Prosecution Insights
Last updated: September 17, 2026
Application No. 18/995,025

DATA CONFLICT PROCESSING METHOD, SYSTEM, APPARATUS AND COMPUTER READABLE STORAGE MEDIUM

Non-Final OA §102
Filed
Jan 15, 2025
Priority
Jul 18, 2022 — CN 202210842930.6 +1 more
Examiner
WALDRON, SCOTT A
Art Unit
2156
Tech Center
2100 — Computer Architecture & Software
Assignee
Ebaotech Corporation
OA Round
1 (Non-Final)
81%
Grant Probability
Favorable
1-2
OA Rounds
1y 2m
Est. Remaining
99%
With Interview

Examiner Intelligence

Grants 81% — above average
81%
Career Allowance Rate
397 granted / 489 resolved
+26.2% vs TC avg
Strong +30% interview lift
Without
With
+29.6%
Interview Lift
resolved cases with interview
Typical timeline
2y 10m
Avg Prosecution
15 currently pending
Career history
507
Total Applications
across all art units

Statute-Specific Performance

§101
17.7%
-22.3% vs TC avg
§103
35.4%
-4.6% vs TC avg
§102
21.1%
-18.9% vs TC avg
§112
18.4%
-21.6% vs TC avg
Black line = Tech Center average estimate • Based on career data from 489 resolved cases

Office Action

§102
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 . Information Disclosure Statement The information disclosure statement (IDS) submitted on 01/15/2025 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner. 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)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention. Claims 1-12 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by “Model Migration Approach for Database Preservation” by Rahman et al. (published in 2010, hereinafter “Rahman”). Rahman teaches: 1. A data conflict processing method, applied to a system comprising a transaction configuration module, an export module, and an import module, wherein the export module runs in a configuration environment, the import module runs in a target environment [Rahman, page 86, Fig. 1], and the method comprises: the transaction configuration module sending an export request for target configuration data to an export module in response to a detected export operation, wherein the target configuration data is data generated by a user setting parameter of a transaction service provided by the system [Rahman, page 83, Software Independent Archiving of Relational Databases (SIARD)]; the export module obtaining the target configuration data and generating data description file corresponding to the target configuration data in response to the export request, wherein the data description file comprises source dataset related to the target configuration data and description of an association relationship between data tables in each source dataset [Rahman, pages 83-84, schema and context information]; the transaction configuration module sending the received target configuration data, the data description file and a release request to the import module in response to the detected release operation [Rahman, page 85, § 3.2, Model Migration]; and the import module determining processing result of data of the data table that has a data conflict in the target environment in response to the release request based on the data description file, and applying the received target configuration data to processed data of the data table [Rahman, page 85, § 3.2, Model Migration]. 2. The method of claim 1, wherein the export module comprises export rules of the configuration data that are prese [Rahman, pages 83-84, schema and context information]t, and the export module generating the data description file corresponding to the target configuration data based on the export rules in response to the export request [Rahman, pages 83-84, schema and context information]. 3. The method of claim 2, wherein the export rules at least comprises: presetting at least one association relationship for each data table in the configuration environment database, and determining the base table in each preset association relationship, wherein the base table corresponds to the transaction service provided by the system [Rahman, pages 83-84, schema and context information]. 4. The method of claim 3, wherein the base table in each association relationship is associated with an association data table by foreign key [Rahman, page 88, Fig. 3]. 5. The method of claim 1, wherein the import module determining processing result of data of the data table that has a data conflict in the target environment comprises: the import module obtaining target dataset corresponding to each source dataset from a target environment database, based on the description of association relationship between data tables in each source dataset in the data description file, wherein the association relationship between data tables in the target dataset conforms to the association relationship description between corresponding source dataset data tables [Rahman, page 85, § 3.2, Model Migration]; and the import module comparing the target dataset with the corresponding source dataset and determining processing results of data of the data table that has a data conflict in the target environment based on comparison result of the comparing [Rahman, page 85, § 3.2, Model Migration]. 6. The method of claim 5, wherein the import module comparing the target dataset with the corresponding source dataset and determining processing results of data of the data table that has a data conflict in the target environment based on comparison result of the comparing, comprises: if a unique key of the target dataset base table is consistent with the unique key of the source dataset base table, a primary key of the target dataset base table is consistent with the primary key of the source dataset base table, determining that the data content of the target dataset base table is updated to data content of the source dataset base table in a case in which the source dataset base table and the target dataset base table comprise another inconsistent field [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; if a unique key of the target dataset base table is consistent with the unique key of the source dataset base table, but a primary key of the target dataset base table is inconsistent with the primary key of the source dataset base table, determining that data content of the target dataset base table is updated to data content of the source dataset base table, and the primary key of the target dataset base table is modified to primary key of the source dataset base table [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; if a unique key of the target dataset base table is inconsistent with unique key of the source dataset base table, and the first field corresponding to unique key of the source dataset base table does not exist in the target dataset base table, determining that the first field is inserted into the target dataset base table, and the data content under unique key field of the source dataset base table is updated to the target dataset base table [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; if a unique key of the target dataset base table is inconsistent with unique key of the source dataset base table and the second field corresponding to unique key of the source dataset base table does not exist in the source dataset base table, determining that the second field and data content under the second field are deleted in the target dataset base table [Rahman, pages 87-88, § 4, and Figs. 2 & 3]. 7. The method of claim 6, wherein the import module comparing the target dataset with the corresponding source dataset and determining processing results of data of the data table that has a data conflict in the target environment based on comparison result of the comparing, further comprises: if no unique key is defined in the target dataset base table, determining that the primary key of the target dataset base table is set to the unique key of the target dataset base table [Rahman, pages 87-88, § 4, and Figs. 2 & 3]. 8. The method of claim 7, wherein the import module comparing the target dataset with the corresponding source dataset and determining processing results of data of the data table that has a data conflict in the target environment based on comparison result, further comprises: comparing data content of the association data table in the target dataset with data content of the association data table in the source dataset based on a foreign key in the target dataset base table [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; and determining processing results of data content of the association data table in the target dataset based on comparison results of the comparing [Rahman, pages 87-88, § 4, and Figs. 2 & 3]. 9. The method of claim 8, wherein the determining processing results of data content of the association data table in the target dataset based on the comparison results of the comparing, comprises: if a unique key of the association data table in the target dataset is consistent with the unique key of the association data table in the source dataset, and a primary key of the association data table in the target dataset is consistent with the primary key of the association data table in the source dataset, determining that the data content of the association data table in the target dataset is updated to data content of the association data table in the source dataset, in a case in which the association data table in the source dataset and the association data table in the target dataset comprise another inconsistent field [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; if a unique key of the association data table in the target dataset is consistent with the unique key of the association data table in the source dataset, but a primary key of the association data table in the target dataset is inconsistent with the primary key of the association data table in the source dataset, determining that the data content of the association data table in the target dataset is updated to the data content of the association data table in the source dataset, and the primary key of the association data table in the target dataset is modified to the primary key of the association data table in the source dataset [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; if a unique key of the association data table in the target dataset is inconsistent with the unique key of the association data table in the source dataset, and the third field corresponding to unique key of the association data table in the source dataset does not exist in the association data table in the target dataset, determining that the third field is inserted into the association data table in the target dataset, and the data content under unique key field in the association data table in the source dataset is updated to the association data table in the target dataset [Rahman, pages 87-88, § 4, and Figs. 2 & 3]; if a unique key of the association data table in the target dataset is inconsistent with the unique key of the association data table in the source dataset, and the fourth field corresponding to the unique key of the association data table in the source dataset does not exist in the association data table in the source dataset, determining that the fourth field and data content under the fourth field are deleted in the association data table in the target dataset [Rahman, pages 87-88, § 4, and Figs. 2 & 3]. Claims 10-12 recite limitations that correspond to those recited in claim 1, and are 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. Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Ajay Bhatia can be reached at (571) 272-3906. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000. /Scott A. Waldron/Primary Examiner, Art Unit 2156
Read full office action

