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 .
Notice to Applicant
This is the first Non-Final Office Action in response to Application Serial Number: 19/151,699, filed on July 29, 2025. Claims 1-20 are pending in this application and have been rejected below.
Priority
Acknowledgment is made of Applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55. The Examiner has noted this Application is a National Stage entry of PCT/JP2023/044341 filed December 12, 2023, which claims foreign priority to Japan Application No. JP2023-018764 filed February 09, 2023.
Information Disclosure Statement
The information disclosure statement (IDS) filed on July 29, 2025 complies with the provisions of 37 CFR 1.97, 1.98 and MPEP § 609 and is considered by the Examiner.
Specification
The abstract of the disclosure is objected to because it exceeds 150 words in length. Correction is required. See MPEP § 608.01(b).
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.
Step 1: The claimed subject matter falls within the four statutory categories of patentable subject matter.
Claims 1-10 are directed towards a system, claims 11-15 are directed towards a method, claims 16-19 are directed towards a non-transitory computer readable medium, and claim 20 is directed towards a device, all of which are among the statutory categories of invention.
Step 2A – Prong One: The claims recite an abstract idea.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1 and 10 recite using a trained model to create a project plan using previous project plan results and claim 20 recites acquiring data used to train a model to output project plans.
Claim 1 recites limitations directed to an abstract idea based on certain methods of organizing human activity and mental processes. Specifically, receive plan creation information including a classification of a new project to be created and at least one of a goal, a delivery deadline, or a budget of the new project; create a plan based on the received plan creation information for a project including one or more tasks for implementing the new project using, as inputs, actual result data including a classification of a past project, at least one of a goal, a delivery deadline, or a budget of the past project, and the classification of one or more actual result tasks in which at least one of the goal, the delivery deadline, or the budget of the past project have been achieved; and output the created plan constitutes methods based on business relations, as well as, observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a system comprising memory storing instructions executable by a processor and using a trained model trained to output a plan do es not take the claim out of the certain methods of organizing human activity and mental processes groupings. Thus the claim recites an abstract idea. Claims 11 and 16 recite certain method of organizing human activity and mental processes for similar reasons as claim 1.
Claim 20 recites limitations directed to an abstract idea based on mental processes. Specifically, acquire training data that is actual result data including a classification of a past project, at least one of a goal, a delivery deadline, or a budget of the past project, and the classification of one or more actual result tasks in which at least one of the goal, the delivery deadline, or the budget of the past project have been achieved constitutes methods based on observations, evaluations, judgements and/or opinion that can be performed mentally by a combination of the human mind and a human using pen and paper. The recitation of a device comprising memory storing instructions executable by a processor does not take the claim out of the mental processes grouping. Thus the claim recites an abstract idea.
Step 2A – Prong Two: The judicial exception is not integrated into a practical application.
The judicial exception is not integrated into a practical application. In particular, claim 1 recites a system comprising memory storing instructions executable by a processor at a high-level of generality such that it amounts to no more than generic computer components used as tools to apply the instructions of the abstract idea; see MPEP 2106.05(f). Additionally, claim 1 recites using a trained model trained to output a plan. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the trained model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea and merely limits the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h). Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. Claim 1 as a whole, looking at the additional elements individually and in combination, does not integrate the judicial exception into a practical application and therefore is directed to an abstract idea. The method recited in claim 11 and non-transitory computer readable medium storing instructions executable by a processor in claim 16 also amount to no more than mere instructions to apply the exception using generic computer components, as well as, limiting the abstract idea to a particular technological environment or field of use. Thus, the additional elements recited in claims 11 and 16 do not integrate the abstract idea into practical application for similar reasons as claim 1.
Claim 20 recites generate a trained model trained to output a plan for a project including one or more tasks for implementing a new project using the training data as an input. The general use of a machine learning technique does not provide a meaningful limitation to transform the abstract idea into a practical application. Therefore, the generated trained model disclosed in the claims are solely used as a tool to perform the instructions of the abstract idea and merely limits the abstract idea to a particular technological environment or field of use; see MPEP 2106.05(h).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception.
