DETAILED ACTION
Status of the Claims
The following is a Final Office Action in response to amendments and remarks filed 08 January 2026.
Claims 1-2 and 4-14 have been amended.
Claim 15 has been added.
Claims 1-15 are pending and have been examined.
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 Arguments
Applicants argue that the 35 U.S.C. 101 rejection under the Alice Corp. vs. CLS Bank Int’l be withdrawn; however the Examiner respectfully disagrees. The Examiner again notes that method claim 14 is devoid of structure whatsoever and thus can only amount to an abstract idea. Applicant next argues that the claims are integrated into a practical application (citing Examples 23 and 42); however the Examiner respectfully disagrees for a plurality of reasons. Firstly, the instant claims recite no such GUI (graphical user interface), let alone for the same purposes thereof in Examples 23 and 42 which resulted in improvements. With regard to Example 23, the inventor has improved upon previous GUIs by dynamically relocating obscured textual information of an underlying window to become automatically viewable to the user. In particular, in a graphical user interface that comprises multiple windows, the invention continuously monitors the boundaries of the windows to ascertain an overlap condition indicating that the windows overlap such that the textual information of an underlying window is obscured from a user’s view by the overlapping window. Only when the textual information of the underlying window is detected to be obscured, the invention re‐formats and moves the textual information in the underlying window to an unobscured portion of the underlying window so that the textual information is viewable by the user. When the overlap condition no longer exists, the textual information is returned to its original format and location. Claim 1 of Example 42 recites a combination of additional elements including storing information, providing remote access over a network, converting updated information that was input by a user in a non-standardized form to a standardized format, automatically generating a message whenever updated information is stored, and transmitting the message to all of the users. The claim as a whole integrates the method of organizing human activity into a practical application. Specifically, the additional elements recite a specific improvement over prior art systems by allowing remote users to share information in real time in a standardized format regardless of the format in which the information was input by the user. Thus, the claim is eligible because it is not directed to the recited judicial exception (abstract idea). The only correlations between the instant claims and Example 23 and the Claim 1 of Example 42 is the ability to collect information into a format. However the instant claims are more akin to Claim 2 of Example 42 as they, as a whole, merely describe how to generally “apply” the concept of electronic data, storage, query, and retrieval of historic communications in a computer environment. The claimed computer components are recited at a high level of generality and are merely invoked as tools to perform an existing social network interest group process. Simply implementing the abstract idea on a generic computer is not a practical application of the abstract idea. Hence, the claims are not similar to Example 23 or Claim 1 of Example 42, and thus the rejection was not withdrawn. Lastly, the Examiner also notes that the Examples provided on the USPTO website are purely hypothetical for demonstration purposes only and do not serve as a benchmark for patent eligibility. As such, the arguments are not persuasive, and the rejection not overcome.
This argument also appears to be whether or not the use of computer or computing components for increased speed and efficiency integrates the claims into a practical application; however the Examiner respectfully disagrees. Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted)).
Applicant’s remarks with respect to the §112 and prior art rejections have been fully considered but are moot on grounds of new rejection, as necessitated by amendments.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the Applicants in regards to distinctly and specifically pointing out the supposed errors in the Examiner's prior office action (37 CFR 1.111). The Examiner asserts that the Applicants only argue that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Claim Objections
Claim 12 is objected to because of the following informalities: The claims have the acronym “wherein the processor is configured to determine whether a project completed in the past has an final out of failure or success, based on a preset condition” which appears to be the typographical error of “a final outcome.” Appropriate correction is required.
Claim Rejections - 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1, 13, and 14 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for pre-AIA the inventor(s), at the time the application was filed, had possession of the claimed invention. The claims recite the newly amended limitation “upon selection of the displayed feature quantity, replace the display of the feature quantity with display of a list of communications exchanged between users regarding the feature quantity during implementation of the past project” however there is no discussion, throughout the entirety of the specification and drawings, that the feature quantity is replaced with a display of a list of communications between users regarding the feature quantity upon any selection. Paragraph [0086] states “For example, when the use of a certain guideline, material, or the like is identified to be a success factor, deliverables and a list of communication histories based on the certain guideline, material, or the like are presented to the user together with the success factor." While a communication history based upon a certain guideline can be presented together with the success factor, there is no mention or discussion as to how the feature quantity is replaced with a display of a list of communications exchanged between users regarding the feature quantity when any selection of a feature quantity occurs. As such, the Examiner asserts this as evidence that the newly amended claims are new matter.
