DETAILED ACTION
Status of Claims
This is a non-final action in reply to the application filed on May 28, 2024.
Claims 1-20 are currently pending and have been examined.
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 Statements filed on 5/28/2024 has been considered. Initialed copies of the Form 1449 are enclosed herewith.
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more. Per MPEP 2106.03 Eligibility Step 1: The Four Categories of Statutory Subject Matter [R-07.2022]. Step 1 is directed to determining whether or not the claims fall within a statutory class. Herein, claims 1-7 falls within statutory class of a process, claims 8-14 falls within statutory class of a machine and claims 15-20 falls within statutory class of an article of manufacturing. Hence, the claims qualify as potentially eligible subject matter under 35 U.S.C §101. With Step 1 being directed to a statutory category, per MPEP 2106.04 Eligibility Step 2A: Whether a Claim is Directed to a Judicial Exception [R-07.2022]. Step 2 is the two-part analysis from Alice Corp. (also called the Mayo test). The 2019 PEG makes two changes in Step 2A: It sets forth new procedure for Step 2A (called “revised Step 2A”) under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. The two-prong inquiry is as follows: Prong One: evaluate whether the claim recites a judicial exception. If claim recites an exception, then Prong Two: evaluate whether the claim recites additional elements that integrate the exception into a practical application of the exception. The claim(s) recite(s) the following abstract idea indicated by non-boldface font and additional limitations indicated by boldface font:
Claim 1:
receiving a feature request;
extracting a plurality of feature keywords by processing one or more descriptions of the feature request;
identifying a plurality of team-specific keywords for a work item associated with the feature request; generating a work item vector using the team-specific keywords;
identifying a plurality of prior work items that are related to the team-specific keywords;
generating a plurality of prior work item vectors, wherein each respective prior work item vector corresponds to a respective prior work item, among the plurality of identified prior work items; calculating a similarity score between the work item vector and each of the prior work item vectors; and estimating a time to complete the work item based on the similarity score.
Claim 8:
one or more computer processors; and one or more memories collectively containing one or more programs, which, when executed by the one or more computer processors, perform operations, the operations comprising:
receiving a feature request;
extracting a plurality of feature keywords by processing descriptions of the feature request; identifying a plurality of team-specific keywords for a work item associated with the feature request; generating a work item vector using the team-specific keywords;
identifying a plurality of prior work items that are related to the team-specific keywords;
generating a plurality of prior work item vectors, wherein each respective prior work item vector corresponds to a respective prior work item, among the plurality of identified prior work items; calculating a similarity score between the work item vector and each of the prior work item vectors; and estimating a time to complete the work item based on the similarity score.
Claim 15:
receiving a feature request;
extracting a plurality of feature keywords by processing one or more descriptions of the feature request;
identifying a plurality of team-specific keywords for a work item associated with the feature request; generating a work item vector using the team-specific keywords;
identifying a plurality of prior work items that are related to the team-specific keywords;
generating a plurality of prior work item vectors, wherein each respective prior work item vector corresponds to a respective prior work item, among the plurality of identified prior work items; calculating a similarity score between the work item vector and each of the prior work item vectors; and estimating a time to complete the work item based on the similarity score.