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. The additional elements in the claims other than the abstract idea per se, including system comprising memory storing instructions executable by a processor and non-transitory computer readable medium storing instructions executable by a processor amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as receiving or transmitting data over a network, e.g., using the Internet to gather data, 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); electronic recordkeeping, Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755 (updating an activity log) and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II). The trained model recited in the claims are disclosed at a high-level of generality (see at least Specification [0035]) and does not amount to significantly more than the abstract idea. Viewed as a whole, these additional claim elements do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself. Therefore, since there are no limitations in the claim that transform the abstract idea into a patent eligible application such that the claim amounts to significantly more than the abstract idea itself, the claims are rejected under 35 U.S.C. § 101 as being directed to non-statutory subject matter.
§ 101 Analysis of the dependent claims.
Regarding the dependent claims, dependent claims 2-10, 12-15 and 17-19 recite steps that further narrow the abstract idea of creating a project plan using. No additional elements are disclosed in the dependent claims that were not considered in the independent claims. Therefore claims2-10, 12-15 and 17-19 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 –
Claims 1-4, 8, 11-14 and 16-20 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Meharwade et al., U.S. Publication No. 2021/0125124 [hereinafter Meharwade].
Referring to Claim 1, Meharwade teaches:
A plan creation system comprising:
at least one memory storing instructions, and at least one processor configured to execute the instructions to;
receive plan creation information including a classification of a new project to be created and at least one of a goal, a delivery deadline, or a budget of the new project (Meharwade, [0014]), “The project management platform may receive new project data identifying project information associated with a new project”; (Meharwade, [0031]), “… the new project data may be received as a project description that is sorted or organized according to particular information associated with a complexity and/or a size of the new project… For example, to provide information on a complexity of the new project, the prompts may request the user to provide information indicating the type of the new project (e.g., software, construction, manufacturing, design, presentation, and/or the like), subject matter of the new project… timeliness requirements (e.g., a requested release schedule that includes dates or times of completion for particular tasks or subtasks) …”
create a plan based on the received plan creation information by using a trained model trained to output a plan for a project including one or more tasks for implementing the new project using, as inputs, actual result data including a classification of a past project, at least one of a goal, a delivery deadline, or a budget of the past project, and the classification of one or more actual result tasks in which at least one of the goal, the delivery deadline, or the budget of the past project have been achieved (Meharwade, [0017]), “the project management platform may use a machine learning model, that is trained according to the historical project data of the historical project data structure, to determine a project release plan for a project (e.g., based on characteristics of the project)”; (Meharwade, [0014]), “the project management platform may train a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project. The historical project data may identify or include a plurality of prior projects, subject matter associated with the prior projects, and prior resources consumed by the prior projects. The project management platform may receive new project data identifying project information associated with a new project and convert (e.g., using natural language processing) the new project data to processed new project data. In some implementations, the project management platform may receive resource data identifying resource availability for the new project and process, using the trained machine learning model, the processed new project data and the resource data to determine release information for the new project. The release information may include a release schedule for the new project. The project management platform may perform, according to the release schedule, an action associated with the release information (e.g., generate the project release plan, allocate resources according to the release schedule, and/or the like)”; (Meharwade, [0025]), “the project management platform may use a support vector machine (SVM) classifier technique to generate a non-linear boundary between data items in the training set. In this case, the non-linear boundary is used to classify test data (e.g., data relating project characteristics of prior projects) into a particular class (e.g., a class indicating that a project has a particular complexity or number of story points, a class indicating that a project requires a particular amount of resources to reach a particular release schedule, a class indicating that a project can be released within a timeline based on an availability of particular resources, and/or the like). In some implementations, the project management platform may perform a bucketizing technique of the historical project data”; (Meharwade, [0018]-[0019];[0025]; [0027]); and