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 1-15 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). However, the claim(s) recite(s) presenting features that have contributed to the success of previous projects which is an abstract idea of organizing human activities as well as a mental process.
The limitations of “extract a feature quantity regarding a past project having a final outcome of success, wherein the feature quantity is identified as having contributed to the success of the past project using a predetermined method; display the feature quantity of the past project as a factor to increase a chance of success of a target project; upon selection of the displayed feature quantity, replace the display of the feature quantity with display of a list of communications exchanged between users regarding the feature quantity during implementation of the past project” as drafted, is a process that, under its broadest reasonable interpretation, covers organizing human activities--fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) or a mental process—concepts performed in the human mind (including an observation, evaluation, judgment, opinion) but for the recitation of generic computer components (Step 2A Prong 1). That is, other than reciting “An information processing apparatus comprising: a processor configured to:,” (or “A non-transitory computer readable medium storing a program causing a computer to execute a process, the process comprising:” in claim 13 or “An information processing method comprising:” in claim 14) nothing in the claim element precludes the step from the methods of organizing human interactions grouping or from practically being performed in the mind. For example, but for the “by a computer system” language, “extract...” “display...,” and “replace the display...” in the context of this claim encompasses the user manually organizing and comparing historical data from successful projects, which is a business relation/fundamental economic practice/commercial or legal interaction/managing personal behavior or mental process/judgement on the organized or collected information. However, if possible, the Examiner should consider the limitations together as a single abstract idea rather than as a plurality of separate abstract ideas to be analyzed individually. “For example, in a claim that includes a series of steps that recite mental steps as well as a mathematical calculation, an examiner should identify the claim as reciting both a mental process and a mathematical concept for Step 2A, Prong One to make the analysis clear on the record.” MPEP 2106.04, subsection II.B. Under such circumstances, however, the Supreme Court has treated such claims in the same manner as claims reciting a single judicial exception. Id. (discussing Bilski v. Kappos, 561 U.S. 593 (2010)). Here, the limitations are considered together as a single abstract idea for further analysis. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitations as a method of organizing human activities, while some of the limitations may be performed in the mind after certain limitations are performed, but for the recitation of generic computer components, then it falls within the grouping of abstract ideas. (Step 2A, Prong One: YES). Accordingly, the claim(s) recite(s) an abstract idea.
This judicial exception is not integrated into a practical application (Step 2A Prong Two). Method claim 14 is devoid of structure whatsoever and thus can only be directed towards an abstract idea. In particular, the claims only recite one additional element – using a processor or a computer to perform the steps. The processor or computer in the steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). Method claim 14 is devoid of structure whatsoever and thus can only be directed towards an abstract idea. As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a processor or computer to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component. Mere instructions to apply an exception using a generic computer component cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claims 2-7 recite the additional limitations that include mathematical concepts which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 13, and 14, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 8-12 and 15 recite the additional limitations further limiting how the factors contribute and alternatives thereof, which is still directed towards the abstract idea previously identified and is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 13, and 14, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, this additional element does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-15 are therefore not eligible subject matter, even when considered as a whole.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-5 and 8-15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hickey et al. (US PG Pub. 2013/0073533) and further in view of Marinovic (US PG Pub. 2022/0067662).