Per Prong One of Step 2A, the identified recitation of an abstract idea falls within at least one of the Abstract Idea Groupings consisting of: Mathematical Concepts, Mental Processes, or Certain Methods of Organizing Human Activity. Particularly, the identified recitation falls within Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion. Per Prong Two of Step 2A, this judicial exception is not integrated into a practical application because the claim as a whole does not integrate the identified abstract idea into a practical application. The one or more computer processors and one or more memories is recited at a high level of generality, i.e., as a generic computing and processing system. This one or more computer processors and one or more memories is no more than mere instructions to apply the exception using a generic computing devices each comprising at least a processor, memory and display device. Further, processor configured to cause receiving/determining/transmitting data is mere instruction to apply an exception using a generic computer component which cannot integrate a judicial exception into a practical application. Accordingly, this/these additional element(s) does/do not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Thus, since the claims are directed to the determined judicial exception in view of the two prongs of Step 2A, MPEP 2106.05 Eligibility Step 2B: Whether a Claim Amounts to Significantly More [R-07.2022] is directed to Step 2B. Therein, per Step 2B the additional elements and combinations therewith are examined in the claims to determine whether the claims as a whole amounts to significantly more than the judicial exception. It is noted here that the additional elements are to be considered both individually and as an ordered combination. In this case, the claims each at most comprise additional elements of a one or more computer processors and one or more memories. Taken individually, the additional limitations each are generically recited and thus does not add significantly more to the respective limitations. Further, executing all the steps/functions by a user/service subsystem is mere instruction to apply an exception using a generic computer component which cannot provide an inventive concept in Step 2B (or, looking back to Step 2A, cannot integrate a judicial exception into a practical application). For further support, the Applicant’s specification supports the claims being directed to use of a generic one or more computer processors and one or more memories type structure at paragraphs 0024: “PROCESSOR SET 110 includes one, or more, computer processors of any type now known or to be developed in the future. Processing circuitry 120 may be distributed over multiple packages, for example, multiple, coordinated integrated circuit chips.” And paragraph 0027: “VOLATILE MEMORY 112 is any type of volatile memory now known or to be developed in the future. Examples include dynamic type random access memory (RAM) or static type RAM. Typically, volatile memory 112 is characterized by random access, but this is not required unless affirmatively indicated. In computer 101, the volatile memory 112 is located in a single package and is internal to computer 101, but, alternatively or additionally, the volatile memory may be distributed over multiple packages and/or located externally with respect to computer 101.” See also figure 1 and paragraphs 0023-0028.
Taken as an ordered combination, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the limitations are directed to limitations referenced in Alice Corp. that are not enough to qualify as significantly more when recited in a claim with an abstract idea include, as a non-limiting or non-exclusive examples: i. Adding the words "apply it" (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, e.g., a limitation indicating that a particular function such as creating and maintaining electronic records is performed by a computer, as discussed in Alice Corp., 134 S. Ct. at 2360, 110 USPQ2d at 1984 (see MPEP § 2106.05(f)); ii. Simply appending well-understood, routine, conventional activities previously known to the industry, specified at a high level of generality, to the judicial exception, e.g., a claim to an abstract idea requiring no more than a generic computer to perform generic computer functions that are well-understood, routine and conventional activities previously known to the industry, as discussed in Alice Corp., 134 S. Ct. at 2359-60, 110 USPQ2d at 1984 (see MPEP § 2106.05(d)); iii. Adding insignificant extra-solution activity to the judicial exception, e.g., mere data gathering in conjunction with a law of nature or abstract idea such as a step of obtaining information about credit card transactions so that the information can be analyzed by an abstract mental process, as discussed in CyberSource v. Retail Decisions, Inc., 654 F.3d 1366, 1375, 99 USPQ2d 1690, 1694 (Fed. Cir. 2011) (see MPEP § 2106.05(g)); or v. Generally linking the use of the judicial exception to a particular technological environment or field of use, e.g., a claim describing how the abstract idea of hedging could be used in the commodities and energy markets, as discussed in Bilski v. Kappos, 561 U.S. 593, 595, 95 USPQ2d 1001, 1010 (2010) or a claim limiting the use of a mathematical formula to the petrochemical and oil-refining fields, as discussed in Parker v. Flook. The courts have recognized the following computer functions inter alia to be well-understood, routine, and conventional functions when they are claimed in a merely generic manner: performing repetitive calculations; receiving, processing, and storing data (e.g., the present claims); electronically scanning or extracting data; electronic recordkeeping; automating mental tasks (e.g., process/machine for performing the present claims); and receiving or transmitting data (e.g., the present claims). The dependent claims 2-7, 9-14 and 16-20 do not cure the above stated deficiencies, and in particular, the dependent claims further narrow the abstract idea without reciting additional elements that integrate the exception into a practical application of the exception or providing significantly more than the abstract idea. Claims 2, 9 and 16 further limit the abstract idea by assigning a weight to each respective prior work item based on the similarity score between the work item vector and a respective prior work item vector, wherein each respective prior work item has a respective recorded historical time; for each prior work item, multiplying the recorded