output the created plan (Meharwade, [0046]), “the project management platform may generate a project release plan that is configured to be displayed via the user device. For example, a visualization of the project release plan may indicate timing associated with the project release plan, resource allocation (e.g., which resource, when the resource is to be available or utilized, a duration of use of the resource, how the resource is to be utilized (e.g., manually, automatically, semi-automatically, and/or the like), and/or the like) for one or more tasks associated with releasing the new project, priority information for the project, release order information for tasks of the new project, release order for the new project relative to other projects of a project backlog, and/or the like”; (Meharwade, [0038]), “trained machine learning model, to determine release information for the new project. The release information may include information associated with the complexity of the project and/or the size of the project, as described herein. Additionally, or alternatively, the release information may include a release schedule, associated with the determined (or predicted) complexity and/or size, that indicates predicted timing associated with releasing the project. The predicted timing may indicate when certain events or phases associated with tasks of the new project are projected to start and/or end”; (Meharwade, [0043]), “the project management platform performs an action according to a release schedule of the release information…the project management platform may be configured to determine and/or generate a project release plan according to the release information. The project release plan may indicate the release schedule. Additionally, or alternatively, the project release plan may include an iteration velocity (e.g., a speed at which new project or a task of the project is to be completed or can be completed), a quantity of iterations associated with the new project (and/or tasks of the new project), a start date and/or an end date of the project release, and/or the like” (Meharwade, [0045]).
Referring to Claim 2, Meharwade teaches the plan creation system according to claim 1. Meharwade further teaches:
wherein the at least one processor is configured to execute the instructions to create a plan including a plurality of tasks having different granularities according to the classification of the new project to be created (Meharwade, [0047]), “if the project management platform determines that the new project is too complex or too large (e.g., because machine learning model was not trained with historical data associated with projects of the same or similar scope), the machine learning model may request the user to rescope and/or provide the new project as a set of individual subprojects. Additionally, or alternatively, the machine learning model may be configured to automatically identify subprojects of a project and rescope the new project into the plurality of subprojects. For example, based on determining that described individual tasks in the project description have a similar complexity or size, as described herein, as projects associated with the historical data, the project management platform may process descriptions of the individual tasks as individual new projects, as described herein”; (Meharwade, [0031]; [0053]).
Referring to Claim 3, Meharwade teaches the plan creation system according to claim 1. Meharwade further teaches:
wherein a plurality of the actual result tasks that have achieved the goal of the past project includes a relationship among the plurality of actual result tasks, and the at least one processor is configured to execute the instructions to create a plan including an order relationship between two or more tasks or a parallel relationship between two or more tasks (Meharwade, [0047]), “if the project management platform determines that the new project is too complex or too large (e.g., because machine learning model was not trained with historical data associated with projects of the same or similar scope), the machine learning model may request the user to rescope and/or provide the new project as a set of individual subprojects. Additionally, or alternatively, the machine learning model may be configured to automatically identify subprojects of a project and rescope the new project into the plurality of subprojects. For example, based on determining that described individual tasks in the project description have a similar complexity or size, as described herein, as projects associated with the historical data, the project management platform may process descriptions of the individual tasks as individual new projects, as described herein”; (Meharwade, [0052]-[0053]), “the project management platform receives a project description, resource data, and historical data, and uses natural language processing and/or a machine learning model to generate a project release plan… the project release plan may include a projected release schedule that may indicate a quantity of iterations, an iteration velocity, a start date and/or end date, and/or the like according to a projected complexity and/or size of the project release that are determined from the identified complexity characteristics and size characteristics… the project description may include information identifying or indicating a plurality of complexity characteristics and size characteristics…The complexity characteristics may correspond to any information that is indicative of a complexity of a project, such as a configuration of the project release (e.g., a structure of a building, a relationship between functions or operations of a software product, and/or the like), types of operations involved in the project release… relationships between tasks of the project release (e.g., whether tasks can be performed in parallel or are to be sequential or hierarchical), and/or the like. The size characteristics may correspond to any information that is indicative of a size or scale of the project (e.g., a quantity of materials required for the project, a quantity of types of materials that are to be combined for the project, a quantity of tasks involved, a quantity of different operations involved, a quantity of end products that are to be produced by the project, and/or the like)”.