As per claims 1, 13, and 14, HIckey discloses an information processing apparatus comprising: a processor configured to; a non-transitory computer readable medium storing a program causing a computer to execute a process, the process comprising:; and an information processing method comprising: (computer, computer system, processor, Hickey ¶33):
extract a feature quantity regarding a past project having a final outcome of success, wherein the feature quantity is identified as having contributed to the success of the past project using a predetermined method (extraction of features from a project, Hickey ¶13 and ¶17; FIG. 8D provides a control-flow diagram for the learning module, which represents one embodiment of the present invention discussed above with reference to FIG. 7. The learning module is invoked, at intervals, to analyze stored learning-module data, provided to the learning module by the project-search engine, in order to provide feedback to the project comparator. In step 850, the learning module computes correlation coefficients for each feature used in project comparison with a list of projects associated with positive feedback. In step 852, the learning module computes correlation coefficients of each feature with a list of projects associated with negative feedback. In step 854, features with correlation coefficients with respect to project lists associated with negative feedback greater than some threshold correlation-coefficient value are added to a first list. In step 856, the remaining features with correlation coefficients with respect to project lists associated with negative feedback less than or equal to the threshold value are added to a second list. In step 858, features with computed correlation coefficients with respect to project lists associated with positive result greater than a threshold value are removed from the second list. Thus, the first list includes features that appear to be strongly correlated with negative results and the second list includes features that are not strongly associated with negative results but also not strongly correlated to positive results. In step 860, the second portion of the learning module functionality is invoked, ¶30; in order to identify the positive and negative characteristics, ¶32) (Examiner notes the positive characteristics as the equivalent to the features with a successful contribution, based upon feedback i.e. historical outcomes);
display the feature quantity of the past project as a factor to increase a chance of success of a target project, (particular features, Hickey ¶18; in order to identify the positive and negative characteristics, ¶32) (Examiner interprets identifying positive characteristics as the ability to display a feature quantity of past projects as a factor which would increase a chance of success of a future or target project).
Hickey does not expressly disclose upon selection of the displayed feature quantity, replace the display of the feature quantity with display of a list of communications exchanged between users regarding the feature quantity during implementation of the past project.
However, Marinovic teaches upon selection of the displayed feature quantity, replace the display of the feature quantity with display of a list of communications exchanged between users regarding the feature quantity during implementation of the past project (In some embodiments, the server can generate a notification based on the feature vector, a comparison of two or more feature vectors, and/or one or more characteristics associated with a user. Accordingly, the server can generate and send a notification to a user, e.g., an administrator user. The notification can be presented to the user via a display. In some embodiments, the notification can be associated with suggested team members, automatically generated teams, final scores, successful teams and their communication patterns, and/or less successful teams (e.g., low performing teams) and their communication patterns, Marinovic ¶98; The analytics server 602 can analyze the graph 609 to determine, for example, which users are frequently in communication with each other and the projects that involved the communications data for those user, and determine an impact, character, or role for each particular user across the communication data for one or more prior projects. For example, the analytics server 602 may determine whether users tend to work in teams where there is a lot of communication or the opposite. As another example, the analytics server 602 can determine which team members are the “glue” socially, who keep the projects, teams, or other aspects of the organization socially interconnected. In this example, the graph indicates to the analytics server 602 users who connect users, are very engaged on projects, and contributes to teams assembled for prior or current projects, ¶81 and Fig. 6) (Examiner interprets the selection of the notification as the selection of the feature quantity which is then displaying the suggested team members, automatically generated teams, final scores, successful teams and their communication patterns, and/or less successful teams (e.g., low performing teams) and their communication patterns).
Both the Hickey and Marinovic references are analogous in that both are directed towards/concerned with project management. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Marinovic’s ability to identify success metrics and connect users for teams and projects in Hickey’s system to improve the system and method with reasonable expectation that this would result in a project management system that is able to generate successful projects.
The motivation being that there are, however, no practical means for efficiently reviewing interactions and communications between enterprise users, across all enterprise communications tools (Marinovic).
As per claim 2, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey further discloses wherein the predetermined method comprises a method of identifying the feature quantity for display as the factor to increase the chance of success of the target project through predicting an outcome of the target project based on a prediction model that is generated by machine learning using, as training data, the feature quantity of the past project having the final outcome of success and the final outcome of success (FIG. 8D provides a control-flow diagram for the learning module, which represents one embodiment of the present invention discussed above with reference to FIG. 7. The learning module is invoked, at intervals, to analyze stored learning-module data, provided to the learning module by the project-search engine, in order to provide feedback to the project comparator. In step 850, the learning module computes correlation coefficients for each feature used in project comparison with a list of projects associated with positive feedback. In step 852, the learning module computes correlation coefficients of each feature with a list of projects associated with negative feedback. In step 854, features with correlation coefficients with respect to project lists associated with negative feedback greater than some threshold correlation-coefficient value are added to a first list. In step 856, the remaining features with correlation coefficients with respect to project lists associated with negative feedback less than or equal to the threshold value are added to a second list. In step 858, features with computed correlation coefficients with respect to project lists associated with positive result greater than a threshold value are removed from the second list. Thus, the first list includes features that appear to be strongly correlated with negative results and the second list includes features that are not strongly associated with negative results but also not strongly correlated to positive results. In step 860, the second portion of the learning module functionality is invoked, Hickey ¶30).