historical time by the assigned weight to generate a weighted historical time; and calculating the time to complete the work item by summing the weighted historical times for each prior work item (a more detailed abstract idea remains an abstract idea). Claims 3, 10 and 17 further limit the abstract idea by selecting one or more prior work items, from the plurality of prior work items, that each has a similarity score exceeding a defined threshold; and estimating the time to complete the work item based on the similarity scores of the one or more prior work items (a more detailed abstract idea remains an abstract idea). Claims 4 and 11 further limit the abstract idea that calculating the similarity score between the work item vector and each of the prior work item vectors comprises using a cosine similarity metric or a distance similarity metric (a more detailed abstract idea remains an abstract idea). Claims 5, 12 and 18 further limit the abstract idea that identifying the plurality of team-specific keywords for the work item comprises searching a keyword correlation database that stores mappings between feature keywords and team-specific keywords extracted from one or more completed feature requests (a more detailed abstract idea remains an abstract idea). Claims 6, 13 and 19 further limit the abstract idea by extracting a plurality of completed feature keywords by processing descriptions of a completed feature request; identifying a plurality of team-specific prior work items associated with the completed feature request; for each identified team-specific prior work item associated with the completed feature request, extracting a plurality of team-specific prior work item keywords; and generating one or more entries in the keyword correlation database that maps the plurality of completed feature keywords to the plurality of team-specific prior work item keywords (a more detailed abstract idea remains an abstract idea). And claims 7, 14 and 20 further limit the abstract idea by accessing a pre-planning correlation database to identify one or more pre-planning activities related to each of the plurality of prior work items, wherein each respective prior work item has a respective first recorded historical time, and each respective pre-planning activity has a respective second recorded historical time; and calculating the time to complete the work item using the similarity scores, the first recorded historical times, and the second recorded historical times (a more detailed abstract idea remains an abstract idea). The identified recitation of the dependents claims falls within the Mental Processes, concepts performed in the human mind including observations, evaluation, judgement and opinion and Mathematical Concepts such as mathematical relationships and calculations. Since there are no elements or ordered combination of elements that amount to significantly more than the judicial exception, the claims are not eligible subject matter under 35 USC §101. Thus, viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself. Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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 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 1-4, 7-11, 14-17 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al., (US 2023/0186203 A1) hereinafter “Kulkarni” in view of Rajkumar Ponnusamy (US 2021/0224722 A1) hereinafter “Ponnusamy”.
Claim 1:
Kulkarni as shown discloses a method, the method comprising:
receiving a feature request (¶ 0002: “receiving, by a device, project management data that includes text descriptions related to one or more requirements associated with a project”);
extracting a plurality of feature keywords by processing one or more descriptions of the feature request (¶ 0024: “the bag of words model may then extract features from the text descriptions to find word occurrences”);
identifying a plurality of team-specific keywords for a work item associated with the feature request (¶ 0013: “the text descriptions may relate to requirements, user stories, epics, features, tasks, risks, issues, actions, decisions, change requests, feedback, milestones, deliverables, and/or any other suitable functional entity associated with a project.” ¶0016: “the text description associated with a functional entity may include a title and a natural language description that includes one or more text chunks (e.g., sentences, paragraphs, and/or bullet points, among other examples) that are used to document the respective functional entity.” And ¶0024: “a frequency of each word may be treated as a classifier for determining a similarity metric between the new and historical data”);
generating a work item vector using the team-specific keywords (¶ 0024: “each text chunk may be converted into a vector […] FIG. 1B, reference number 135 illustrates an example where various text chunks that relate to user interfaces are converted to vectors, and cosines of angles between the various vectors are used to determine similarity metrics.”);
identifying a plurality of prior work items that are related to the team-specific keywords (¶0023: “given a set of work items associated with a project, the dependency management system may process the set of work items to identify historical data (e.g., text descriptions) associated with dependencies that may be relevant to the set of work items and to identify new data (e.g., text descriptions) associated with the work items (e.g., new or active user stories).” See also ¶ 0019: “the artificial intelligence model may include an NLP-based model or an artificial intelligence engine that is trained using historical data related to closed work items (e.g., work items that have been completed)” and ¶ 0019);
generating a plurality of prior work item vectors, wherein each respective prior work item vector corresponds to a respective prior work item, among the plurality of identified prior work items (¶ 0024: “each text chunk may be converted into a vector […] FIG. 1B, reference number 135 illustrates an example where various text chunks that relate to user interfaces are converted to vectors, and cosines of angles between the various vectors are used to determine similarity metrics.”);