Referring to Claim 4, Meharwade teaches the plan creation system according to claim 3. Meharwade further teaches:
wherein the relationship among the plurality of actual result tasks that have achieved the goal of the past project includes at least one of presence or absence of a causal relationship among the plurality of actual result tasks, a temporal order among the plurality of actual result tasks, or a relationship of a superordinate concept or a subordinate concept (Meharwade, [0053]), “relationships between tasks of the project release (e.g., whether tasks can be performed in parallel or are to be sequential or hierarchical)”; (Meharwade, [0031]) “tasks and subtasks”; (Meharwade, [0038]), “the release information may include a release schedule, associated with the determined (or predicted) complexity and/or size, that indicates predicted timing associated with releasing the project. The predicted timing may indicate when certain events or phases associated with tasks of the new project are projected to start and/or end”.
Referring to Claim 8, Meharwade teaches the plan creation system according to claim 1. Meharwade further teaches:
wherein the plan includes task assignment information for assigning a task to a person or a machine (Meharwade, [0045]), “the project management platform may determine a project release plan that includes a release schedule and an allocation of resources to perform a release of the project (and/or develop the project). Accordingly, the action may include the project management platform allocating the resources for the release of the new project, reserving the resources for the release of the new project, and/or the like. For example, for a software development project, the project management platform may reserve computing resources (e.g., processing resources, memory resources, communication resources, virtual machines, and/or the like) and/or hardware resources for use in releasing the new project (e.g., for use in performing one or more tasks of developing or releasing the new project)… for another type of project (e.g., a construction project, a service-related project, and/or the like) the project management platform may reserve real estate resources associated with a release the new project (e.g., a location, a building, a room of a building, and/or the like that may be used when the project is being developed and or released). According to some implementations, the project management platform may reserve time of individuals (e.g., or workers) that may be used to assist with a development and/or release of a project or portion of a project. For example, the project management platform may generate, provide, and/or store calendar information associated with an individual's calendar to indicate that the individual is to be working on the new project during a time period associated with the calendar information”; (Meharwade, [0046]), “a visualization of the project release plan may indicate timing associated with the project release plan, resource allocation (e.g., which resource, when the resource is to be available or utilized, a duration of use of the resource, how the resource is to be utilized (e.g., manually, automatically, semi-automatically, and/or the like), and/or the like) for one or more tasks associated with releasing the new project, priority information for the project”; (Meharwade, [0118]).
Referring to Claim 11, Meharwade teaches:
A plan creation method comprising (Meharwade, [0126]):
Claim 11 disclose substantially the same subject matter as claim 1, and is rejected using the same rationale as previously set forth.
Claim 12 disclose substantially the same subject matter as claim 2, and is rejected using the same rationale as previously set forth.
Claim 13 disclose substantially the same subject matter as claim 3, and is rejected using the same rationale as previously set forth.
Claim 14 disclose substantially the same subject matter as claim 4, and is rejected using the same rationale as previously set forth.
Referring to Claim 16, Meharwade teaches:
A non-transitory computer readable medium storing a program causing a computer to execute processing of the method according to claim 11 (Meharwade, [0082]-[0083]).
Claim 17 disclose substantially the same subject matter as claim 12, and is rejected using the same rationale as previously set forth.
Claim 18 disclose substantially the same subject matter as claim 13, and is rejected using the same rationale as previously set forth.
Claim 19 disclose substantially the same subject matter as claim 14, and is rejected using the same rationale as previously set forth.
