Detailed Action
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Amendments
This action is in response to amendments filed October 28th 2025, in which Claims 1, 3-8, 10-15, & 17-20 have been amended. Claims 21-23 have been added and claims 2, 9, & 16 have been cancelled. The amendments have been entered, and Claims 1, 3-8, 10-15, & 17-23 are currently pending.
Response to Arguments
Regarding the applicant’s traversal of the 35 U.S.C. 101 rejections of the previous office action, the applicant’s arguments filed October 28th 2025 have been fully considered, and are unpersuasive.
The applicant asserts that since claim 1, as a whole, is directed toward computer operations which require configuring and updating a machine-maintained graph in memory, which cannot be done in the human mind, the claim does not recite an abstract idea.
The examiner respectfully asserts that whether the claim, as a whole, can be done with the human mind or not, if any limitation within the claim can, the claim accordingly recites an abstract idea. It is then that the additional limitations must be examined to see if they accordingly integrate the abstract idea into a practical application. Accordingly, since there are cited abstract ideas, these claims do, in fact, recite abstract ideas.
Further, the applicant asserts that the cited abstract ideas are integrated into a practical application via a cited technical improvement, asserting that the claim integrates the recited data analysis into a specific technical solution that improves the operation of the underlying computer-implemented prediction system. Specifically, the applicant clarifies that the claim does not simply recite the use of a computer as a tool to perform the abstract idea, since it requires construction and maintenance of a weighted graph data structure that represents user relationships using multiple heterogenous signals, further asserting that the claim now requires that user acceptance, rejection, or editing of a candidate virtual team directly modifies the weights or weighting factors of a persistent graph.
The examiner respectfully asserts that only “A system comprising: a processor; a graphical user interface (GUI); and a memory comprising computer program code, the memory and the computer program code configured to, with the processor” was ever cited as the limitation which simply recites the use of a computer as a tool to perform an abstract idea, as this limitation does not specify anything beyond this. Each other limitation was analyzed separately and failed to integrate for varying other reasons, as described in the previous office action and in the rejections below.
Further, the construction of the weighted graph is one of the cited abstract ideas since a person can mentally evaluate the users of a system and their relationships, and make a judgement to construct a graph from it where the nodes and edges correlate as claimed. Further, numbers or weights could be assigned. The maintenance of the graph, as argued, and the modifications of weights in the graph seems to be directed toward the following limitation:
“adjust a weight in the weighted graph or a weighting factor used to construct the weighted graph based on the user feedback, such that future candidate virtual teams generated from the weighted graph more accurately represent actual associations among the set of users”
The examiner respectfully asserts that the prior limitation is simply directed toward mere instructions to apply the judicial exception. Simply using the model to update or adjust weights is standard use of neural networks and machine learning models in general. Therefore, this does not provide evidence of integration into a practical application.
Therefore, the 35 U.S.C. 101 rejections for claim 1 are maintained, and further, the rejections for the remaining claims are also maintained under similar rationale, in addition to their own merits as listed in the previous office action and below.
Regarding the applicant’s traversal of the 35 U.S.C. 102/103 rejections of the previous office action, the applicant’s arguments filed October 28th 2025 have been fully considered, and are unpersuasive.
The applicant asserts that the examiner has failed to present evidence that a person of ordinary skill in the art could have arrived at the claimed invention, further elaborating that Kenthapadi “does not generate a candidate virtual team based on activity data”, but instead detects skill similarity for job seekers, and does not teach the construction of a weighted relationship graph from activity data or incrementally updating that graph based on interactive feedback about candidate teams. The applicant further asserts that KENTHAPADI does not form a graph of user-user relationships, nor does it incrementally update edge weights based on interactive acceptance/rejection of team proposals (user feedback)
The examiner would first like to note that the generation of candidate virtual teams actually seems to have been removed by the amendments and replaced with the identification of candidate virtual teams. The examiner respectfully asserts that, as shown at [0033] & [0045], the member skill metrics for each member of a social network (activity data) are collected based on the activities users have engaged with, and these interactions are “monitored”. Further, [0033] also calculates a “similarity value” based on this, which is used to identify, using processors, a candidate virtual team based on those similarity values, meaning that a virtual team is indeed “identified” based on the collected data. Further, the examiner would like to point out that KENTHAPADI was never claimed to teach the incremental updates based on user feedback, as ALKAN was relied upon to teach these limitations.
