DETAILED ACTION
Status of Claims
This is a final action in reply to the response filed on July 1, 2026.
Claims 8, 13 and 15 have been amended.
Claims 10, 12 and 14 have been cancelled.
Claims 21-23 have been added.
Claims 1-9, 11, 13 and 15-23 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 .
Response to Amendments
Applicant’s amendment necessitated the new ground(s) of rejection presented in this Office action.
The rejection of claims 1-9, 11, 13 and 15-23 under 35 USC § 101 is maintained.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 15-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per claim 15 recites “estimating a time to complete the work item based on the similarity score; identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; and generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the one or more defect-related keywords into the estimated time to complete the work item” Examiner is not clear are there two different estimated time to complete the work item? One based on the similarity score and another one based on defect-resolution time and defect-related keywords? Is the total estimated time considering the estimated total time based on the similarity score? And is the estimated defect-resolution time an input from a user or is being calculated as the same estimated time to complete the work item including defect-related keywords? Appropriate correction is required.
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 1-9, 11, 13 and 15-23 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 and 21-23 falls within statutory class of a process, claims 8-9, 11 and 13 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 by searching a keyword correlation database that stores mappings between feature keywords and team-specific keywords extracted from one or more completed feature requests;
generating, based on the plurality of team-specific keywords, one or more suggested team-specific work items for implementing the feature request;
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, and wherein the plurality of prior work item vectors and the work item vector are generated in a same vector space for similarity comparison;
calculating a similarity score between the work item vector and each of the prior work item vectors;
selecting one or more prior work items, from the plurality of prior work items, that each has a similarity score exceeding a defined threshold;
accessing a pre-planning correlation database to identify one or more pre-planning activities related to each selected prior work item of the one or more selected prior work items, wherein each selected prior work item has a respective first recorded historical time, and each respective pre-planning activity has a respective second recorded historical time
and estimating a time to complete the work item based on the similarity scores by generating an estimated sizing for the work item using the similarity scores of the one or more selected prior work items, the first recorded historical times of the one or more selected prior work items, and the second recorded historical times of the one or more pre-planning activities identified using the pre-planning correlation database.
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; estimating a time to complete the work item based on the similarity score;
identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; and
generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the one or more defect-related keywords into the estimated time to complete the work item
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, one or more memories and databases is recited at a high level of generality, i.e., as a generic computing and processing system. This one or more computer processors, one or more memories and databases 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 one or more computer processors, one or more memories and databases. 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, one or more memories and databases 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, 11, 13 and 16-23 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 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 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). Claims 7and 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). Claim 21 further limit the abstract idea that generating the work item vector using the team-specific keywords comprises generating a contextual word embedding of the team-specific keywords (a more detailed abstract idea remains an abstract idea). Claim 22 further limit the abstract idea by accessing a pre-planning correlation database that includes a mapping strength for each mapping between a prior work item of the plurality of prior work items and a corresponding pre-planning activity; and generating the time to complete the work item by weighting a recorded historical time of the corresponding pre-planning activity using the mapping strength (a more detailed abstract idea remains an abstract idea). And claim 23 further limit the abstract idea by identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; generating a defect vector using the one or more defect-related keywords; comparing the defect vector with one or more vectors of previously addressed defects to identify a previously addressed defect related to the feature request; and generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the previously addressed defect into the time to complete the work item (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.
Response to Arguments
Applicant's arguments filed on 7/1/2026 have been fully considered but they are not persuasive.
With regards to the 35 U.S.C. 101 rejection, Applicant argues that with regard to claim 1 (1) “the claims do not merely recite a mental process” (2) “the claims integrate any alleged exception into a practical application.” (3) “Step 2B also does not support the rejection”; (4) “claim 8, as amended, is patent eligible under 35 U.S.C. § 101 “, (5) “claim 15, as amended, is patent eligible under 35 U.S.C. § 101” (6) Newly added claims 21-23 (Remarks, pages 11-17).