As per claim 3, Hickey and Marinovic disclose as shown above with respect to claim 2. Hickey further discloses wherein the prediction model is generated by the machine learning performed using, as the training data, a feature quantity of a past project having a final outcome of failure in addition to the feature quantity of the past project having the final outcome of success (ascertain the success rate, Hickey ¶32; from feedback into a learning component, ¶26; tuning modifications. ¶27) (Examiner notes the learning module that is able to be trained with feedback for fine tuning as the equivalent as the machine learning using training data).
As per claim 4, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey further discloses wherein: the past project is one of a plurality of past projects having a final outcome of success; and the predetermined method comprises a method of identifying the feature quantity for display as the factor to increase the chance of success of the target project through analysis of all feature quantities of the plurality of past projects (The learning module is invoked, at intervals, to analyze stored learning-module data, provided to the learning module by the project-search engine, in order to provide feedback to the project comparator. In step 850, the learning module computes correlation coefficients for each feature used in project comparison with a list of projects associated with positive feedback. In step 852, the learning module computes correlation coefficients of each feature with a list of projects associated with negative feedback. In step 854, features with correlation coefficients with respect to project lists associated with negative feedback greater than some threshold correlation-coefficient value are added to a first list. In step 856, the remaining features with correlation coefficients with respect to project lists associated with negative feedback less than or equal to the threshold value are added to a second list. In step 858, features with computed correlation coefficients with respect to project lists associated with positive result greater than a threshold value are removed from the second list. Thus, the first list includes features that appear to be strongly correlated with negative results and the second list includes features that are not strongly associated with negative results but also not strongly correlated to positive results. In step 860, the second portion of the learning module functionality is invoked, Hickey ¶30).
As per claim 5, Hickey and Marinovic disclose as shown above with respect to claim 4. Hickey further discloses wherein the processor is configured to identify the feature quantity for display as a factor to increase the chance of success of the target project, based on frequencies of the respective feature quantities in the plurality of past projects having the final outcome of success or magnitudes of values of a correlation coefficient between the final outcome of each of the plurality of past projects and presence of each of the feature quantities (The learning module is invoked, at intervals, to analyze stored learning-module data, provided to the learning module by the project-search engine, in order to provide feedback to the project comparator. In step 850, the learning module computes correlation coefficients for each feature used in project comparison with a list of projects associated with positive feedback. In step 852, the learning module computes correlation coefficients of each feature with a list of projects associated with negative feedback. In step 854, features with correlation coefficients with respect to project lists associated with negative feedback greater than some threshold correlation-coefficient value are added to a first list. In step 856, the remaining features with correlation coefficients with respect to project lists associated with negative feedback less than or equal to the threshold value are added to a second list. In step 858, features with computed correlation coefficients with respect to project lists associated with positive result greater than a threshold value are removed from the second list. Thus, the first list includes features that appear to be strongly correlated with negative results and the second list includes features that are not strongly associated with negative results but also not strongly correlated to positive results. In step 860, the second portion of the learning module functionality is invoked, Hickey ¶30).