The construction of the weighted graph is newly amended and was not previously addressed, but the examiner respectfully asserts that this is taught by KENTHAPADI as follows:
([0048] “Members of the social networking service may establish connections with one or more other members of the social networking service. The connections may be defined as a social graph, where the member is represented by a vertex (nodes representing users) in the social graph and the edges identify connections between pairs of vertices (relationships among the users)...”)
And further:
([0167] “In some embodiments, the members with a high PS (professional score) are given higher weights than other members because the members with the high PS are usually leaders who greatly increase the value of the virtual team (e.g., a software developer with 20 years of experience and who is a Chief Technical Officer). Thus, in some example embodiments, the VTS is calculated as a weighted average of the professional scores PS1 of the virtual team members, wherein virtual team members with higher professional scores have higher weights than virtual team members with lower professional scores.”) Here, the members are explicitly shown to be weighted meaning that the nodes of the graph are weighted, thus meaning, a weighted graph is formed.
And further:
[0161] “In the virtual-team group, the job-to-group score 1408 measures the strength of the virtual team for the company associated with the job. In the virtual-team ranking phase, a score is assigned to each virtual team member based on their professional accomplishments, such as education, companies worked at, years of experience, number of followers, number of presentations at major conferences, number of published papers, number of issued patents, number of skill endorsements, etc. Thus, the professional strength for each member of the virtual team is calculated and then an aggregated value is calculated for the virtual team.” Here, the professional score is calculated by skill metrics as monitored in the collected activity data cited above, thus showing that the graph is weighted based on the collected activity data, which explicitly tracks the number of relevant activities/skill metrics.
The applicant further asserts that ALKAN is “very different” from the presently claimed invention wherein edge weights of a weighted graph are modified to improve future suggestions without a full rebuild of the model.
The examiner respectfully asserts that ALKAN is being relied upon to teach “receive user feedback comprising editing of the candidate…; and adjust a weight… or a weighting factor used… based on the user feedback such that future candidate… more accurately represent actual associations…” and Page 1, Introduction, Col. 2, Paragraph 2 has been cited to teach this. The citation describes FROTE (Feedback Rule-Based Oversampling Technique) to edit a model in response to user feedback. In the example, user feedback is used to change the output by adjusting factors such as age for a loan process, to change the model and its output without having to retrain the model. It is asserted that this user-feedback based loop being applied to the virtual teams identified by KENTHAPADI, will result in the system as claimed where factors can be changed to add or remove users from candidate teams, and is further detailed in the rejections below.
Therefore, the 35 U.S.C. 103 rejections for claim 1 are maintained. Independent claims 8 & 15 comprise similar limitations as claim 1 and thus, their rejections under 35 U.S.C. 103 are also maintained. Further, dependent claims 3-7, 10-14, & 17-23 are all dependent upon these claims and are rejected under the same rationale, in addition to their own merits, provided in the rejections below.
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 1, 3-8, 10-15, & 17-23 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.
Claim 1 recites the limitation "…future candidate virtual teams generated from the weighted graph…" in “…adjust a weight in the weighted graph or a weighting factor used to construct the weighted graph based on the user feedback, such that future candidate virtual teams generated from the weighted graph more accurately represent actual associations among the set of users”. Preceding this limitation in the claim, “a candidate virtual team” is “identified” from “the weighted graph”, but is never “generated”. As such, the scope of the claim remains unclear, as to whether candidate virtual teams are merely identified from the weighted graph, or if they are generated from it. For the sake of compact prosecution, the examiner will interpret this use of the word “generated” to be equivalent to the word “identified” herein.
Regarding claims 8 & 15, they recite similar limitations to claim 1 and are rejected under the same rationale. Further, claims 3-7, 10-14, & 17-23 all depend upon one of these claims and thereby inherit the same indefinite limitations, and are thus, rejected under the same rationale.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (mental process) without significantly more.
Regarding claim 1, in Step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A system comprising: a processor; a graphical user interface (GUI); and a memory comprising computer program code”. A system is within one of the four statutory categories of invention.
In Step 2a Prong 1 of the 101-analysis set forth in the MPEP 2106, the examiner has determined that the following limitations recite a process that, under the broadest reasonable interpretation, covers a mental process but for recitation of generic computer components:
“construct, from the activity data, a weighted graph comprising nodes representing the users and edges representing relationships among the users, with weights being based on a number of activities” (A person can mentally evaluate the users of a system and their relationships, and make a judgement to construct a graph from it where the nodes and edges correlate as claimed. Further, numbers or weights could be assigned (MPEP 2106).)