With regards to the 35 U.S.C. 103 rejection (Remarks pages 18-31):
Claim 1: Applicant argues that (7) “Kulkarni, on current review, does not teach or suggest this claimed relationship between feature-request terminology, team-specific work-item terminology, vector generation using team-specific keywords, and time estimation based on similarity to prior work-item vectors. The cited portions of Kulkarni generally describe processing project-management text descriptions and determining similarity between text chunks. That is not the same as "identifying a plurality of team-specific keywords for a work item associated with the feature request" and "generating a work item vector using the team-specific keywords," as required by claim 1.” (8) “The proposed combination therefore relies on hindsight reconstruction. The rejection identifies text-vector similarity in Kulkarni and task-time estimation in Ponnusamy, but does not provide an adequate reason why a person of ordinary skill would have modified Kulkarni to estimate time to complete a work item based on similarity scores generated from the particular team-specific work-item vector framework recited in claim 1.”)
Claim 5: Applicant argues that (9) “Vijayaraghavan, on current review, does not cure the deficiencies of Kulkarni and Ponnusamy.”
Claim 6: Applicant argues that (10) “The cited art does not teach or suggest that claimed process.”
Claim 7: Applicant argues that (11) “Accordingly, Kulkarni in view of Ponnusamy fails to teach or suggest the additional limitations of claim 7”
Claim 8: Applicant argues that (12) “Accordingly, the cited combination does not teach or suggest amended claim 8”
Claim 15: (13) Applicant argues in regards to the newly added limitations.
Claim 21-23 (14) Applicant argues in regards to the newly added claims 21-23.
In response to Applicant’s argument (1). Examiner respectfully disagrees. Claim 1 recites a method for estimating a time to complete the work item based on the similarity score, which is estimated by 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 and calculating a similarity score between the work item vector and each of the prior work item vectors as described in the Applicant's disclosure in paragraph 0014 " Accurate work item sizing is important for project management and coordination as it allows for more precise budget and deadline estimations and facilitates more efficient resource allocation and project progress tracking." Therefore, claim 1 recites an abstract idea falling within the Guidance's subject-matter grouping to the group of Mental Processes, concepts performed in the human mind including observations (feature request, plurality of feature keywords, team-specific keywords for the work item), evaluation (work item vector using team-specific keywords, plurality of prior work items related to the team-specific keywords, plurality of prior work item vectors, similarity score calculation), judgement and opinion (estimated time to complete the work item based on the similarity score).
In response to Applicant’s argument (2). Examiner respectfully disagrees. 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, one or more memories and databases is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting to receive inputs, analyze the inputs i.e., vector analysis and comparison in order to estimate time to complete the work item based on the similarity score. The recited one or more computer processors, one or more memories and databases merely links the abstract idea to a computer environment. In this way, the one or more computer processors, one or more memories and databases involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claim 1 uses the one or more computer processors, one or more memories and databases as a tool, in its ordinary capacity, to carry out the abstract idea. Claim 5 uses the database and the mapping function of a database, as a tool, in its ordinary capacity, to carry out the abstract idea. Claim 6 uses the database and the mapping function of a database as a tool, in its ordinary capacity, to carry out the abstract idea. Claim 7 uses the database, the accessing function i.e., search and storing function as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field. Further, a processor configured to cause receiving/determining/transmitting data to a device 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.
In response to Applicant’s argument (3). Examiner respectfully disagrees. The claims each at most comprise additional elements of one or more computer processors, one or more memories and databases. 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, one or more memories and databases 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). Applicant's claims do not apply the additional elements in a manner beyond what has been recognized by the courts as well-understood, routine, and conventional for computers and computing devices, the computer is merely used as a tool, there is no improvement to the field of computer technology. Employing well-known computer functions to execute an abstract idea, even when limiting the use of the idea to one particular environment, does not add significantly more, similar how limiting the abstract idea in Flook to petrochemical and oil-refining industries was insufficient. The rejection is maintained.