calculating a similarity score between the work item vector and each of the prior work item vectors; and (¶ 0022: “an artificial intelligence model that can use text similarities to predict similarities between different requirements, user stories, epics, features, tasks, risks, issues, actions, decisions, change requests, feedback, milestones, deliverables, and/or other suitable functional entities,” ¶0023: “given a first text chunk and a second text chunk, the dependency management system may determine one or more similarity metrics, such as a cosine similarity that represents a closeness between the first text chunk and the second text chunk based on a meaning and/or surface of the first text chunk and the second text chunk.” See also ¶0024-0025: “the similarity metrics may be mapped to confidence scores or confidence levels that represent how similar any two text chunks are in terms of semantic meaning and/or structure,”);
Kulkarni as explained above calculate the similarity score between two vectors. Kulkarni is silent with regard to the following limitations. However, Ponnusamy in an analogous art of work items/task management for the purpose of providing the following limitations as shown does:
estimating a time to complete the work item based on the similarity score (¶0020: “cloud server further calculates a weighted arithmetic mean of the time ratios for the completed tasks using the similarity score for each completed task as the corresponding weight, and a total amount of time spent on the current task up to the determined point in time using data from the activity database server. Finally, the cloud server can calculate the amount of time required to complete the current task by multiplying the weighted arithmetic mean and the total amount time spent on the current task up to the determined point in time”);
Both Kulkarni and Ponnusamy teach work items/task management. Kulkarni teaches in the ¶ 0010: “In a project management context, a dependency is a logical, constraint-based, or preferential relationship between two work items (which may be referred to herein interchangeably as work items, tasks, and/or activities, among other examples) such that starting and/or finishing a first task is dependent on a second task starting and/or finishing.” Ponnusamy teaches in ¶0018: “estimating time required to complete a current task.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Ponnusamy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ponnusamy to the teaching of Kulkarni would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as estimating a time to complete the work item based on the similarity score into similar systems. Further, as noted by Ponnusamy “the task completion estimating module 125 can further alerts the user whenever the amount of time 819 exceeds or is below a predetermined threshold, so that the user may considering reassigning the task to another user who is more appropriate to work on the selected task.” (Ponnusamy, ¶ 0096).
Claims 8 and 15:
The limitations of claims 8 and 15 (¶0004: “a non-transitory computer-readable medium that stores a set of instructions for a device”) encompass substantially the same scope as claim 1. Accordingly, those similar limitations are rejected in substantially the same manner as claim 1, as described above. The following are the limitations of claim 8 that differ from claim 1.
Kulkarni as shown discloses a system, the system comprising:
one or more computer processors; and one or more memories collectively containing one or more programs, which, when executed by the one or more computer processors, perform operations, the operations comprising (Figures 2 and 3);
Claims 2, 9 and 16:
Kulkarni is silent with regard to the following limitations. However, Ponnusamy in an analogous art of work items/task management for the purpose of providing the following limitations as shown does:
wherein estimating the time to complete the work item comprises: assigning a weight to each respective prior work item based on the similarity score between the work item vector and a respective prior work item vector (¶ 0020: “The cloud server further calculates a weighted arithmetic mean of the time ratios for the completed tasks using the similarity score for each completed task as the corresponding weight,”);
wherein each respective prior work item has a respective recorded historical time; (¶ 0040: “The prediction can be based on historical task completion data of one or more past time periods and can be made using machine learning techniques. The forecast engine can first compute historical completion rates of the tasks performed during a past time period (e.g., end of quarter to the same relative day as the current date in the quarter) using a number of relevant attributes of the tasks.”);
for each prior work item, multiplying the recorded historical time by the assigned weight to generate a weighted historical time; and calculating the time to complete the work item by summing the weighted historical times for each prior work item (¶ 0020: “the cloud server can calculate the amount of time required to complete the current task by multiplying the weighted arithmetic mean and the total amount time spent on the current task up to the determined point in time.” See also Figure 7);
Both Kulkarni and Ponnusamy teach work items/task management. Kulkarni teaches in the ¶ 0010: “In a project management context, a dependency is a logical, constraint-based, or preferential relationship between two work items (which may be referred to herein interchangeably as work items, tasks, and/or activities, among other examples) such that starting and/or finishing a first task is dependent on a second task starting and/or finishing.” Ponnusamy teaches in ¶0018: “estimating time required to complete a current task.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Ponnusamy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ponnusamy to the teaching of Kulkarni would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein estimating the time to complete the work item comprises: assigning a weight to each respective prior work item based on the similarity score between the work item vector and a respective prior work item vector, wherein each respective prior work item has a respective recorded historical time; for each prior work item, multiplying the recorded historical time by the assigned weight to generate a weighted historical time; and calculating the time to complete the work item by summing the weighted historical times for each prior work item into similar systems. Further, as noted by Ponnusamy “the task completion estimating module 125 can further alerts the user whenever the amount of time 819 exceeds or is below a predetermined threshold, so that the user may considering reassigning the task to another user who is more appropriate to work on the selected task.” (Ponnusamy, ¶ 0096).