“identify, from the weighted graph, a candidate virtual team comprising a subset of the set of users, the subset comprising a plurality of users” (A person can mentally evaluate a set of users in the form of a weighted graph, and make a judgement to identify a candidate team from it that includes a plurality of the users (MPEP 2106).)
“collect activity data associated with activities in which a set of users have engaged, the activity data comprising user identifiers and activity identifiers” (A person can mentally evaluate users engaged in activities and make a judgement to collect data of which activities they are engaged, including identifying characteristics/identifiers associated with both the users and the activities (MPEP 2106).)
If claim limitations, under their broadest reasonable interpretation, covers performance of the limitations as a mental process but for the recitation of generic computer components, then it falls within the mental process grouping of abstract ideas. According, the claim “recites” an abstract idea.
In Step 2a Prong 2 of the 101-analysis set forth in MPEP 2106, the examiner has
determined that the following additional elements do not integrate this judicial exception into a
practical application:
“A system comprising: a processor; a graphical user interface (GUI); and a memory comprising computer program code, the memory and the computer program code configured to, with the processor, cause the processor to:” (Uses a computer as a tool to perform an abstract idea (MPEP 2106.05(f)).)
“present the candidate virtual team to a user using the GUI” (Adding insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g)).)
“receive user feedback comprising editing of the candidate virtual team” (Adding insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g)).)
“adjust a weight in the weighted graph or a weighting factor used to construct the weighted graph based on the user feedback, such that future candidate virtual teams generated from the weighted graph more accurately represent actual associations among the set of users” (Mere instructions to apply the judicial exception (MPEP 2106.05(f)).)
Since the claim as a whole, looking at the additional elements individually and in combination, does not contain any other additional elements that are indicative of integration into a practical application, the claim is “directed” to an abstract idea.
In Step 2b of the 101-analysis set forth in the 2019 PEG, the examiner has determined that the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
As discussed above, additional element (iv) recites use of a computer as a tool to perform the abstract idea, which is not indicative of significantly more. Additional elements (v), & (vi) recite insignificant extra-solution activities. Further, element (vi) recites steps of receiving/transmitting data via a network, which has been determined by the courts to recite a well-understood, routine, and conventional activity, which is not indicative of significantly more (Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362). Further, element (v) recites steps of presenting output of data, which the courts have found to not be indicative of significantly more (Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93). Additional element (vii) recites mere instructions to apply the judicial exception, which is not indicative of significantly more. Considering the additional elements individually and in combination, and the claim as a whole, the additional elements do not provide significantly more than the abstract idea. Therefore, the claim is not patent eligible.
Regarding claim 3, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 3 recites “wherein the user feedback includes editing data that indicates adding or removing members from or to the candidate virtual team” (In step 2a, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h).) In step 2B, generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 4, it is dependent upon claim, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 4 recites “wherein the memory and the computer program code are configured to, with the processor, further cause the processor to: responsive to acceptance of the candidate virtual team, add the candidate virtual team to a set of publicly viewable virtual teams wherein the candidate virtual team is designated as a published virtual team and information regarding the set of publicly viewable virtual teams is accessible to other users of the system” (In step 2A, prong 2, this recites insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g).) In step 2B, the courts have found steps that present output of data to be a well-understood, routine, and conventional activity, which is not indicative of significantly more (Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 5, it is dependent upon claim 4, and thereby incorporates the limitations of, and corresponding analysis applied to claim 4. Further, claim 5 recites “wherein the memory and the computer program code are configured to, with the processor, further cause the processor to present a set of GUI components including: a GUI component enabling viewing of data of members of the published virtual team” (In step 2A, prong 2, this recites insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g).) In step 2B, the courts have found steps that present output of data to be a well-understood, routine, and conventional activity, which is not indicative of significantly more (Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 6, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 6 recites “wherein causing the candidate virtual team to be presented using the GUI includes causing user IDs of members of the candidate virtual team to be presented” (In step 2A, prong 2, this recites insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g).) In step 2B, the courts have found steps that present output of data to be a well-understood, routine, and conventional activity, which is not indicative of significantly more (Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 7, it is dependent upon claim 1, and thereby incorporates the limitations of, and corresponding analysis applied to claim 1. Further, claim 7 recites “wherein the activity data includes activity data associated with a meeting activity including multiple users in attendance” (In step 2a, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h).) In step 2B, generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 8, in Step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computerized method”. A method is one of the four statutory categories of invention.
