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 .
This communication is in response to application 19/225,680 filed 06/02/2025. Claims 1-8 are pending and hereby examined. No claims are allowed.
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 1-8 are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to a mental process (See MPEP 2106.04(a)(2)(III)). The independent claim (Claim 1) recites a system to acquire and evaluate project data to identify responsible departments for the project. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Using project data to make recommendations on responsible departments for the project can equivalently be achieved by human observation and evaluation of the data. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a system to implement the identified abstract idea of receiving information, processing information, and displaying the result of the analysis (i.e. processing requirement data for a project to recommend responsible departments and the like) in a general computer environment. The claims do not include additional elements that amount to significantly more than the judicial exception. The independent claims recite the additional element “a processor” The claim element is recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using a general computer environment. The machine merely acts as a modality to implement the abstract idea and is not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
Regarding the dependent claims:
Claim 2 introduces the new additional element “a machine learning model”. However, simply using machine learning to identify department relevance is not indicative of practical application or significantly more. The machine learning is described at a high level of generality and merely acts as a modality to implement the abstract idea (i.e., the additional element is simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
Claim 3 specifies the machine learning is a deep learning model. However, this still recites machine learning at a high level of generality and falls under the same MPEP 2106.05(f) analysis.
Claims 4-8 further define the existing abstract idea, and they do not introduce any new abstract ideas or new additional elements impacting analysis under 35 USC 101.
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.
(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 1-2 and 4-8 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Knijnik (US 20170249574 A1).
Regarding Claim 1, Knijnik teaches:
A project support system comprising: at least one processor configured to execute: a first process of acquiring requirement data recording information on a requirement content required in a project; [see at least: (Para 0068) “The user devices have a display capable of displaying a graphical user interface (GUI), which is provided to the user device by the server machine 102 and which permits users to provide data for use or storage by the system”, (Para 0074) “The server machine 102 provides the user with an interface and prompts the user to input information concerning a new project, including a description of the project, the type of project, the total labor force (in hours or days) needed for the project, the total cost for the project, the start date, and the deadline date.”]
and a second process of identifying a responsible department having high relevance to the requirement content recorded in the requirement data. [see at least: (Figures 7 and 10), (Para 0080) “The user confirms or enters initial information regarding the task, including the project that relates to the task, create a name for the task being scheduled, identify the relevant department or group that will do the task…The project manager is not responsible for determining what resource will perform the task or when it should be started, as these functions are performed automatically by the system…”]
Regarding Claim 2, Knijnik reaches the limitations set forth above, Knijnik further teaches:
wherein: the second process is configured to enter the requirement data obtained in the first process into an input layer of a machine learning model and to obtain the responsible department having a high degree of relevance to the requirement data from an output layer thereof; [see at least: (Figure 10), (Para 0069) “The machine learning engine analyzes data corresponding to the input and creation of tasks and the results thereof, and identifies associations between inputs and positive results.”, (Para 0080) “The user confirms or enters initial information regarding the task, including the project that relates to the task, create a name for the task being scheduled, identify the relevant department or group that will do the task…The project manager is not responsible for determining what resource will perform the task or when it should be started, as these functions are performed automatically by the system…”, (Para 0119) “After taking into account the above considerations, the server machine 102 automatically creates a new task alert that contains information regarding the new task and sends the new task alert to a user device terminal 108 for viewing by another user, such as a production manager. The new task alert generated by the server machine 102 shows that a new task has been created, to whom it was allocated (confirming expertise level defined by project manager), when is it starting, its duration, its formal scope, and other relevant details as shown in FIG. 10.”]
and the machine learning model is a pre-trained machine learning model trained based on training data recording correlations between requirement data recording the information on requirement contents required in projects and predetermined responsible departments handling the projects. [see at least: (Para 0082) “The information entered by the user may be used to seed the machine learning engine and may serve as a template of optimized parameters to be used by the best practices engine 134 for subsequent tasks that are identical or similar to the particular task.”]
Regarding Claim 4, Knijnik teaches the limitations set forth above, Knijnik further teaches:
wherein the at least one processor is further configured to execute a process of updating the machine learning model using, as additional training data, corrected data containing corrected relationships with responsible departments that are output from an output layer of the machine learning model. [see at least: (Para 0022) “stores said new project data record in said memory, and (iii) dynamically updates the characteristics of said resources”, (Para 0070) “As additional information is provided to the system, the machine learning engine continually updates and improves its ability to forecast what is likely to happen with respect to the selection of a particular parameter for a particular action (e.g., the assignment of a particular resource to a particular task)”]
Regarding Claim 5, Knijnik teaches the limitations set forth above, Knijnik further teaches:
wherein the at least one processor is further configured to execute: a process of acquiring information recorded in a database of a responsible department handing the project; and [see at least: (Para 0022) “ a learning engine that continuously quantifies the characteristics of the resources based upon an analysis of the metrics; and a feedback loop that (i) generates a new project data record when the project is completed, (ii) stores said new project data record in said memory, and (iii) dynamically updates the characteristics of said resources”, (Para 0080) “The user confirms or enters initial information regarding the task, including the project that relates to the task, create a name for the task being scheduled, identify the relevant department or group that will do the task…The project manager is not responsible for determining what resource will perform the task or when it should be started, as these functions are performed automatically by the system…”]