In response to Applicant’s argument (4). Examiner respectfully disagrees. Claim 8 recites a system for estimating a time to complete the work item based on the similarity score by generating an estimated sizing for the work item, which is estimated by 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 by searching a keyword correlation database that stores mappings between feature keywords and team-specific keywords extracted from one or more completed feature requests; generating, based on the plurality of team-specific keywords, one or more suggested team-specific work items for implementing 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 and wherein the plurality of prior work item vectors and the work item vector are generated in a same vector space for similarity comparison; selecting one or more prior work items, from the plurality of prior work items, that each has a similarity score exceeding a defined threshold; accessing a pre-planning correlation database to identify one or more pre-planning activities related to each selected prior work item of the one or more selected prior work items, wherein each selected prior work item has a respective first recorded historical time, and each respective pre-planning activity has a respective second recorded historical time; calculating a similarity scores by generating an estimated sizing for the work item using the similarity scores of the one or more selected prior work items, the first recorded historical times of the one or more selected prior work items, and the second recorded historical times of the one or more pre-planning activities identified using the pre-planning correlation database between the work item vector and each of the prior work item vectors as described in the Applicant's disclosure in paragraph 0014 " Accurate work item sizing is important for project management and coordination as it allows for more precise budget and deadline estimations and facilitates more efficient resource allocation and project progress tracking." Therefore, claim 1 recites an abstract idea falling within the Guidance's subject-matter grouping to the group of Mental Processes, concepts performed in the human mind including observations (feature request, plurality of feature keywords, team-specific keywords for the work item, pre-planning activities related to each selected prior work item with recorded historical time), evaluation (work item vector using team-specific keywords, suggested team-specific work items, plurality of prior work items related to the team-specific keywords, plurality of prior work item vectors, similarity score calculation), judgement (similarity score exceeding a defined threshold) and opinion (estimated time to complete the work item based on the similarity score by generating an estimated sizing for the work item using the similarity scores of the one or more selected prior work items, the first recorded historical times of the one or more selected prior work items, and the second recorded historical times of the one or more pre-planning activities identified using the pre-planning correlation database). 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, one or more memories and databases is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting to receive inputs, analyze the inputs i.e., vector analysis and comparison in order to estimate time to complete the work item based on the similarity score. The recited one or more computer processors, one or more memories and databases merely links the abstract idea to a computer environment. In this way, the one or more computer processors, one or more memories and databases involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claim 8 uses the one or more computer processors, one or more memories and databases as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field. Further, a processor configured to cause receiving/determining/transmitting data to a device 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. Please see also the Response to Applicant’s argument (3).
In response to Applicant’s argument (5). Examiner respectfully disagrees. Claim 15 recites one or more non-transitory computer-readable media containing, in any combination, computer program code for generating a total estimated for implementing a feature request by including estimated defect-resolution with the one or more defect-related keywords into the estimated time to complete the work item, which is estimated by 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 and calculating a similarity score between the work item vector and each of the prior work item vectors; estimating a time to complete the work item based on the similarity score; identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; and generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the one or more defect-related keywords into the estimated time to complete the work item as described in the Applicant's disclosure in paragraph 0014 " Accurate work item sizing is important for project management and coordination as it allows for more precise budget and deadline estimations and facilitates more efficient resource allocation and project progress tracking." Therefore, claim 1 recites an abstract idea falling within the Guidance's subject-matter grouping to the group of Mental Processes, concepts performed in the human mind including observations (feature request, plurality of feature keywords, team-specific keywords for the work item, defect-related keywords, estimated, estimated defect-resolution time), evaluation (work item vector using team-specific keywords, plurality of prior work items related to the team-specific keywords, plurality of prior work item vectors, similarity score calculation), judgement and opinion (estimated time to complete the work item based on generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the one or more defect-related keywords into the estimated time to complete the work item). 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, one or more memories and databases is recited at a high level of generality, i.e., as a generic processor performing a generic computer function of receiving/determining/transmitting data. This generic processor limitation is no more than mere instructions to apply the exception using a generic computer component. Considering the claims as a whole, these additional limitations merely add generic computer activities i.e., receiving/determining/transmitting to receive inputs, analyze the inputs i.e., vector analysis and comparison in order to estimate time to complete the work item based on the similarity score. The recited one or more computer processors, one or more memories and databases merely links the abstract idea to a computer environment. In this way, the one or more computer processors, one or more memories and databases involvement is merely a field of use which only contributes nominally and insignificantly to the recited method, which indicates absence of integration. Claim 15 uses the one or more computer processors, one or more memories and databases as a tool, in its ordinary capacity, to carry out the abstract idea. As to this level of computer involvement, mere automation of manual processes using generic computers does not necessarily indicate a patent-eligible improvement in computer technology. Considered as a whole, the claimed method does not improve the functioning of the computer itself or any other technology or technical field. Further, a processor configured to cause receiving/determining/transmitting data to a device 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. Please see also the Response to Applicant’s argument (3).