Further, claim 8 comprises similar additional limitations as claim 1, and is rejected under the same rationale, but with the following additional mental processes:
“constructing, from the activity data, a weighted graph comprising nodes representing the users and edges representing relationships among the users, with weights being based on a number of activities, similarity of activities, semantic similarity of subject matter, recency of activities, or duration of user engagement” (A person can mentally evaluate the users of a system and their relationships, and make a judgement to construct a graph from it where the nodes and edges correlate as claimed. Further, numbers or weights could be assigned (MPEP 2106).)
“identifying, from the weighted graph, a candidate virtual team comprising a subset of the set of users, the subset comprising a plurality of users fewer than the set of users” (A person can mentally evaluate a set of users in the form of a weighted graph, and make a judgement to identify a candidate team from it that includes a plurality of the users, but not including all of them (MPEP 2106).)
Further, claim 8 also recites “receiving, via the interface, user feedback comprising acceptance of the candidate virtual team, rejection of the candidate virtual team, or editing of the candidate virtual team” (In step 2A, prong 2, this recites insignificant extra-solution activity (mere data gathering) to the judicial exception (MPEP 2106.05(g).) In step 2B, the courts have found steps that store and retrieve information in memory to be a well-understood, routine, and conventional activity, which is not indicative of significantly more (Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015)).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 10, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 10 recites “wherein the user feedback includes editing data that includes editing data indicating adding or removing members of the candidate virtual team; editing data indicating editing of a title of the candidate virtual team; or editing data indicating editing of a keyword of the candidate virtual team” (In step 2a, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h).) In step 2B, generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 11, it is dependent upon claim 8 and thereby incorporate the limitations of, and corresponding analysis applied to claim 8. Further, claim 11 comprises similar additional limitations as claim 4, and is rejected under the same rationale.
Regarding claim 12, it is dependent upon claim 11, and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 12 recites “presenting a user with a set of interface components including an interface component enabling the user to view data of members of the published virtual team; an interface component enabling the user to view a set of published virtual teams that the user shares with a member of the published virtual team; or an interface component enabling the user to communicate with the members of the published virtual team as a team.” (In step 2A, prong 2, this recites insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g).) In step 2B, the courts have found steps that present output of data to be a well-understood, routine, and conventional activity, which is not indicative of significantly more (Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 13, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 13 recites “wherein causing the candidate virtual team to be presented using the interface includes causing the following to be presented: user IDs of members of the candidate virtual team; links to activities with which members of the candidate virtual team have engaged; or keywords associated with activities with which members of the candidate virtual team have engaged.” (In step 2A, prong 2, this recites insignificant extra-solution activity (mere data output) to the judicial exception (MPEP 2106.05(g).) In step 2B, the courts have found steps that present output of data to be a well-understood, routine, and conventional activity, which is not indicative of significantly more (Presenting offers and gathering statistics, OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93).)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 14, it is dependent upon claim 8, and thereby incorporates the limitations of, and corresponding analysis applied to claim 8. Further, claim 14 recites “wherein the activity data includes activity data associated with the following types of activities: a meeting activity including multiple users in attendance; a task activity associated with a task assigned to a user; a document generation activity in which a user engaged; or a communication activity between multiple users.” (In step 2a, prong 2, this recites generally linking the use of the judicial exception to a particular technological environment or field of use (MPEP 2106.05(h).) In step 2B, generally linking the use of the judicial exception to a particular technological environment or field of use is not indicative of significantly more.)
Since the claim does not recite additional elements that either integrate the judicial exception into a practical application, nor provide significantly more than the judicial exception, the claim is not patent eligible.
Regarding claim 15, in Step 1 of the 101-analysis set forth in MPEP 2106, the claim recites “A computer storage medium having computer-executable instructions”, which has been found, in light of the specification, at [0075] to be within the four statutory categories of invention. Further, claim 15 comprises similar additional limitations as claim 8, and is rejected under the same rationale.
Regarding claims 17-21, they are dependent upon claim 15 and thereby incorporate the limitations of, and corresponding analysis applied to claim 15. Further, claims 17-21 comprise similar additional limitations as claims 10-14, respectively, and are rejected under the same rationale.
Regarding claim 22, it is dependent upon claim 4 and thereby incorporates the limitations of, and corresponding analysis applied to claim 4. Further, claim 22 recites “wherein the adding of the candidate virtual team to the set of publicly viewable virtual teams comprises indexing the candidate virtual team in an enterprise search index to enable discovery by other users of the system” (In step2A, prong 2, this recites mere instructions to apply the judicial exception (MPEP 2106.05(f).) In step 2B, mere instructions to apply the judicial exception is not indicative of significantly more.)