Referring to Claim 20, Meharwade teaches:
A training device comprising: at least one memory storing instructions, and at least one processor configured to execute the instructions to (Meharwade, [0066]; [0077]; [0082];
acquire training data that is actual result data including a classification of a past project, at least one of a goal, a delivery deadline, or a budget of the past project, and the classification of one or more actual result tasks in which at least one of the goal, the delivery deadline, or the budget of the past project have been achieved; and generate a trained model trained to output a plan for a project including one or more tasks for implementing a new project using the training data as an input (Meharwade, [0086]), “training a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project, wherein the historical project data identifies: a plurality of prior projects, subject matter associated with the prior projects, and prior resources consumed by the prior projects (block 610). For example, the project management platform… may train a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project, as described above. In some implementations, the historical project data identifies a plurality of prior projects, subject matter associated with the prior projects, and prior resources consumed by the prior projects”; (Meharwade, [0017]), “the project management platform may use a machine learning model, that is trained according to the historical project data of the historical project data structure, to determine a project release plan for a project (e.g., based on characteristics of the project)”; (Meharwade, [0014]), “the project management platform may train a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project. The historical project data may identify or include a plurality of prior projects, subject matter associated with the prior projects, and prior resources consumed by the prior projects… using the trained machine learning model, the processed new project data and the resource data to determine release information for the new project. The release information may include a release schedule for the new project. The project management platform may perform, according to the release schedule, an action associated with the release information (e.g., generate the project release plan, allocate resources according to the release schedule, and/or the like)”; (Meharwade, [0038]), “trained machine learning model, to determine release information for the new project. The release information may include information associated with the complexity of the project and/or the size of the project, as described herein. Additionally, or alternatively, the release information may include a release schedule, associated with the determined (or predicted) complexity and/or size, that indicates predicted timing associated with releasing the project. The predicted timing may indicate when certain events or phases associated with tasks of the new project are projected to start and/or end”; (Meharwade, [0021];[0063]; [0027]).
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 5-7, 9 and 15 are rejected under 35 U.S.C. 103 as being unpatentable over Meharwade et al., U.S. Publication No. 2021/0125124 [hereinafter Meharwade], and further in view of Guha et al., U.S. Publication No. 2024/0104467 [hereinafter Guha].
Referring to Claim 5, Meharwade teaches the plan creation system according to claim 1. Meharwade teaches a timeliness requirements (e.g., a requested release schedule that includes dates or times of completion for particular tasks or subtasks) (see par. 0031) and historical data associated with previously completed project releases (e.g., that are associated with a same product, team, and/or set of resources that are to be involved with a project release, where the information identify individual team members and/or characteristics of the team members (e.g., titles, skill sets, levels of experience, years of experience, types of experience or background, and/or the like) (see par. 0054), but Meharwade does not explicitly teach;
wherein the information regarding the actual result task includes a level of proficiency of a worker including a simple work level.
However Guha teaches:
wherein the information regarding the actual result task includes a level of proficiency of a worker including a simple work level (Guha, [0080]), “the AI component 206 can determine, train, and generate a model that can relate to tasks and users, wherein the model can model or be representative of historical performance of tasks by users, characteristics (e.g., education, work experience or skill relating to tasks, age, personality, employment role or position, demographic, or other characteristics) associated with users, attributes associated with tasks, levels of user expertise associated with tasks, environments associated with users or tasks, and/or other features relating to the users (e.g., users 108, 110, and/or 112) and tasks to be performed by users, such as described herein”; (Guha, [0124]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the task requirements and team information in Meharwade to include the task limitation as taught by Guha. The motivation for doing this would have been to improve the method of training a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project in Meharwade (see par. 0002) to efficiently include the results of managing tasks for efficient workflow management (see Guha par. 0001).
Referring to Claim 6, Meharwade in view of Guha teaches the plan creation system according to claim 5. Meharwade teaches timeliness requirements (e.g., a requested release schedule that includes dates or times of completion for particular tasks or subtasks (see par. 0031), but Meharwade does not explicitly teach:
wherein the information regarding the actual result task includes a workload per unit period of one worker according to the level of the proficiency.
However Guha teaches:
wherein the information regarding the actual result task includes a workload per unit period of one worker according to the level of the proficiency (Guha, [0055]), “the task organizer component 204 can determine an amount of time to allocate for the user 108 to perform such particular task based at least in part on the higher level of expertise of the user 108, historical amounts of time for performance of the task by the user 108 or other users (e.g., taking into account their respective levels of expertise with respect to performing the particular task or a similar task), or a predicted amount of time (e.g., as predicted based on the AI-based analysis) that it will take for the user 108 to perform the particular task”; (Guha, [0044]).