In response to Applicant’s argument (6). Examiner respectfully disagrees. The newly added claims 21-23 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. Claim 21 further limit the abstract idea that generating the work item vector using the team-specific keywords comprises generating a contextual word embedding of the team-specific keywords (a more detailed abstract idea remains an abstract idea). Claim 22 further limit the abstract idea by accessing a pre-planning correlation database that includes a mapping strength for each mapping between a prior work item of the plurality of prior work items and a corresponding pre-planning activity; and generating the time to complete the work item by weighting a recorded historical time of the corresponding pre-planning activity using the mapping strength (a more detailed abstract idea remains an abstract idea). And claim 23 further limit the abstract idea by identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; generating a defect vector using the one or more defect-related keywords; comparing the defect vector with one or more vectors of previously addressed defects to identify a previously addressed defect related to the feature request; and generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the previously addressed defect into the time to complete the work item (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. Claim 22 uses the database and the mapping function of a database, as a tool, in its ordinary capacity, to carry out the abstract idea. 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.
In response to Applicant’s argument (7). Examiner respectfully disagrees. Applicant’s disclosure describes in ¶ 0013: “In software development, a work item is a primary unit of work completion within a project. […] Each work item represents an individual task that can be assigned to different teams or engineers, and these work items collectively contribute to the completion of the requested feature. Work item sizing is a process of determining the amount of time and effort required to complete each work item. Various units may be used in sizing, such as hours, days, or person months (which represent the amount of work one person can complete in one month).” Kulkarni teaches in ¶ 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. For example, a given project will typically involve various work items that need to be started, completed, or otherwise performed in a particular order (possibly including some work items that can be performed in parallel),” Therefore Kulkarni does teach team-specific key words for a work item associated with a feature and generate work item vector using the team-specific keywords as explained below in the rejection of claim 1. In addition, in response to applicant's arguments against the references individually, one cannot show nonobviousness by attacking references individually where the rejections are based on combinations of references. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981); In re Merck & Co., 800 F.2d 1091, 231 USPQ 375 (Fed. Cir. 1986).
In response to applicant's argument (8) that the examiner's conclusion of obviousness is based upon improper hindsight reasoning, it must be recognized that any judgment on obviousness is in a sense necessarily a reconstruction based upon hindsight reasoning. But so long as it takes into account only knowledge which was within the level of ordinary skill at the time the claimed invention was made, and does not include knowledge gleaned only from the applicant's disclosure, such a reconstruction is proper. See In re McLaughlin, 443 F.2d 1392, 170 USPQ 209 (CCPA 1971). In addition, 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.
In response to Applicant’s argument (9). Examiner respectfully disagrees. Please see the response to Applicant’s argument (7). 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.” Vijayaraghavan in an analogous art of work items management teaches the database with the mapping function between data/keywords in ¶ 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.”
In response to Applicant’s argument (10). Examiner respectfully disagrees. As explained above in the response to Applicant’s argument (7) and (9). Kulkarni in view of Ponnusamy and Vijayaraghavan teaches the limitations of claim 6, please see the rejection below.
In response to Applicant’s argument (11). Examiner respectfully disagrees. Applicant’s disclosure describes pre-planning activities ¶0060: “ Pre-planning activities may refer to work that needed to be performed in an earlier release in preparation for the main work item. Without performing these activities, the introduction or implementation of the main work item may disrupt the normal operations of a computing system. Examples of pre-planning activities may include, but are not limited to, evaluating the risk of updating, performing architectural review and changes to prevent disruptions, mitigating data to prevent data loss and minimize downtime, or developing experimental prototypes to test new concepts before full-scale implementation.” Kulkarni teaches in ¶ 0016: “the project management data obtained from the project management system(s) may include text descriptions that relate to one or more requirements, user stories, epics, features, tasks, risks, issues, actions, decisions, change requests, feedback, milestones, deliverables, and/or any other functional entities associated with a project.” Ponnusamy teaches in ¶ 0029: “ the cloud server 111 can provide users with insights into which tasks are most likely to complete and which tasks are at the highest risk of slipping, to enable the users to focus their resources on the right tasks.” Ponnusamy teaches 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 in ¶ 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.” And calculating the time to complete the work item using the similarity scores, the first recorded historical times, and the second recorded historical times in ¶ 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” In a broadest reasonable interpretation and to one of ordinary skill in the art at the time the invention was filed, the historical task completion data of Ponnusamy includes pre-planning activities in order to complete the task as shown above.