Regarding claim 23, it is dependent upon claim 11 and thereby incorporates the limitations of, and corresponding analysis applied to claim 11. Further, claim 23 comprises similar additional limitations as claim 22, and is rejected under the same rationale.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Kenthapadi, K. et al. US PGPUB No. US 2018/0189739 A1 (hereafter, KENTHAPADI), and further in view of Alkan, Ö. Et al., “FROTE: Feedback Rule-Driven Oversampling for Editing Models.” Available at https://arxiv.org/pdf/2201.01070 on January 6 2022 (hereafter, ALKAN)
Regarding claim 1, KENTHAPADI teaches “A system comprising: a processor; a graphical user interface (GUI); and a memory comprising computer program code”:
([0023] “FIG. 19 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein (computer program code), according to an example embodiment.”)
And further:
[Fig. 19]
The above cited figure of the system shows that it comprises processors (1904) and a memory storage (1906) storing instructions/code (1910.)
And further:
([0006] “FIG. 2 is a screenshot of a user interface that includes job recommendations, according to some example embodiments.”)
And finally:
[Figure 2]
Here, the cited user interface can be seen to be a “graphical user interface (GUI).”
Further, KENTHAPADI teaches “the memory and the computer program code configured to, with the processor, cause the processor to: collect activity data associated with activities in which a set of users have engaged, the activity data comprising user identifiers and activity identifiers”:
([0033] “One general aspect includes a method including operations for generating, by one or more processors, member skill metrics for members of a social network (activity data), and for detecting a request for presentation of information about a job posting of a company. The method also includes determining one or more job skill metrics (activity identifiers) associated with the job posting, and calculating, by the one or more processors, a similarity value between the job posting and each of one or more employees of the company who are members (user identifiers) of the social network (activity data associated with activities in which a set of users have engaged.) ...”)
And further:
([0045] “As members interact with the social networking service provided by the social networking server 112, the social networking server 112 is configured to monitor these interactions (collecting activity data, these interactions being activity identifiers). Examples of interactions include, but are not limited to, commenting on posts entered by other members, viewing member profiles (user identifiers), editing or viewing a member's own profile, sharing content from outside of the social networking service (e.g., an article provided by an entity other than the social networking server 112), updating a current status, posting content for other members to view and comment on, suggesting jobs for the members, conducting job-post searches, and other such interactions. In one embodiment, records of these interactions are stored in the member activity database 116 (collecting activity data associated with the activity), which associates interactions (activity identifiers) made by a member with his or her member profile (user identifiers) stored in the member profile database 120. In one example embodiment, the member activity database 116 includes the posts created by the members of the social networking service for presentation on member feeds.”)
Further, KENTHAPADI teaches “construct, from the activity data, a weighted graph comprising nodes representing the users and edges representing relationships among the users, with weights being based on a number of activities”:
([0048] “Members of the social networking service may establish connections with one or more other members of the social networking service. The connections may be defined as a social graph, where the member is represented by a vertex (nodes representing users) in the social graph and the edges identify connections between pairs of vertices (relationships among the users)...”)
And further:
([0167] “In some embodiments, the members with a high PS (professional score) are given higher weights than other members because the members with the high PS are usually leaders who greatly increase the value of the virtual team (e.g., a software developer with 20 years of experience and who is a Chief Technical Officer). Thus, in some example embodiments, the VTS is calculated as a weighted average of the professional scores PS1 of the virtual team members, wherein virtual team members with higher professional scores have higher weights than virtual team members with lower professional scores.”) Here, the members are explicitly shown to be weighted meaning that the nodes of the graph are weighted, thus meaning, a weighted graph is formed.
And further:
[0161] “In the virtual-team group, the job-to-group score 1408 measures the strength of the virtual team for the company associated with the job. In the virtual-team ranking phase, a score is assigned to each virtual team member based on their professional accomplishments, such as education, companies worked at, years of experience, number of followers, number of presentations at major conferences, number of published papers, number of issued patents, number of skill endorsements, etc. Thus, the professional strength for each member of the virtual team is calculated and then an aggregated value is calculated for the virtual team.” Here, the professional score is calculated by skill metrics as monitored in the collected activity data cited above, thus showing that the graph is weighted based on the collected activity data, which explicitly tracks the number of relevant activities/skill metrics.
