza teaches wherein the at least one processor is configured to: acquire a default prompt relating to generation of the database, the default prompt being provided in advance; and execute the generation-related processing further based on the default prompt (Dsouza, [0039]: The validation module 219 validates the one or more attributes by identifying the missing attributes in the one or more attributes received from the users. Further, the validation module 219 checks for the minimum availability of the attributes using which the architectural designs can be determined. Further, if the validation module identifies missing attributes, the validation module 219 prompts the users to provide with the missing attributes in order to determine the architectural designs associated with the software application.).
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 decision table user interface of Nelson and Kanner with the teaching about the design retrieval system of Dsouza because it helps in improving the productivity, reducing the inconsistency with an organisation and also reduces re-work and maintenance efforts (Dsouza, [0064]).
Claim 15 is rejected under 35 U.S.C. 103 as being unpatentable over Nelson in view of Kanner and further in view of Arazi et al. (US 2009/0012986, hereinafter “Arazi”).
Regarding claim 15, Nelson in view of Kanner teaches the system of claim 1 as discussed above. Kanner also teaches wherein the at least one processor is configured to acquire the field information that indicates: (1) a field name that is a name of each field (Nelson, [0072]: Further, as shown in FIG. 4, the user may also set one or more fields (e.g., attributes or column names 440 of a particular selected database table) as decision inputs 430. In the example shown in FIG. 4, the user sets the Impact and Urgency (column names “u_urgency” and “u_impact”) columns of an incident table as decision inputs 430 to the Priority Calculation decision table.), (3) a field type that is a type of each field (Nelson, Fig. 4: discussing about column “Type”), (4) a calculation formula associated with each field (Nelson, [0076]: Thus, based on the complex condition logic 510 specified by the user, the decision table calculates a particular type of contract template record (template object) as a decision answer.), (5) a sequential order of each field (Nelson, Fig. 4: discussing about column “Order”), (6) a position of an input form for the user to input a value of each field, (7) a design of each field on the screen (Nelson, [0073]: Further, as shown in FIG. 5, decision table user interface 325 allows the policy logic setting user to specify complex condition logic 510 based on values for decision inputs 430 to resolve to a particular decision answer 520 when the one or more conditions 530 specified in the condition logic 510 are determined to be true for a given value 540 of the decision inputs.), and (8) an access right to each field (Nelson, [0041]: By setting a field value of a triggering record to a path that references a particular application object based on the returned decision answer, the triggering record can be linked to another rich application object, thereby coupling (and allowing access from) the triggering record in a table to any other record (and corresponding field or column values and associated metadata) in any other table in the aPaaS environment.).
Nelson in view of Kanner does not explicitly teach a field code that is a code of each field.
Arazi teaches a field code that is a code of each field (Arazi, [0082]: FIG. 4 shows a tabular view of the field code, name, types and parameters for selected tables along with a tree view of the generated schema interface.).
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 decision table user interface of Nelson and Kanner with the teaching about the field code of Arazi because it would optimize field operations and cut administrative costs.
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.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Mathew et al. (US 11,138,218) discloses that extraction rules can be used to extract one or more values for a field from events by parsing the event data and examining the event data for one or more patterns of characters, numbers, delimiters, etc., that indicate where the field begins and, optionally, ends.
Baum et al. (US 2021/0398633) discloses that a prescription engine can be a neural network engine that is trained to receive image files, audio files, video files, text messages, etc., transmitted by mobile devices of prescribers and extract from the received files/messages prescriber, patient, prescribed item, etc., so as to populate the extracted information into the fields of a prescription form to generate a prescription in an electronic format. In some instances, the prescription engine can include or be coupled to other AI -powered engines which may be used in the extraction of the above-noted information.
Contact Information
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PHONG H NGUYEN whose telephone number is (571)270-1766. The examiner can normally be reached Monday-Friday, 8:30am-5pm EST.
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/PHONG H NGUYEN/ Primary Examiner, Art Unit 2156
June 11, 2026
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