In response to Applicant’s argument (12). Please see the response to Applicant’s arguments (9) and (11) and the updated rejection below as necessitated by amendments.
In response to Applicant’s argument (13). Please see the updated rejection below as necessitated by amendments.
In response to Applicant’s argument (14). Please see the updated rejection below as necessitated by amendments.
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 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).
Claim 2:
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).
Claim 3:
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).
Claim 4:
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”);
Claim 7:
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 and 21 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 claim 1 above, further in view of Vijayaraghavan et al., (US 2022/0350967 A1) hereinafter “Vijayaraghavan”.
Claim 5:
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.” (Vijayaraghavan, ¶ 0096).
Claim 6:
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.” (Vijayaraghavan, ¶ 0096).
Claim 21:
Kulkarni teaches in ¶ 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.” 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 generating the work item vector using the team-specific keywords comprises generating a contextual word embedding of the team-specific keywords (¶ 0088: “ the device may generate word embeddings based on the final work item data before processing the final work item data, the synonyms, and the keywords with the machine learning model.”);
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 generating the work item vector using the team-specific keywords comprises generating a contextual word embedding of the team-specific 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.” (Vijayaraghavan, ¶ 0096).
Claims 8-9, 11 and 13 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al., (US 2023/0186203 A1) hereinafter “Kulkarni” in view of Vijayaraghavan et al., (US 2022/0350967 A1) hereinafter “Vijayaraghavan” and Rajkumar Ponnusamy (US 2021/0224722 A1) hereinafter “Ponnusamy”.
Claim 8:
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);
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”);
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 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:
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.”);
generating, based on the plurality of team-specific keywords, one or more suggested team-specific work items for implementing the feature request (¶ 0021: “the traceability system performs natural language processing (NLP) on the cleansed work item data to identify synonyms for words in the cleansed work item data. In some implementations, the traceability system determines vectors for words in the cleansed work item data. The traceability system may determine a similarity (e.g., a cosine similarity) between each vector and all of the other vectors. The traceability system may identify a set of words as synonyms when the similarity between the vectors determined for the set of words satisfies a similarity threshold.”);
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 searching a keyword correlation database that stores mappings between feature keywords and team-specific keywords extracted from one or more completed feature requests and generating, based on the plurality of team-specific keywords, one or more suggested team-specific work items for implementing the feature request 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.” (Vijayaraghavan, ¶ 0096).
In addition, Kulkarni teaches:
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.”);
and wherein the plurality of prior work item vectors and the work item vector are generated in a same vector space for similarity comparison (¶ 0024: “from a mathematical perspective, the cosine similarity metric may measure the cosine of the angle between two vectors projected in a multi-dimensional space, which may be advantageous because two similar text chunks that are far apart in Euclidean distance (e.g., due to the size of the text chunk(s)), the text chunks may still be oriented closer together by the cosine similarity metric (e.g., where a smaller angle generally corresponds to a higher cosine similarity).”);
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,”);
selecting one or more prior work items, from the plurality of prior work items, that each has a similarity score exceeding a defined threshold (¶ 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 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”. Vijayaraghavan teaches in ¶ 0016: “an enterprise resource planning software,” Kulkarni in view of Vijayaraghavan 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:
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.”);
estimating a time to complete the work item based on the similarity scores by generating an estimated sizing for the work item using the similarity scores of the one or more selected prior work items, the first recorded historical times of the one or more selected prior work items, and the second recorded historical times of the one or more pre-planning activities identified using the pre-planning correlation database. (¶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” see also ¶ 0002: “estimating time required to complete a task based on historical data and time already spent on the task”);
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 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 • estimating a time to complete the work item based on the similarity scores by generating an estimated sizing for the work item using the similarity scores of the one or more selected prior work items, the first recorded historical times of the one or more selected prior work items, and the second recorded historical times of the one or more pre-planning activities identified using the pre-planning correlation database. 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).
Claim 9:
The limitations of claim 9 encompass substantially the same scope as claim 2. Accordingly, those similar limitations are rejected in substantially the same manner as claim 2, as described above.
