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 .
Remarks
In response to communication files on October 17, 2025, claims 5 and 11 are amended, claim 10 is cancelled, and claims 12-19 are added by applicant's request. Therefore, claims 1-9 and 11-19 are presently pending in the application.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 4/7/2026 was filed after the mailing date of the application on 10/17/2025. The submission 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 § 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 and 11 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claims 1 and 11,
Step 1 Analysis: Claims 1 and 11 are directed to a method, computer readable medium, and system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claims 1 and 11 recites,
The limitations of:
“presenting a data processing task page, and acquiring a
“replacing the data source information of the first to-be-processed data table in the SQL associated query statement with a data table information corresponding to the second to-be- processed data table, so as to obtain an executable SQL query statement” is a mental process which can be performed by the human mind. A human can replace data information.
These limitations, as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: Claims 1, 7, and 15 recites the additional elements: “presenting a data processing task page, and acquiring a SQL associated query statement in response to an input operation of a user on the data processing task page, the SQL associated query statement comprising a data source information of a plurality of first to-be- processed data tables, wherein at least two of the plurality of first to-be-processed data tables are from different data sources”, “acquiring a second to-be-processed data table based on the data source information”, “the SQL”, “processor”, and “computer readable medium”. The limitation of “presenting and acquiring” are an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. , recites wherein the relational query language is SQL. Thus, these additional elements that do not do more than recite the words "apply it" (or an equivalent) and do not integrate a judicial exception into a practical application. Processor”, and “computer readable medium” note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B Analysis: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more. With respect to the "presenting and acquiring” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. the recitation of “processor”, “computer readable medium”, and “memory”, are recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f).
Regarding claims 5 and 15,
Step 1 Analysis: Claims 5 and 15 are directed to a method, computer readable medium, and system, which falls within one of the four statutory categories.
Step 2A Prong 1 Analysis: Claims 5 and 15 recites,
The limitations of:
“determining a plurality of target data tasks of the scheduling task from a plurality of pre-created candidate data tasks based on a selection operation of the user, wherein the candidate data task comprises a data processing task, and the data processing task is implemented by using the data processing method ” is a mental process which can be performed by the human mind. A human can make determination about data task.
“determining an execution order for the plurality of target data tasks in the scheduling task based on a moving operation of a user to the icons” is a mental process which can be performed by the human mind. A human can determine the order a process.
“executing the plurality of target data tasks in the scheduling task according to the execution order” is a mental process which can be performed by the human mind. A human can execute data task.
These limitations, as drafted, are processes that, under broadest reasonable interpretation, covers the performance of the limitation in the mind which falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong 2 Analysis: Claims 5 and 15 recites the additional elements: “presenting a task scheduling page, and creating a scheduling task based on an input operation of a user on the task scheduling page”, “representing icons of the plurality of target data tasks on the task scheduling page”. “processor” and “computer readable medium”, “. The limitation of “presenting and representing…” are an additional element and is insignificant extra-solution activity as retrieval/receiving of data (i.e. mere data gathering) such as 'obtaining information' as identified in MPEP 2106.05(g) and does not provide integration into a practical application. “processor” and “computer readable medium”, note that these recited additional elements are a high-level recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Step 2B Analysis: the conclusions for the additional elements representing mere implementation using a computer are carried over and do not provide significantly more. With respect to the "presenting and executing” limitation is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more. The recitation of “processor” and “computer readable medium”, are recitation of generic computer components to perform the mental process and applied on a computer as in MPEP 2106.05(f).
Therefore, the claims as a whole does not change this conclusion and the claims are ineligible.
Claims 2 and 12 recites additional elements that do not do more than recite the words "apply it" (or an equivalent) and do not integrate a judicial exception into a practical application.
Claims 3-4, 6-8, 13-14, and 16-18, recites limitations abstract ideas previously identified in the independent claims, that are mental process which can be performed by the human mind. This claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Claims 9 and 19, recite additional elements of using a computer as a tool to perform the recited step amount to no more than mere instructions to apply the abstract idea using generic computer component.
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 5-9 and 15-19 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by St. Pierre et al (US Pub 2024/0127144) (Eff filing date of app: 6/28/23) (Hereinafter Pierre).