Claims 3, 10 and 17:
Kulkarni as shown discloses the following limitations:
selecting one or more prior work items, from the plurality of prior work items, that each has a similarity score exceeding a defined threshold; and (¶ 0025: “ the similarity metrics may be mapped to confidence scores or confidence levels that represent how similar any two text chunks are in terms of semantic meaning and/or structure, and text chunks with confidence levels or confidence scores that satisfy a threshold”);
Kulkarni is silent with regard to the following limitations. However, Ponnusamy in an analogous art of work items/task management for the purpose of providing the following limitations as shown does:
estimating the time to complete the work item based on the similarity scores of the one or more prior work items (¶0020: “cloud server further calculates a weighted arithmetic mean of the time ratios for the completed tasks using the similarity score for each completed task as the corresponding weight, and a total amount of time spent on the current task up to the determined point in time using data from the activity database server. Finally, the cloud server can calculate the amount of time required to complete the current task by multiplying the weighted arithmetic mean and the total amount time spent on the current task up to the determined point in time”);
Both Kulkarni and Ponnusamy teach work items/task management. Kulkarni teaches in the ¶ 0010: “In a project management context, a dependency is a logical, constraint-based, or preferential relationship between two work items (which may be referred to herein interchangeably as work items, tasks, and/or activities, among other examples) such that starting and/or finishing a first task is dependent on a second task starting and/or finishing.” Ponnusamy teaches in ¶0018: “estimating time required to complete a current task.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Ponnusamy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ponnusamy to the teaching of Kulkarni would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as estimating a time to complete the work item based on the similarity score into similar systems. Further, as noted by Ponnusamy “the task completion estimating module 125 can further alerts the user whenever the amount of time 819 exceeds or is below a predetermined threshold, so that the user may considering reassigning the task to another user who is more appropriate to work on the selected task.” (Ponnusamy, ¶ 0096).