Prosecution Timeline

Jan 15, 2025
Application Filed
Jul 01, 2026
Non-Final Rejection mailed — §102 (current)

Precedent Cases

Applications granted by this same examiner with similar technology

Patent 12737391
HYBRID COMPUTING FOR ACCESS-LESS SURGICAL RETRIEVAL
2y 12m to grant Granted Sep 15, 2026
Patent 12730801
SYSTEM AND METHOD FOR ENHANCING CHATBOT INTELLIGENCE THROUGH TRANSFORMER-BASED TABULAR QUESTION-ANSWERING MODEL INTEGRATION WITH CYCLICAL VECTOR DATASET GENERATION
2y 2m to grant Granted Sep 08, 2026
Patent 12724816
AUTOMATIC SUB-CLUSTER SELECTION ALGORITHM FOR THE HIERARCHICAL CLUSTERING OF FILE OBJECTS
1y 10m to grant Granted Sep 01, 2026
Patent 12694008
DIVIDING A DATA PARTITION BASED ON A DATA STORAGE CODING SCHEME
2y 0m to grant Granted Jul 28, 2026
Patent 12688156
GARBAGE COLLECTION OF REDUNDANT PARTITIONS
4y 10m to grant Granted Jul 21, 2026
Study what changed to get past this examiner. Based on 5 most recent grants.

Strategy Recommendation AI-generated — please review before filing

Get a prosecution strategy drawn from examiner precedents, rejection analysis, and claim mapping.
Typically takes 5-10 seconds — AI-generated, attorney review required before filing

Prosecution Projections

1-2
Expected OA Rounds
81%
Grant Probability
99%
With Interview (+29.6%)
2y 10m (~1y 2m remaining)
Median Time to Grant
Low
PTA Risk
Based on 489 resolved cases by this examiner. Grant probability derived from career allowance rate.

Sign in with your work email

Enter your email to receive a magic link. No password needed.

Personal email addresses (Gmail, Yahoo, etc.) are not accepted.

Free tier: 3 strategy analyses per month