Further, KENTHAPADI teaches “identify, from the weighted graph, a candidate virtual team comprising a subset of the set of users, the subset comprising a plurality of users”:
([0033] “…The method also includes determining one or more job skill metrics associated with the job posting, and calculating… a similarity value between the job posting and each of one or more employees of the company who are members of the social network. The similarity value is based on a comparison of the one or more job skill metrics (similarity value based on collected activity data) with the member skill metrics of each employee of the company. The method further includes identifying, by the one or more processors, a virtual team of a plurality of employees having the similarity value above a predetermined threshold, and causing presentation of the virtual team in a user interface (Here, a plurality of employees are identified and presented as a “virtual team” based on the previously collected activity data of the set of users.).”)
And further, KENTHAPADI teaches “present the candidate virtual team to a user using the GUI”:
([0033] “…The method further includes identifying, by the one or more processors, a virtual team of a plurality of employees having the similarity value above a predetermined threshold, and causing presentation of the virtual team in a user interface. Here, the previously generated virtual team is presented to a user via the previously cited user interface.”)
KENTHAPADI fails to explicitly teach “receive user feedback comprising editing of the candidate virtual team; and adjust a weight in the weighted graph or a weighting factor used to construct the weighted graph based on the user feedback such that future candidate virtual teams generated from the weighted graph more accurately represent actual associations among the set of users.” However, analogous art, ALKAN, does teach “receive user feedback comprising editing of the candidate…; and adjust a weight… or a weighting factor used… based on the user feedback such that future candidate… more accurately represent actual associations…”:
([Page 1, Introduction, Col. 2, Paragraph 2] “In this paper, we propose an algorithm called FROTE (Feedback Rule-Based Oversampling Technique) to edit an ML model for tabular data in response to user feedback rules. FROTE thus complements the input transformation method of (Daly et al., 2021). Given an input dataset, the algorithm first modifies the training data if allowed, and then augments it so that re-training the model (adjusting the weights based on the user feedback) on the augmented data results in better alignment with the feedback rules (…such that future output is more accurate). FROTE can thus be used with any classification algorithm that takes training data as input and produces a classifier as output (the output being the generated candidate, which can notably be used with “any classification algorithm”); the algorithm (which could be proprietary) is treated as a black box. Unlike Daly et al. (2021), the user feedback is directly encoded in the model.”) Further, When combined with KENTHAPADI, the “generated candidate” would be a “virtual team”, as claimed, and the more accurate outputs will “more accurately represent actual associations among the set of users, which will take the form of a weighted graph”.
It would be obvious to one of ordinary skill in the art, prior to the effective filing date of the claimed invention, to combine the base reference of KENTHAPADI with the teachings of ALKAN because KENTHAPADI uses ML techniques in a real-world scenario with user feedback and ALKAN promotes using the user feedback of a ML model to train it.
One of ordinary skill in the art would be motivated to do so because as ALKAN points out in its abstract, “to deploy such ML models in the real world, one must address problems that arise from the model being inherently governed and limited by the training data. In many applications, domain expert knowledge (gathered from user feedback) could be used to improve performance either where data coverage is sparse, or where decision boundaries may have changed over time.”
Regarding claim 3, KENTHAPADI in view of ALKAN teaches the limitations of claim 1. Further, ALKAN teaches “wherein the user feedback includes editing data that indicates adding or removing members from or to the candidate…”:
([Page 1, Col. 2, paragraph 3 onward] “We use Figure 1 to be suggestive of a loan approval scenario and to illustrate our solution. Suppose there is a new policy to lower the ages of applicants for whom loans are approved (The various ages are qualifications for candidates signifying a “member” to be edited). Rather than crafting rules from scratch, the user relies on the existing ML model and accompanying rule-based explanations to capture relevant dependencies among a potentially large number of features, and only modifies rules that involve age. Given the resulting feedback rule set, the user may wish to relabel and remove existing instances as shown in Figure 1(b) (removing members). FROTE then generates synthetic instances that reflect both the feedback rules as well as the existing data. Synthetic data generation can address the challenge of insufficient training data in the region to be adjusted, as seen in Figure 1(c). For data generation, we build upon the SMOTE method (Chawla et al., 2002) in several ways; other methods could also be adapted.”) Further, when combined with KENTHAPADI, as cited above, the “generated candidate” would be a “virtual team” as claimed, and the members are therefore removed from the candidate team, as claimed. The method of ALKAN is meant to illustrate feedback-driven updates which, when applied to the virtual teams of KENTHAPADI, will achieve the limitation as claimed.