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the timeliness requirement in Meharwade to include the task limitation as taught by Guha. The motivation for doing this would have been to improve the method of training a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project in Meharwade (see par. 0002) to efficiently include the results of managing tasks and users in the performance of tasks (see Guha par. 0022).
Referring to Claim 7, Meharwade teaches the plan creation system according to claim 1. Meharwade teaches permitting the machine learning model to evolve and adjust overtime (e.g., due to changes in resource usage, availability, capability, type, and/or the like) (see par. 0049), but Meharwade does not explicitly teach:
wherein the plan creation information includes information regarding a progress state, a worker change, a delivery deadline change, and a specification change for each task indicating whether some of the tasks after the project progresses are completed, and the at least one processor is configured to execute the instructions to reconstruct a plan in consideration of the progress state, the worker change, the delivery deadline change, and the specification change for each task.
However Guha teaches:
wherein the plan creation information includes information regarding a progress state, a worker change, a delivery deadline change, and a specification change for each task indicating whether some of the tasks after the project progresses are completed, and the at least one processor is configured to execute the instructions to reconstruct a plan in consideration of the progress state, the worker change, the delivery deadline change, and the specification change for each task (Guha, [0137]), “if the TMC determines that the user has undesirably fallen behind in performing and/or completing the task, the TMC can adaptively adjust certain attributes associated with the task and/or one or more of the other remaining tasks to be performed by the user to account for the user being behind schedule on performing or completing the task. For instance, the TMC can allocate additional time for the user to complete the task, and adjust the scheduling of one or more of the other remaining tasks and/or their allocated times for performance and completion, to try to create a suitable revised schedule for completion of the remaining tasks. Additionally or alternatively, the TMC can determine whether there are improved or alternate instructions for performing the task that the user can utilize to attempt to catch up time-wise in the performance and completion of the task and the other remaining tasks as well, and, if so, can adjust (e.g., adaptively adjust) an attribute(s) associated with the task, e.g., can adjust the instructions for performing the task”; (Guha, [0069]; [0136])
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the adjustments due to changes in Meharwade to include the progress limitations as taught by Guha. The motivation for doing this would have been to improve the method of training a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project in Meharwade (see par. 0002) to efficiently include the results of managing and adaptively adjusting scheduling of tasks (see Guha par. 0119).
Referring to Claim 9, Meharwade teaches the plan creation system according to claim 1. Meharwade teaches a quantity of tasks or subtasks for the new project (e.g., a number of operations that are to be performed), a quantity of different types of tasks or subtasks for the new project (see par. 0031), but Meharwade does not explicitly teach:
wherein the classification of the actual result task includes a major item and a minor item included in the major item.
However Guha teaches:
wherein the classification of the actual result task includes a major item and a minor item included in the major item (Guha, [0037]), “The task-related information can comprise information regarding or relating to, for example, a type of task, sub-tasks of a task, a level of user expertise desired with regard to a task, a location(s) where the task is to be performed, a due date(s) for performance and/or completion of the task or its sub-task(s)”; (Guha, [0065]), “the task organizer component 204 can determine whether a task can and/or should be divided (e.g., split or partitioned) up into multiple sub-tasks, and, if so, can determine the sub-tasks of the task, based at least in part on the results of analyzing information relating to the task, including type of task, attributes associated with the task, historical information relating to performance of the task by users, and/or other task-related information”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the tasks and sub-tasks in Meharwade to include the task limitation as taught by Guha. The motivation for doing this would have been to improve the method of training a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project in Meharwade (see par. 0002) to efficiently include the results of managing tasks and users in the performance of tasks (see Guha par. 0022).
Claim 15 disclose substantially the same subject matter as claim 5, and is rejected using the same rationale as previously set forth.