As to claims 5 and 15, Pierre teaches a task scheduling method, wherein the method is applied to a big data platform, and the method comprises:
presenting a task scheduling page, and creating a scheduling task based on an input operation of a user on the task scheduling page (see fig. 2, character 202, display task schedule);
determining a plurality of target data tasks of the scheduling task from a plurality of pre-created candidate data tasks based on a selection operation of the user, wherein the candidate data task comprises a data processing task, and the data processing task is implemented by using the data processing method according claims 1 (see fig 2, character 208, display one or more selection titles of alternate task schedules);
representing icons of the plurality of target data tasks on the task scheduling page (se p. 50, “For example, each work center in the work environment may be displayed together with a respective icon representing a performance metric, such as efficiency, utilization rates of equipment and/or workers, and on-time performance of tasks” and p. 68, 84);
determining an execution order for the plurality of target data tasks in the scheduling task based on a moving operation of a user to the icons (see p. 84, “The system displays, in a second region 406 of the GUI 401, a set of icons representing tasks 407. Selection of an icon, such as icon 407a, causes the system to change the display in the GUI 401 to a Gantt chart, as illustrated in FIG. 4C.”); and
executing the plurality of target data tasks in the scheduling task according to the execution order (see claim 3, “wherein the first digital representation includes a first set of icons representing the first subset of tasks performed by a first user at the first work center, wherein the second digital representation includes a second set of icons representing the first subset of tasks performed by a second user at the second work center”).
As to claims 6 and 16, Pierre teaches wherein the candidate data task further comprises a data integration task, and the method further comprises:
determining a data integration task name, a data source information and a data destination information based on an operation information of the user on a data integration page (see fig. 4A, character 407A and fig 1, character 131, task schedule data); and
creating a data integration task corresponding to the data integration task name based on the data source information and the data destination information (see p. 66, “When moving tasks from a source work center to a target work center, another set of tasks may be bumped from the target work center. The system may further display the rescheduling of tasks from the target work center”).
As to claims 7 and 17, Pierre teaches the method according further comprising:
based on a selection operation by the user on the icon, representing a development page of a target data task corresponding to the icon (see p. 84, “The system displays, in a second region 406 of the GUI 401, a set of icons representing tasks 407.”); and
in response to detecting a modification operation of the user on the development page, saving a modified target data task (see fig. 47C).
As to claims 8 and 18, Pierre teaches wherein the input operation comprises a time instant at which the scheduling task is to run for the first time (see fig 4C, character 411 and 412, time duration); and the executing the plurality of target data tasks in the scheduling task according to the execution order comprises:
executing, in response to a current time reaching time instant at which the scheduling task is to run for the first time, the plurality of target data tasks in the scheduling task according to the execution order, and acquiring a running status of each target data task (see p. 30, “A performance metric calculation engine 112 calculates performance metrics for the work environment (e.g., task schedule metrics 132) based on the work environment data obtained by the work environment monitoring engine 111. For example, the performance metric calculation engine 112 may analyze equipment status data 135 to calculate a utilization rate—or a percentage of time that a piece of equipment is in use”);
in a case that the running status is an error status, stopping executing the scheduling task and returning a status of the scheduling task as an error status (see p. 23, error detection… and re-ordering scheduled tasks); and
in a case that the plurality of target data tasks are executed in the execution order, returning the status of the scheduling task as a completed status (see p. 29, “Monitoring worker status data may include detecting a worker log in/log out, detecting a selection by a worker at a work center terminal to being or pause a task or indicate a particular task has been completed, and detecting notifications requesting particular workers at particular work centers in the work environment”).
As to claims 9 and 19, Pierre teaches The method according further comprising:
representing, based on a selection operation of the user, a corresponding scheduling task template on a scheduling task template page (see p. 24, “According to one or more embodiments, the system trains the machine learning model on historical user selections of task configurations to learn relationships among performance metrics, costs, time (such as seasonal conditions), and whether a user selected or refrained from selecting a candidate task schedule for implementation in the work environment.”);
acquiring a configuration information for the scheduling task template by the user; and
generating a scheduling task matching the configuration information based on the scheduling task template (see p. 37, “machine learning algorithm generates a target model f such that when the target model f is applied to the datasets of the training data, a maximum number of results determined by the target model f matches the labels of the training data”).
Allowable Subject Matter
Claims 1-4 and 11-14 are allowed over prior art.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to BELIX M ORTIZ DITREN whose telephone number is (571)272-4081. The examiner can normally be reached M-F 9am -5pm.
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, Amy Ng can be reached at 571-270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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BELIX M. ORTIZ DITREN
Primary Examiner
Art Unit 2164
/Belix M Ortiz Ditren/Primary Examiner, Art Unit 2164
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