Regarding claim 4, KENTHAPADI in view of ALKAN teaches the limitations of claim 1.
Further, KENTHAPADI teaches “wherein the memory and the computer program code are configured to, with the processor, further cause the processor to responsive to acceptance of the candidate virtual team, add the candidate virtual team to a set of publicly viewable virtual teams wherein the candidate virtual team is designated as a published virtual team and information regarding the set of publicly viewable virtual teams is accessible to other users of the system.”:
([0072-0074] “FIG. 6 is a diagram of a user interface 602, according to some example embodiments, for presenting a virtual team associated with a job posting. The user interface 602 is for presenting a job page to the member (“member” meaning member of the social network but not the team. In this case, the published virtual team is being presented to other users of the system). The job page includes information about the job, such as the name of the company and connections who work at the company 604, buttons for applying to the job (showing the social network “member” is not yet a member of the team, thus qualifying as another user of the system) at the company website or for saving the job into the member's list of interesting jobs, a job description 606, a connections area 608, and a virtual team area 610. The connections area 608 presents one or more members of the social network who work at the company that posted the job (published the candidate virtual team) and who are socially connected to the member in the social network (a publicly viewable candidate virtual team) (directly or indirectly)… the user interface 602 shows people who may work with the member if the member joined the company (Again, showcasing the “member” to be a social network member whom the candidate virtual team is being presented to)...”)
Regarding claim 5, KENTHAPADI in view of ALKAN teaches the limitations of claim 4. Further, KENTHAPADI teaches “wherein the memory and the computer program code are configured to, with the processor, further cause the processor to present a set of GUI components including a GUI component enabling viewing of data of members of the published virtual team”:
([0072-0074] “FIG. 6 is a diagram of a user interface 602, according to some example embodiments, for presenting a virtual team associated with a job posting. The user interface 602 is for presenting a job page to the member (“member” meaning member of the social network but not the team. In this case, the published virtual team is being presented to a user who is not a member of the team). The job page includes information about the job, such as the name of the company and connections who work at the company 604, buttons for applying to the job (showing the social network “member” is not yet a member of the team) at the company website or for saving the job into the member's list of interesting jobs, a job description 606, a connections area 608, and a virtual team area 610. The connections area 608 presents one or more members of the social network who work at the company that posted the job (published the candidate virtual team) and who are socially connected to the member in the social network (a publicly viewable candidate virtual team)(directly or indirectly).
The virtual team area 610 includes a header (e.g., "Meet the team at Co Corp"), and information about the members of the virtual team (A GUI component enabling viewing of data of members of the published virtual team). For example, one of the virtual team members is highlighted, and information 614 of this member is presented in more detail, including the member's profile picture, name, professional experience, and skills. A scrolling option is available (e.g., "View next") to select the next member of the virtual team.
On the left, profile pictures 612 for other team members are presented, and if the member clicks on one of the profile pictures 612, the detailed information for the selected team member is presented. Thus, the user interface 602 shows people who may work with the member if the member joined the company (A GUI component enabling viewing of data of members of the published virtual team). One of the reasons for choosing a job is that a member may want to work in a good team. These are possibly the people whom the member will interact with on a day-to-day basis.”)
Regarding claim 6, KENTHAPADI in view of ALKAN teaches the limitations of claim 1. Further, KENTHAPADI teaches “wherein causing the candidate virtual team to be presented using the GUI includes causing user IDs of members of the candidate virtual team to be presented”:
[Figure 5]
In the figure (506) shows a virtual team with user IDs of members (Jane Beta, Joe Alpha, Jack Gamma).
Regarding claim 7, KENTHAPADI in view of ALKAN teaches the limitations of claim 1. Further, KENTHAPADI teaches “wherein the activity data includes activity data associated with a meeting activity including multiple users in attendance”:
([0045] “As members interact with the social networking service provided by the social networking server 112, the social networking server 112 is configured to monitor these interactions (collecting activity data). Examples of interactions include, but are not limited to, commenting on posts entered by other members (a communication/meeting activity between multiple users), viewing member profiles, editing or viewing a member's own profile, sharing content from outside of the social networking service (e.g., an article provided by an entity other than the social networking server 112) (a communication/meeting activity between multiple users), updating a current status, posting content for other members to view and comment on (a communication/meeting activity between multiple users), suggesting jobs for the members (a communication/meeting activity between multiple users), conducting job-post searches, and other such interactions. In one embodiment, records of these interactions are stored in the member activity database 116 (collecting activity data associated with the activity), which associates interactions made by a member with his or her member profile stored in the member profile database 120. In one example embodiment, the member activity database 116 includes the posts created by the members of the social networking service for presentation on member feeds (a communication/meeting activity between multiple users).”)