Claim 11:
The limitations of claim 11 encompass substantially the same scope as claim 4. Accordingly, those similar limitations are rejected in substantially the same manner as claim 4, as described above.
Claim 13:
The limitations of claim 13 encompass substantially the same scope as claim 6. Accordingly, those similar limitations are rejected in substantially the same manner as claim 6, as described above.
Claims 15-17 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” and Leszak et al., Classification and evaluation of defects in a project retrospective, Journal of Systems and Software, Volume 61, Issue 3, 2002, Pages 173-187, hereinafter “Leszak”.
Claim 15:
Kulkarni as shown discloses one or more non-transitory computer-readable media, in any combination, computer program code, which, when executed by a computer system, performs operations 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);
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 scores (¶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).
Kulkarni as explained above identify a plurality of team-specific keywords for a work item associated with the feature request. Kulkarni in view of Ponnusamy is silent with regard to the following limitations. However, Leszak in an analogous art of software project management for the purpose of providing the following limitations as shown does:
identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; and (page 174: “The input data we used were defect numbers, static code analysis data, and defect classification data from a sample from all defects. […] The other two data sets were input to smaller investigations, one on process compliance, the other on code complexity and its relation to defect insertion.“ Abstract: “We report a retrospective analysis of the defect modification requests (MRs) discovered while building, testing, and deploying a release of a network element as part of an optical transmission network.” Figure 1 illustrates a MR database, page 175-176: “Fig. 1 depicts the process workflow in which RCA plays a key role for analyzing defects and learning from systematic root causes: from the project's MR databases, samples of both internally detected and customer delivered defects” );
generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the one or more defect-related keywords into the estimated time to complete the work item (page 179: “Interesting is the mismatch between number and effort which is most significant for defects of type previous, unexpected interaction, performance, and data design. This is reflected in the estimated effort (in person days) per MR of those defects. On the average, we find 4.6 days for external, 6.2 for interface, 4.7 for implementation defects. […] As depicted in Fig. 3, system integration represents 50% of the distribution for defect detection, while the elimination of those defects taking up almost 60% of effort. As expected, the data show a significant variation in effort per MR. Defects found in SW design and SW integration require less effort than those found in system integration and test. The estimated effort per MR (in person-days) as extracted from those figures are 3 days during SW design and integration, 6 days during system integration and system test and 9 days after delivery.” See also Figure 4.);
Both Kulkarni and Leszak teach software development management. Kulkarni teaches in the ¶ 0013: “the intelligent dependency management system described herein may be used to support any suitable project management scenario, such as managing a software development lifecycle” Leszak teaches in page 186: “Measurement and evaluation is a critical activity throughout the entire software development and evolution lifecycle.” 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 Leszak would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Leszak 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 identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; and generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the one or more defect-related keywords into the estimated time to complete the work item into similar systems. Further, as noted by Leszak “Typically for an enhancement release, more than 80% of all software defects are introduced during component development. Therefore, analyzing product quality and steps towards preventing defects should focus on the software component development process.” (Leszak, page 184).
Claim 16:
The limitations of claim 16 encompass substantially the same scope as claim 2. Accordingly, those similar limitations are rejected in substantially the same manner as claim 2, as described above.
Claim 17:
The limitations of claim 17 encompass substantially the same scope as claim 3. Accordingly, those similar limitations are rejected in substantially the same manner as claim 3, as described above.
Claim 20:
The limitations of claim 20 encompass substantially the same scope as claim 7. Accordingly, those similar limitations are rejected in substantially the same manner as claim 7, as described above.
Claims 18 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Kulkarni et al., (US 2023/0186203 A1) hereinafter “Kulkarni”, Rajkumar Ponnusamy (US 2021/0224722 A1) hereinafter “Ponnusamy” and Leszak et al., Classification and evaluation of defects in a project retrospective, Journal of Systems and Software, Volume 61, Issue 3, 2002, Pages 173-187, hereinafter “Leszak” as applied to claim 15 above, further in view of Vijayaraghavan et al., (US 2022/0350967 A1) hereinafter “Vijayaraghavan”.
Claim 18:
The limitations of claim 18 encompass substantially the same scope as claim 5. Accordingly, those similar limitations are rejected in substantially the same manner as claim 5, as described above.
Claim 19:
The limitations of claim 19 encompass substantially the same scope as claim 6. Accordingly, those similar limitations are rejected in substantially the same manner as claim 6, as described above.