a process of updating the machine learning model by training the machine learning model to learn an item having a high degree of relevance to the responsible department based on the information recorded in the database of the responsible department handling the project. [see at least: (Para 0070) “As additional information is provided to the system, the machine learning engine continually updates and improves its ability to forecast what is likely to happen with respect to the selection of a particular parameter for a particular action (e.g., the assignment of a particular resource to a particular task)”]
Regarding Claim 6, Knijnik teaches the limitations set forth above, Knijnik further teaches:
further comprising: a department information storage section storing keywords obtained from information recorded in a database of a responsible department handling the project in association with the department. [see at least (Para 0080) “The user confirms or enters initial information regarding the task, including the project that relates to the task, create a name for the task being scheduled, identify the relevant department or group that will do the task, identify particular relevant skills”, (Para 0081) “According to a preferred embodiment, the system 100 calls the best practices engine 134 to search the database storage 104 for optimized parameters generated and stored by the machine learning engine. Using the initial task information (such as the project type or keywords therein), the best practices engine 134 searches for and identifies whether there exists optimized parameters concerning the task scope and/or the task steps”]
Regarding Claim 7, Knijnik teaches the limitations set forth above, Knijnik further teaches:
wherein: the second process is configured to enter the requirement data obtained in the first process into an input layer of a machine learning model and to obtain the responsible department having a high degree of relevance to the requirement data from an output layer thereof; [see at least: (Figure 10), (Para 0069) “The machine learning engine analyzes data corresponding to the input and creation of tasks and the results thereof, and identifies associations between inputs and positive results.”, (Para 0080) “The user confirms or enters initial information regarding the task, including the project that relates to the task, create a name for the task being scheduled, identify the relevant department or group that will do the task…The project manager is not responsible for determining what resource will perform the task or when it should be started, as these functions are performed automatically by the system…”, (Para 0119) “After taking into account the above considerations, the server machine 102 automatically creates a new task alert that contains information regarding the new task and sends the new task alert to a user device terminal 108 for viewing by another user, such as a production manager. The new task alert generated by the server machine 102 shows that a new task has been created, to whom it was allocated (confirming expertise level defined by project manager), when is it starting, its duration, its formal scope, and other relevant details as shown in FIG. 10.”]
and the machine learning model is a pre-trained machine learning model trained so as to identify a responsible department having high relevance to the requirement content recorded in the requirement data based on the keywords stored in the department information storage section. [see at least: (Para 0081) “According to a preferred embodiment, the system 100 calls the best practices engine 134 to search the database storage 104 for optimized parameters generated and stored by the machine learning engine. Using the initial task information (such as the project type or keywords therein), the best practices engine 134 searches for and identifies whether there exists optimized parameters concerning the task scope and/or the task steps” (Para 0082) “The information entered by the user may be used to seed the machine learning engine and may serve as a template of optimized parameters to be used by the best practices engine 134 for subsequent tasks that are identical or similar to the particular task.”]
Regarding Claim 8, Knijnik teaches the limitations set forth above, Knijnik further teaches:
wherein: the machine learning model is configured to extract a word contained in or associated with the requirement data, [see at least: (Para 0081) “Using the initial task information (such as the project type or keywords therein), the best practices engine 134 searches for and identifies whether there exists optimized parameters concerning the task scope and/or the task steps, and, if so, displays such parameters on the GUI of the user device as recommended parameters”]
and to identify a responsible department having a high degree of relevance to the requirement content recorded in the requirement data based on the extracted word and the keywords stored in the department information storage section [see at least: (Para 0080) “The user confirms or enters initial information regarding the task, including the project that relates to the task, create a name for the task being scheduled, identify the relevant department or group that will do the task… The project manager is not responsible for determining what resource will perform the task or when it should be started, as these functions are performed automatically by the system”, (Para 0081) “According to a preferred embodiment, the system 100 calls the best practices engine 134 to search the database storage 104 for optimized parameters generated and stored by the machine learning engine. Using the initial task information (such as the project type or keywords therein), the best practices engine 134 searches for and identifies whether there exists optimized parameters concerning the task scope and/or the task steps, and, if so, displays such parameters on the GUI of the user device as recommended parameters” (Para 0082) “The information entered by the user may be used to seed the machine learning engine and may serve as a template of optimized parameters to be used by the best practices engine 134 for subsequent tasks that are identical or similar to the particular task.”]
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.
Claim 3 is rejected under 35 U.S.C. 103 as being unpatentable over Knijnik (US 20170249574 A1) in view of Xia US 20230186192 A1.
Regarding Claim 3, Knijnik teaches the limitations of claim 2,
However, Knijnik does not explicitly teach but Xia does teach:
wherein the machine learning model is a deep learning model. [see at least: (Para 0096) “For example, the user can consider assigning or not assigning the task to or within the recommended project/space. To this end, in some embodiments, project recommendation module 516 can implement a learning algorithm such as a CNN or other deep learning algorithm. In such embodiments, the learning algorithm can be trained using machine learning techniques with a modeling dataset (e.g., the modeling dataset generated for project recommendation module 516, as described above) to predict (i.e., identify) a project/space within the organization’s project management application that is most relevant to a given (input) task.”]
Further, 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 machine model project management recommendation method (Knijnik) with a deep learning model (Xia). One of ordinary skill would have recognized the benefits of using a deep learning model to handle diverse data sets and output refined results. Further, one of ordinary skill would recognize using a deep learning in a system using FPGA hardware would yield predictable results, see Knijnik (Para 0162) “Method steps can also be performed by, and apparatus of the invention can be implemented as, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit)”, and (Para 0069) “The program controller 128 in the server machine 120 coordinates readings of the database storage 104 and warehouse database 106 through the use of a machine learning engine, which may be based upon a commercial artificial intelligence (AI) tool”. Simply substituting a machine learning model for a deep learning model would yield predictable results to one of ordinary skill.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST)
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/B.L.T./
Examiner, Art Unit 3626
/NATHAN C UBER/Supervisory Patent Examiner, Art Unit 3626