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 1-20 are presented for examination.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 2 & 13 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 2 & 13 recite “calibrating the table schema to include . . . if the table schema does not include the item”. The use of “if” renders the claim limitation indefinite because it is unclear if the language is optional. The examiner suggests amending the language to positively state an action that is occurring.
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.
Claim 20 is rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
The claim does not fall within at least one of the four categories of patent eligible subject matter because specification paragraph [0025] is silent about limiting the invention to non-transitory embodiments, therefore the claim is interpreted as reciting a propagating signal. Additionally, the use of “capable” implies the medium is not actually executed by a processor.
The examiner suggests overcoming this portion of the rejection by amending the claim as follows: “A non-transitory computer-readable recording medium having recorded thereon a program that, when executed by a processor, executes
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to the judicial exception of an abstract idea without significantly more.
Step 1
The claims recite method, server, and computer-readable recording medium (claims 1, 12 & 20). Except as noted above, these claims fall within at least one of the four categories of patentable subject matter.
Step 2A Prong One
Claim 1 recites “A method of generating virtual tabular data, performed on a server using a deep-learning module, comprising: generating a first prompt for generating a table schema; calibrating the table schema by comparing the table schema generated based on the first prompt with a predefined reference table schema; generating a second prompt by referring to the calibrated table schema; generating table condition data for first tabular data generated based on the second prompt; generating a third prompt by referring to the table condition data and the calibrated table schema; and deriving final tabular data through a verification operation on second tabular data generated based on the third prompt.”
These steps analyze information which has been received and calculates a value based on the received input to produce an output, which is an act of evaluating information that can be practically performed in the human mind. Thus, these steps are an abstract idea in the “mental process” grouping.
Claims 2, 4, 6 & 8-11 recite steps which are further extensions of the identified abstract idea. Claims 12 & 20 correspond to claim 1. Claims 13 & 15-19 recite steps which are further extensions of the identified abstract idea.
Step 2A Prong Two
This judicial exception is not integrated into a practical application because the combination of additional elements includes only generic computer elements which do not add a meaningful limitation to the abstract idea because they amount to simply implementing the abstract idea on a computer. These elements are: server, deep-learning module, user terminal, processor, memory, database, and computer-readable recording medium.
Claims 3, 5 & 7 recite steps which amount to insignificant extra-solution activity of data gathering, such as receiving input, transmitting output, and updating/modifying data.
Step 2B
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the recitations of generic computer components performing generic computer functions at a high level of generality do not meaningfully limit the claim. Further, the insignificant extra-solution activities of data gathering and presentation do not meaningfully limit the claim.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-20 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Portisch et al. (US 2025/0265423 A1, hereinafter “Portisch”).
Portisch teaches:
1. A method of generating virtual tabular data, performed on a server using a deep-learning module [Portisch, ¶ 0009], comprising:
generating a first prompt for generating a table schema [Portisch, ¶ 0012];
calibrating the table schema by comparing the table schema generated based on the first prompt with a predefined reference table schema [Portisch, ¶¶ 0012 & 0013];
generating a second prompt by referring to the calibrated table schema [Portisch, ¶ 0014];
generating table condition data for first tabular data generated based on the second prompt [Portisch, ¶ 0014];
generating a third prompt by referring to the table condition data and the calibrated table schema [Portisch, ¶ 0014]; and
deriving final tabular data through a verification operation on second tabular data generated based on the third prompt [Portisch, ¶ 0141].
2. The method of claim 1, wherein the calibrating the table schema comprises:
applying the first prompt to the deep-learning module and receiving the table schema as an output of the deep-learning module [Portisch, ¶ 0012];
comparing the table schema with the predefined reference table schema [Portisch, ¶ 0013]; and
calibrating the table schema to include an item included in the predefined reference table schema if the table schema does not include the item [Portisch, ¶ 0013].
3. The method of claim 1, wherein the generating the first prompt comprises:
receiving a data generation request including information on tabular data desired by a user from a user terminal linked with the server [Portisch, ¶ 0157]; and
generating the first prompt based on information included in the data generation request [Portisch, ¶ 0160].