As per claim 8, Hickey and Marinovic disclose as shown above with respect to claim 2. Hickey further discloses wherein the processor is configured to display the factor to increase the chance of success of the target project, only when the target project has a prediction result indicating a failure from the prediction model (FIG. 8D provides a control-flow diagram for the learning module, which represents one embodiment of the present invention discussed above with reference to FIG. 7. The learning module is invoked, at intervals, to analyze stored learning-module data, provided to the learning module by the project-search engine, in order to provide feedback to the project comparator. In step 850, the learning module computes correlation coefficients for each feature used in project comparison with a list of projects associated with positive feedback. In step 852, the learning module computes correlation coefficients of each feature with a list of projects associated with negative feedback. In step 854, features with correlation coefficients with respect to project lists associated with negative feedback greater than some threshold correlation-coefficient value are added to a first list. In step 856, the remaining features with correlation coefficients with respect to project lists associated with negative feedback less than or equal to the threshold value are added to a second list. In step 858, features with computed correlation coefficients with respect to project lists associated with positive result greater than a threshold value are removed from the second list. Thus, the first list includes features that appear to be strongly correlated with negative results and the second list includes features that are not strongly associated with negative results but also not strongly correlated to positive results. In step 860, the second portion of the learning module functionality is invoked, Hickey ¶30).
As per claim 9, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey further discloses wherein the processor is configured to: acquire specific information corresponding to the feature quantity identified as having contributed to the success of the past project, from information of the past project; and display the specific information together with the factor to increase the chance of success of the target project (success rate, identify the characteristics to the project manager, Hickey ¶32).
As per claim 10, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey further discloses wherein the processor is configured to: acquire user information of the target project that is to be or is being carried out; and, in a case where the factor to increase the chance of success of the target project is unfeasible for a user identified from the user information, display an alternative factor that is feasible for the user (success rate, identify the characteristics to the project manager, Hickey ¶32; extracted information is furnished, ¶28; in ascending or descending order, ¶29) (Examiner notes the presentation of the project with a low success rate as the unfeasible for a user).
As per claim 11, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey further discloses wherein the processor is configured to extract, as the feature quantity of the past project, at least one of information on measures taken to make the past project progress, information on an action performed by a user to make the past project progress, information on a medium that has been used in the past project, information on a user participating in the project, or information on business support software used in the past project (FIG. 8D provides a control-flow diagram for the learning module, which represents one embodiment of the present invention discussed above with reference to FIG. 7. The learning module is invoked, at intervals, to analyze stored learning-module data, provided to the learning module by the project-search engine, in order to provide feedback to the project comparator. In step 850, the learning module computes correlation coefficients for each feature used in project comparison with a list of projects associated with positive feedback. In step 852, the learning module computes correlation coefficients of each feature with a list of projects associated with negative feedback. In step 854, features with correlation coefficients with respect to project lists associated with negative feedback greater than some threshold correlation-coefficient value are added to a first list. In step 856, the remaining features with correlation coefficients with respect to project lists associated with negative feedback less than or equal to the threshold value are added to a second list. In step 858, features with computed correlation coefficients with respect to project lists associated with positive result greater than a threshold value are removed from the second list. Thus, the first list includes features that appear to be strongly correlated with negative results and the second list includes features that are not strongly associated with negative results but also not strongly correlated to positive results. In step 860, the second portion of the learning module functionality is invoked, Hickey ¶30; in order to identify the positive and negative characteristics, ¶32).
As per claim 12, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey further discloses wherein the processor is configured to determine whether a project completed in the past has an final out of failure or success, based on a preset condition (ascertain the success rate, Hickey ¶32; from feedback into a learning component, ¶26; tuning modifications. ¶27).
As per claim 15, Hickey and Marinovic disclose as shown above with respect to claim 1. Marinovic further teaches wherein the processor is configured to determine whether the past project has a final outcome of success or failure based on communications exchanged between users participating in the past project (In some embodiments, the server can generate a notification based on the feature vector, a comparison of two or more feature vectors, and/or one or more characteristics associated with a user. Accordingly, the server can generate and send a notification to a user, e.g., an administrator user. The notification can be presented to the user via a display. In some embodiments, the notification can be associated with suggested team members, automatically generated teams, final scores, successful teams and their communication patterns, and/or less successful teams (e.g., low performing teams) and their communication patterns, Marinovic ¶98; The analytics server 602 can analyze the graph 609 to determine, for example, which users are frequently in communication with each other and the projects that involved the communications data for those user, and determine an impact, character, or role for each particular user across the communication data for one or more prior projects. For example, the analytics server 602 may determine whether users tend to work in teams where there is a lot of communication or the opposite. As another example, the analytics server 602 can determine which team members are the “glue” socially, who keep the projects, teams, or other aspects of the organization socially interconnected. In this example, the graph indicates to the analytics server 602 users who connect users, are very engaged on projects, and contributes to teams assembled for prior or current projects, ¶81 and Fig. 6).