Claim 22 is 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 claim 1 in view of Sharma et al., (US 2021/0240928 B2) hereinafter “Sharma”
Claim 22:
Kulkarni teaches in ¶ 0024: “ a cosine similarity metric may measure how similar two text chunks are regardless of their size. In particular, from a mathematical perspective, the cosine similarity metric may measure the cosine of the angle between two vectors projected in a multi-dimensional space, which may be advantageous because two similar text chunks that are far apart in Euclidean distance (e.g., due to the size of the text chunk(s)), the text chunks may still be oriented closer together by the cosine similarity metric (e.g., where a smaller angle generally corresponds to a higher cosine similarity). Accordingly, based on the cosine similarity between two text chunks, the artificial intelligence model may generate a confidence score that indicates a likelihood that the two text chunks are similar and therefore relevant to a dependency analysis.” Kulkarni as explained above teaches a similarity metric and the prior work items. Kulkarni is silent with regard to the following limitations. However, Sharma in an analogous art of similarity mapping management for the purpose of providing the following limitations as shown does:
that includes a mapping strength for each mapping between a prior work item of the plurality of prior work items and [corresponding pre-planning activity] and (¶ 0020: “The result is a score being generated between the feedback 122 and each of the respective processes 151-155. The scores represent a strength of correlation between the feedback 122 and the respective processes 151-155.” See also ¶ 0037: “ the mapping algorithm may identify keywords within the customer feedback and map the keywords to the different processes using the synonym graph. For example, the synonym graph may include a plurality of nodes representing a plurality of words, and links between the plurality of nodes identify synonym relationships between the corresponding plurality of words. When two words are connected directly to each other in the synonym graph, it indicates that the two words are very close in meaning (strong synonym relationship). When two words have one or more intervening words in between them in the synonym graph, it indicates a less strong relationship. Therefore, the similarity strength may be based on the number of links between two words in the synonym graph.”, ¶ 0025: “ Each activity record 231 may further include a significance weight which represents a weight of correlation between the word/activity and the corresponding process,” and ¶ 0033: “each significance weight is based on a weight assigned to the respective activity that the feedback keyword is mapped to using the business process structure 230 in FIG. 2B.”);
Both Kulkarni and Sharma teach similarity mapping management. Kulkarni teaches in ¶ 0024: “ 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.” Sharma teaches in ¶ 0038: “the mapping value for the process based on a number of nodes on the synonym graph between a node representing the feedback keyword and a node representing the activity keyword. Here, the less links between two mapped words in the synonym graph means a greater score/similarity therebetween.” 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 Sharma would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Sharma 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 a mapping strength into similar systems. Further, as noted by Sharma “the mapping may include respectively mapping a plurality keywords from the text content to the word identifying the process, within the synonym graph.” (Sharma, ¶ 0037).
Kulkarni in view of Sharma as explained above teaches the mapping strength and weight values. 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:
accessing a pre-planning correlation database [that includes a mapping strength for each mapping] between a prior work item of the plurality of prior work items and corresponding pre-planning activity and (¶ 0034: “The database management module 112 can extract a domain ID from each participant ID. Alternatively, the database management module 112 can obtain the domain ID for each participant by searching a data structure or database, or through a third party such as domain registry server 107. The database management module 112 can identify one or more entity objects based on the domain ID, where the entity objects are stored in the task database server 106.” See also ¶ 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.”);
generating the time to complete the work item by weighting a recorded historical time of the corresponding pre-planning activity using the mapping strength (¶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” see also ¶ 0002: “estimating time required to complete a task based on historical data and time already spent on the task” and ¶ 0077: “The predetermined algorithm can use a different weight for each characteristic when calculating the similarity score.”);
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 accessing a pre-planning correlation database [that includes a mapping strength for each mapping] between a prior work item of the plurality of prior work items and corresponding pre-planning activity and estimating a time to complete the work item based and generating the time to complete the work item by weighting a recorded historical time of the corresponding pre-planning activity using the mapping strength 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).
Claim 23 is 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 claim 1, further in view of Leszak et al., Classification and evaluation of defects in a project retrospective, Journal of Systems and Software, Volume 61, Issue 3, 2002, Pages 173-187, hereinafter “Leszak”.