4. The method of claim 3, wherein the generating the second prompt comprises generating the second prompt by referring to the calibrated table schema and the data generation request together [Portisch, ¶ 0160], and
the generating the third prompt comprises generating the third prompt by referring to all of the table condition data, the calibrated table schema, and the data generation request [Portisch, ¶ 0161].
5. The method of claim 1, wherein the generating the table condition data comprises:
applying the second prompt to the deep-learning module and receiving the first tabular data as an output of the deep-learning module [Portisch, ¶¶ 0043 & 0044]; and
generating the table condition data by comparing each column included in the first tabular data with predefined condition data [Portisch, ¶ 0050].
6. The method of claim 5, wherein the table condition data comprises a unary constraint or a binary constraint for each column included in tabular data [Portisch, ¶¶ 0043 & 0044], and
the unary constraint refers to a condition of having one of predetermined values [Portisch, ¶ 0068], and
the binary constraint refers to a condition of including an operational expression with another column [Portisch, ¶¶ 0071 & 0072].
7. The method of claim 1, wherein the deriving the final tabular data comprises:
applying the third prompt to the deep-learning module and receiving the second tabular data as an output of the deep-learning module [Portisch, ¶¶ 0100 & 0101];
applying the second tabular data to a data verification module and obtaining a data evaluation result as an output of the data verification module [Portisch, ¶ 0102]; and
determining the final tabular data based on the data evaluation result [Portisch, ¶ 0141].
8. The method of claim 7, wherein the second tabular data comprises a greater number of example data than the first tabular data [Portisch, ¶ 0044], and
wherein the data verification module:
performs diversity verification on the example data included in the second tabular data [Portisch, ¶ 0073], and
performs constraint satisfaction verification on columns to which a unary constraint or a binary constraint is applied in the example data [Portisch, ¶ 0073].
9. The method of claim 7, wherein the obtaining the data evaluation result comprises:
deriving a first evaluation value for diversity of each row data or each column data included in the second tabular data [Portisch, ¶ 0073];
deriving a second evaluation value for whether each column data included in the second tabular data satisfies a unary constraint [Portisch, ¶ 0068];
deriving a third evaluation value for whether each column data included in the second tabular data satisfies a binary constraint [Portisch, ¶¶ 0071 & 0072]; and
determining whether reference values for the first to third evaluation values are satisfied, and generating the data evaluation result including a result thereof [Portisch, ¶ 0073].
10. The method of claim 9, wherein the determining the final tabular data comprises:
correcting data included in the second tabular data that do not satisfy the reference values to values that satisfy the reference values [Portisch, ¶¶ 0101 & 0102]; and
determining the second tabular data with corrections reflected as the final tabular data [Portisch, ¶¶ 0101 & 0102].
11. The method of claim 7, further comprising:
correcting the third prompt based on the data evaluation result [Portisch, ¶ 0129];
regenerating the second tabular data by applying the corrected third prompt to the deep-learning module [Portisch, ¶ 0129];
applying the regenerated second tabular data to the data verification module and re-obtaining the data evaluation result as an output of the data verification module [Portisch, ¶ 0133]; and
determining the final tabular data based on the re-obtained data evaluation result [Portisch, ¶ 0130].
Claim 12 recites limitations similar to those recited in claim 1 and is rejected for the same reasons discussed above.
Claim 13 recites limitations similar to those recited in claims 2, 5 & 7 and is rejected for the same reasons discussed above.
Claims 14-19 recite limitations similar to those recited in claims 3, 4 & 6-10, respectively, and are rejected for the same reasons discussed above.
Claim 20 recites limitations similar to those recited in claim 1 and is rejected for the same reasons discussed above.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Scott A. Waldron whose telephone number is (571)272-5898. The examiner can normally be reached Monday - Friday 9:00 am - 5:00 pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Neveen Abel-Jalil can be reached at (571)270-0474. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/Scott A. Waldron/Primary Examiner, Art Unit 2152 01/08/2026