Claim(s) 6 and 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over Hickey et al. (US PG Pub. 2013/0073533) and further in view of Ishibashi et al. (JP 2014215710).
As per claim 6, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey and Marinovic do not expressly disclose wherein the processor is configured to identify a single feature quantity of the past project having a largest degree of contribution to the success of the past project and display the single feature quantity of the past project as a factor to increase the chance of success of the target project.
However, Ishibashi teaches wherein the processor is configured to identify a single feature quantity of the past project having a largest degree of contribution to the success of the past project and display the single feature quantity of the past project as a factor to increase the chance of success of the target project (The similarity evaluation attribute setting unit 123 specifies an attribute having a certain correlation or more in the similarity among the project attributes, and sets the attribute to be used for the similarity evaluation. The similar project extraction unit 124 uses the attributes set by the similarity evaluation attribute setting unit 123 to compare the projects registered by the estimated project registration unit 122 and the projects executed in the past registered by the project registration unit 121. The degree of similarity is specified, and project information is extracted according to the degree of similarity for projects having a degree of similarity greater than or equal to a predetermined level, Ishibashi Page 4; above a threshold criterion for attribute, Pages 6-7; numerical importance, Page 11) (Examiner notes the ability to identify a highest attribute similarity score as the ability to identify a single feature quantity having the largest degree of contribution of success of a project).
The Hickey, Marinovic, and Ishibashi references are analogous in that both are directed towards/concerned with project management. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Ishibashi’s ability to score attributes and projects that are similar which have been successful in Marinovic and Hickey’s system to improve the system and method with reasonable expectation that this would result in a project management system that is able to generate successful projects.
The motivation being that classification means classifies past project characteristic data into successful project characteristic data and failure project characteristic data, and similar project estimation means increases or decreases the estimated project characteristic data according to the importance of the characteristic. The similar success project data is created by comparing the principal component scores of the successful project characteristic data and the estimated project characteristic data, and similar failure project data is created in the same manner. The successful project is narrowed down by comparing the performance data of similar failed project data and the estimated data is created from the narrowed successful project performance data (Ishibashi Page 2).
As per claim 7, Hickey and Marinovic disclose as shown above with respect to claim 1. Hickey does not expressly disclose wherein the processor is configured to identify one or more feature quantities of the past project having a degree of contribution to the success of the past project greater than a preset value and display the one or more feature quantities of the past project, as the feature quantity that has contributed to the success.
However, Ishibashi teaches wherein the processor is configured to identify one or more feature quantities of the past project having a degree of contribution to the success of the past project greater than a preset value and display the one or more feature quantities of the past project, as the feature quantity that has contributed to the success (The similarity evaluation attribute setting unit 123 specifies an attribute having a certain correlation or more in the similarity among the project attributes, and sets the attribute to be used for the similarity evaluation. The similar project extraction unit 124 uses the attributes set by the similarity evaluation attribute setting unit 123 to compare the projects registered by the estimated project registration unit 122 and the projects executed in the past registered by the project registration unit 121. The degree of similarity is specified, and project information is extracted according to the degree of similarity for projects having a degree of similarity greater than or equal to a predetermined level, Ishibashi Page 4; above a threshold criterion for attribute, Pages 6-7; numerical importance, Page 11).
The Hickey, Marinovic, and Ishibashi references are analogous in that both are directed towards/concerned with project management. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Ishibashi’s ability to score attributes and projects that are similar which have been successful in Marinovic and Hickey’s system to improve the system and method with reasonable expectation that this would result in a project management system that is able to generate successful projects.
The motivation being that classification means classifies past project characteristic data into successful project characteristic data and failure project characteristic data, and similar project estimation means increases or decreases the estimated project characteristic data according to the importance of the characteristic. The similar success project data is created by comparing the principal component scores of the successful project characteristic data and the estimated project characteristic data, and similar failure project data is created in the same manner. The successful project is narrowed down by comparing the performance data of similar failed project data and the estimated data is created from the narrowed successful project performance data (Ishibashi Page 2).
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629