Claim 23:
Kulkarni as explained above identify a plurality of team-specific keywords for a work item associated with the feature request. Kulkarni in view of Ponnusamy is silent with regard to the following limitations. However, Leszak in an analogous art of software project management for the purpose of providing the following limitations as shown does:
identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; the one or more defected-related keywords; defect (page 174: “The input data we used were defect numbers, static code analysis data, and defect classification data from a sample from all defects. […] The other two data sets were input to smaller investigations, one on process compliance, the other on code complexity and its relation to defect insertion.“ Abstract: “We report a retrospective analysis of the defect modification requests (MRs) discovered while building, testing, and deploying a release of a network element as part of an optical transmission network.” Figure 1 illustrates a MR database, page 175-176: “Fig. 1 depicts the process workflow in which RCA plays a key role for analyzing defects and learning from systematic root causes: from the project's MR databases, samples of both internally detected and customer delivered defects” );
generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the previously addressed defect into the estimated time to complete the work item (page 179: “Interesting is the mismatch between number and effort which is most significant for defects of type previous, unexpected interaction, performance, and data design. This is reflected in the estimated effort (in person days) per MR of those defects. On the average, we find 4.6 days for external, 6.2 for interface, 4.7 for implementation defects. […] As depicted in Fig. 3, system integration represents 50% of the distribution for defect detection, while the elimination of those defects taking up almost 60% of effort. As expected, the data show a significant variation in effort per MR. Defects found in SW design and SW integration require less effort than those found in system integration and test. The estimated effort per MR (in person-days) as extracted from those figures are 3 days during SW design and integration, 6 days during system integration and system test and 9 days after delivery.” See also Figure 4.);
Both Kulkarni and Leszak teach software development management. Kulkarni teaches in the ¶ 0013: “the intelligent dependency management system described herein may be used to support any suitable project management scenario, such as managing a software development lifecycle” Leszak teaches in page 186: “Measurement and evaluation is a critical activity throughout the entire software development and evolution lifecycle.” 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 Leszak would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Leszak 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 identifying one or more defect-related keywords corresponding to the feature request by searching a keyword correlation database that includes defect-related keywords mapped to completed features; defect, defect-related keywords; and generating a total estimated time for implementing the feature request by incorporating an estimated defect-resolution time associated with the previously addressed defect into the time to complete the work item into similar systems. Further, as noted by Leszak “Typically for an enhancement release, more than 80% of all software defects are introduced during component development. Therefore, analyzing product quality and steps towards preventing defects should focus on the software component development process.” (Leszak, page 184).
In addition, Kulkarni teaches:
generating a defect vector using [the one or more defected-related 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.”);
comparing the [defect] vector with one or more vectors of previously addressed [defects] to identify a previously addressed [defect] related to the feature request; and (¶ 0025: “from a mathematical perspective, the cosine similarity metric may measure the cosine of the angle between two vectors projected in a multi-dimensional space, which may be advantageous because two similar text chunks that are far apart in Euclidean distance (e.g., due to the size of the text chunk(s)), the text chunks may still be oriented closer together by the cosine similarity metric (e.g., where a smaller angle generally corresponds to a higher cosine similarity). Accordingly, based on the cosine similarity between two text chunks, the artificial intelligence model may generate a confidence score that indicates a likelihood that the two text chunks are similar”);
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Jørgensen et al.,, Impact of effort estimates on software project work, Information and Software Technology, Volume 43, Issue 15, 2001, Pages 939-948, discloses two case studies and two experiments on how effort estimates impact software project work.
Lämmel et al., 2020. Understanding What Software Engineers Are Working on: The Work-Item Prediction Challenge. In Proceedings of the 28th International Conference on Program Comprehension (ICPC '20). Association for Computing Machinery, New York, NY, USA, 416–424 disclose the corresponding 'work-item prediction challenge' on the grounds of representative scenarios, report on related efforts at Facebook, discuss some lessons learned, and review related work to call to arms to leverage, advance, and combine techniques from program comprehension, mining software repositories, process mining, and machine learning.
Frederik vom Lehn Understanding Vector Similarity for Machine Learning Cosine Similarity, Dot Product, Manhattan Distance L1, Euclidian Distance L2. Oct 7, 2023
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/NADJA N CHONG CRUZ/
Primary Examiner, Art Unit 3623