Claim 10 is rejected under 35 U.S.C. 103 as being unpatentable over Meharwade et al., U.S. Publication No. 2021/0125124 [hereinafter Meharwade], and further in view of Laliberte et al., U.S. Publication No. 2024/0220887 [hereinafter Laliberte].
Referring to Claim 10, Meharwade teaches the plan creation system according to claim 1. Meharwade further teaches:
wherein the at least one processor is configured to execute the instructions to create a plurality of plans, and each of the created plans includes information regarding whether the received delivery deadline is satisfied (Meharwade, [0107]), “generating a project release plan for the plurality of projects, wherein the project is releasing plan includes the release schedule for the plurality of projects and respective allocations of resources for the plurality of projects; and providing the project release plan to a user device for release of the plurality of projects”; (Meharwade, [0117]), “the project information includes a priority of the new project relative to a plurality of projects in the project backlog, the processed new project data is processed by the machine learning model according to the priority of the new project. In a second implementation, alone or in combination with the first implementation, process 800 includes comparing the release schedule to a requested release schedule associated with the new project; determining whether the release schedule satisfies a requirement of the requested release schedule; and generating the project release plan to indicate whether the release schedule satisfies the requirement”; (Meharwade, [0044]), “the project release plan may indicate whether a project requirement of a requested release schedule can be satisfied (e.g., whether a certain project will have a threshold availability, a threshold amount of resources, be released within a threshold time period, and/or the like)”; (Meharwade, [0101])
Meharwade teaches a project release plan satisfying requirements (see par. 004), but Meharwade does not explicitly teach:
each of the created plans includes information regarding whether the received budget is satisfied.
However Laliberte teaches:
each of the created plans includes information regarding whether the received budget is satisfied (Laliberte, [0033]-[0036]), “a Project Attribute Estimator (PAE) system of the present principles predicts a range of a project's cost and/or schedule with prediction intervals by analyzing historical records of project costs and schedules and applying techniques in machine learning and statistics…The stored results/prediction intervals of a PAE of the present principles can then be used with an APM system to, for example: 1) determine and optimally adjust a remaining budget or schedule of a portfolio of projects given a shared budget constraint; 2) optimally select a subset of projects for execution from a set of proposed projects, where the selected set of projects presents an optimal trade-off between cost and benefits, while satisfying shared budget, and scheduling constraints; 3) establish support for budget and schedule proposals”; (Laliberte, [0080]), “… A selected set of projects can present an optimal trade-off between cost and benefits, while satisfying shared budget, and scheduling constraints”.
At the time the invention was filed, it would have been obvious to a person of ordinary skill in the art to have modified the satisfied requirements in Meharwade to include the budget limitation as taught by Laliberte. The motivation for doing this would have been to improve the method of training a machine learning model with historical project data to generate a trained machine learning model that determines or analyzes a release schedule of a project in Meharwade (see par. 0002) to efficiently include the results of predicting a range of estimates of project attributes, such as project cost and scheduling, using machine learning techniques (see Laliberte par. 0001).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Pita et al. (US 20230142105 A1) – A computing platform is configured to: (i) at a first time, input data values for a first set of data variables associated with a given construction project into a first machine-learning model that functions to output a prediction of a first set of reference projects that are similar to the given construction project, (ii) based on historical data for the first set of reference projects, determine a predicted value for a parameter of the given construction project, (iii) at a second time, input data values for a second set of data variables associated with the given construction project into a second machine-learning model that functions to output a prediction of a second set of reference projects that are similar to the given construction project, and (iv) based on historical data for the second set of reference projects, determine an updated predicted value for the parameter of the given construction project.
Hamada et al. (Neural Network Estimation Model to Optimize Timing and Schedule of Software Projects) – This article aims to develop a Neural Network estimation model to manipulate the problem of timing for software projects. The model can predict the estimation value of project time which optimizes the scheduling process, the developed model achieved high accuracy after testing through the test datasets.
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/CRYSTOL STEWART/Primary Examiner, Art Unit 3624
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