Claims 4 and 11:
Kulkarni as shown discloses the following limitations:
wherein calculating the similarity score between the work item vector and each of the prior work item vectors comprises using a cosine similarity metric or a distance similarity metric (¶ 0024: “The bag of words model may then determine a cosine similarity between the first text chunk and the second text chunk, where the cosine similarity measures the cosine of an angle between two vectors”);
Claims 7, 14 and 20:
Kulkarni teaches in ¶ 0021: “the artificial intelligence model used by the dependency management system may be trained with historical data (e.g., titles and/or natural language descriptions) related to closed work items, which may be compared with text descriptions associated with new work items”. Kulkarni is silent with regard to the following limitations. However, Ponnusamy in an analogous art of work items/task management for the purpose of providing the following limitations as shown does:
further comprising: accessing a pre-planning correlation database to identify one or more pre-planning activities related to each of the plurality of prior work items, wherein each respective prior work item has a respective first recorded historical time, and each respective pre-planning activity has a respective second recorded historical time; and (¶ 0040: “Analytics module 117 can include a forecast engine used to predict completion of tasks that are scheduled to be completed within a particular time period. The prediction can be based on historical task completion data of one or more past time periods and can be made using machine learning techniques. The forecast engine can first compute historical completion rates of the tasks performed during a past time period (e.g., end of quarter to the same relative day as the current date in the quarter) using a number of relevant attributes of the tasks. The forecast engine intelligently smoothens and selects the completion rates to be applied to the current time period, such that the projection is more accurate.” See also ¶ 0002: “estimating time required to complete a task based on historical data and time already spent on the task.”);
calculating the time to complete the work item using the similarity scores, the first recorded historical times, and the second recorded historical times (¶0020: “cloud server further calculates a weighted arithmetic mean of the time ratios for the completed tasks using the similarity score for each completed task as the corresponding weight, and a total amount of time spent on the current task up to the determined point in time using data from the activity database server. Finally, the cloud server can calculate the amount of time required to complete the current task by multiplying the weighted arithmetic mean and the total amount time spent on the current task up to the determined point in time”);
Both Kulkarni and Ponnusamy teach work items/task management. Kulkarni teaches in the ¶ 0010: “In a project management context, a dependency is a logical, constraint-based, or preferential relationship between two work items (which may be referred to herein interchangeably as work items, tasks, and/or activities, among other examples) such that starting and/or finishing a first task is dependent on a second task starting and/or finishing.” Ponnusamy teaches in ¶0018: “estimating time required to complete a current task.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Ponnusamy would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Ponnusamy to the teaching of Kulkarni would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as further comprising: accessing a pre-planning correlation database to identify one or more pre-planning activities related to each of the plurality of prior work items, wherein each respective prior work item has a respective first recorded historical time, and each respective pre-planning activity has a respective second recorded historical time; and calculating the time to complete the work item using the similarity scores, the first recorded historical times, and the second recorded historical times into similar systems. Further, as noted by Ponnusamy “the task completion estimating module 125 can further alerts the user whenever the amount of time 819 exceeds or is below a predetermined threshold, so that the user may considering reassigning the task to another user who is more appropriate to work on the selected task.” (Ponnusamy, ¶ 0096).
Claims 5-6, 12-13 and 18-19 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al., (US 2023/0186203 A1) hereinafter “Kulkarni” in view of Rajkumar Ponnusamy (US 2021/0224722 A1) hereinafter “Ponnusamy” as applied to claims 1, 8 and 15 above, further in view of Vijayaraghavan et al., (US 2022/0350967 A1) hereinafter “Vijayaraghavan”.
Claims 5, 12 and 18:
Kulkarni teaches in ¶ 0013: “the text descriptions may relate to requirements, user stories, epics, features, tasks, risks, issues, actions, decisions, change requests, feedback, milestones, deliverables, and/or any other suitable functional entity associated with a project.” ¶0016: “the text description associated with a functional entity may include a title and a natural language description that includes one or more text chunks (e.g., sentences, paragraphs, and/or bullet points, among other examples) that are used to document the respective functional entity.” Kulkarni in view of Ponnusamy is silent with regard to the following limitations. However, Vijayaraghavan in an analogous art of work items management for the purpose of providing the following limitations as shown does:
wherein identifying the plurality of team-specific keywords for the work item comprises searching a keyword correlation database that stores mappings between feature keywords and team-specific keywords extracted from one or more completed feature requests (¶ 0032: “the mappings are identified based on the title portion and/or the description portion of each work item included in the final work item data. The traceability system may perform a cross correlation analysis based on the title portions and/or the description portions and may predict possible associations between the work items based on the cross correlation analysis. The traceability system may identify the mappings between the work items based on predicting the possible associations” and ¶ 0014: “The traceability system may suggest and aid in achieving traceability and/or mappings between work items ranging across a project lifecycle by utilizing a machine learning model in combination with multiple procedures, such as data cleansing, synonyms matching, utilizing an exhaustive dictionary of process and/or project keywords and abbreviations, and/or the like.”);
Both Kulkarni and Vijayaraghavan teach work items/task management. Kulkarni teaches in the ¶ 0010: “In a project management context, a dependency is a logical, constraint-based, or preferential relationship between two work items (which may be referred to herein interchangeably as work items, tasks, and/or activities, among other examples) such that starting and/or finishing a first task is dependent on a second task starting and/or finishing.” Vijayaraghavan teaches in the Abstract: “A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Vijayaraghavan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Vijayaraghavan to the teaching of Kulkarni in view of Ponnusamy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as wherein identifying the plurality of team-specific keywords for the work item comprises searching a keyword correlation database that stores mappings between feature keywords and team-specific keywords extracted from one or more completed feature requests into similar systems. Further, as noted by Vijayaraghavan “the task completion estimating module 125 can further alerts the user whenever the amount of time 819 exceeds or is below a predetermined threshold, so that the user may considering reassigning the task to another user who is more appropriate to work on the selected task.” (Ponnusamy, ¶ 0096).
Claims 6, 13 and 19:
Kulkarni as shown discloses a method, the method comprising:
extracting a plurality of completed feature keywords by processing descriptions of a completed feature request; (¶ 0024: “the bag of words model may then extract features from the text descriptions to find word occurrences”);
identifying a plurality of team-specific prior work items associated with the completed feature request; for each identified team-specific prior work item associated with the completed feature request, extracting a plurality of team-specific prior work item keywords (¶ 0013: “the text descriptions may relate to requirements, user stories, epics, features, tasks, risks, issues, actions, decisions, change requests, feedback, milestones, deliverables, and/or any other suitable functional entity associated with a project.” ¶ 0016: “the text description associated with a functional entity may include a title and a natural language description that includes one or more text chunks (e.g., sentences, paragraphs, and/or bullet points, among other examples) that are used to document the respective functional entity.” ¶ 0024: “the bag of words model may then extract features from the text descriptions to find word occurrences, and a frequency of each word may be treated as a classifier for determining a similarity metric between the new and historical data” and ¶ 0025: “the similarity metrics may be mapped to confidence scores or confidence levels that represent how similar any two text chunks are in terms of semantic meaning and/or structure, and text chunks with confidence levels or confidence scores that satisfy a threshold”);
Kulkarni in view of Ponnusamy is silent with regard to the following limitations. However, Vijayaraghavan in an analogous art of work items management for the purpose of providing the following limitations as shown does:
and generating one or more entries in the keyword correlation database that maps the plurality of completed feature keywords to the plurality of team-specific prior work item keywords (¶ 0024-0025: “the traceability system performs NLP to identify keywords in the final work item data. In some implementations, the NLP includes a token-based NLP technique, such as a technique using regular expressions, to identify the keywords. For example, the traceability system may reference a data structure that stores regular expressions that may be used to identify a keyword associated with the project, a work item, and/or the like. […] the traceability system may execute an approximation-based NLP technique to identify data that satisfies a threshold level of similarity with data stored in a data structure. In this case, the traceability system may set a threshold level of similarity (e.g., a percentage, a number of characters, and/or the like), and may compare information included in the final work order data to information stored in the data structure.” See also ¶ 0032: and ¶ 0014);
Both Kulkarni and Vijayaraghavan teach work items/task management. Kulkarni teaches in the ¶ 0010: “In a project management context, a dependency is a logical, constraint-based, or preferential relationship between two work items (which may be referred to herein interchangeably as work items, tasks, and/or activities, among other examples) such that starting and/or finishing a first task is dependent on a second task starting and/or finishing.” Vijayaraghavan teaches in the Abstract: “A device may receive work item data identifying work items associated with requirements from different tools of a project and may perform data cleansing to remove and/or modify particular words from the work item data and to generate cleansed work item data.” Thus, they are deemed to be analogous references as they are reasonably pertinent to each other and are directed towards solving similar problems within the same environment. One of ordinary skill in the art would have recognized that applying the known technique of Vijayaraghavan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Vijayaraghavan to the teaching of Kulkarni in view of Ponnusamy would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate such as generating one or more entries in the keyword correlation database that maps the plurality of completed feature keywords to the plurality of team-specific prior work item keywords into similar systems. Further, as noted by Vijayaraghavan “the task completion estimating module 125 can further alerts the user whenever the amount of time 819 exceeds or is below a predetermined threshold, so that the user may considering reassigning the task to another user who is more appropriate to work on the selected task.” (Ponnusamy, ¶ 0096).
Conclusion
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623