Regarding claim 8, KENTHAPADI teaches “A computerized method” ([0023] “FIG. 19 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein (A computerized method), according to an example embodiment.”)
Further, claim 8 comprises similar additional limitations as claim 1, and is rejected under the same rationale.
Regarding claim 10, KENTHAPADI in view of ALKAN teaches the limitations of claim 8. Further, ALKAN teaches “wherein the received user feedback data includes editing data that includes at least one of the following: editing data indicating editing of the set of members of the candidate…; editing data indicating editing of a title of the candidate…; editing data indicating editing of a keyword of the candidate…”:
([Page 1, Col. 2, paragraph 3 onward] “We use Figure 1 to be suggestive of a loan approval scenario and to illustrate our solution. Suppose there is a new policy to lower the ages of applicants for whom loans are approved (“age” qualifies as a keyword to be edited). Rather than crafting rules from scratch, the user relies on the existing ML model and accompanying rule-based explanations to capture relevant dependencies among a potentially large number of features, and only modifies rules that involve age (“age” qualifies as a keyword to be edited). Given the resulting feedback rule set, the user may wish to relabel and remove existing instances as shown in Figure 1(b) (editing data indicating edits made). FROTE then generates synthetic instances that reflect both the feedback rules as well as the existing data. Synthetic data generation can address the challenge of insufficient training data in the region to be adjusted, as seen in Figure 1(c). For data generation, we build upon the SMOTE method (Chawla et al., 2002) in several ways; other methods could also be adapted.”) Further, when combined with KENTHAPADI, as cited above, the “generated candidate” would be a “virtual team” as claimed.
Regarding claim 11, KENTHAPADI in view of ALKAN teaches the limitations of claim 8. Further, claim 11 recites similar additional limitations as claim 4 and is rejected under the same rationale.
Regarding claim 12, KENTHAPADI in view of ALKAN teaches the limitations of claim 11. Further, KENTHAPADI teaches “presenting a user with a set of interface components including an interface component enabling the user to view data of members of the published virtual team; an interface component enabling the user to view a set of published virtual teams that the user shares with a member of the published virtual team; or an interface component enabling the user to communicate with the members of the published virtual team as a team.”:
([0072-0074] “FIG. 6 is a diagram of a user interface 602, according to some example embodiments, for presenting a virtual team associated with a job posting. The user interface 602 is for presenting a job page to the member (“member” meaning member of the social network but not the team. In this case, the published virtual team is being presented to a user who is not a member of the team). The job page includes information about the job, such as the name of the company and connections who work at the company 604, buttons for applying to the job (showing the social network “member” is not yet a member of the team) at the company website or for saving the job into the member's list of interesting jobs, a job description 606, a connections area 608, and a virtual team area 610. The connections area 608 presents one or more members of the social network who work at the company that posted the job (published the candidate virtual team) and who are socially connected to the member in the social network (a publicly viewable candidate virtual team)(directly or indirectly).
The virtual team area 610 includes a header (e.g., "Meet the team at Co Corp"), and information about the members of the virtual team (A GUI component enabling viewing of data of members of the published virtual team). For example, one of the virtual team members is highlighted, and information 614 of this member is presented in more detail, including the member's profile picture, name, professional experience, and skills. A scrolling option is available (e.g., "View next") to select the next member of the virtual team.
On the left, profile pictures 612 for other team members are presented, and if the member clicks on one of the profile pictures 612, the detailed information for the selected team member is presented. Thus, the user interface 602 shows people who may work with the member if the member joined the company (A GUI component enabling viewing of data of members of the published virtual team). One of the reasons for choosing a job is that a member may want to work in a good team. These are possibly the people whom the member will interact with on a day-to-day basis.”)
Regarding claim 13, KENTHAPADI in view of ALKAN teaches the limitations of claim 8. Further, KENTHAPADI teaches “wherein causing the candidate virtual team to be presented using the interface includes causing the following to be presented: user IDs of members of the candidate virtual team; links to activities with which members of the candidate virtual team have engaged; or keywords associated with activities with which members of the candidate virtual team have engaged”:
[Figure 5]
In the above figure (506) shows a virtual team with user IDs of members (Jane Beta, Joe Alpha, Jack Gamma).
And further:
[Figure 6]
In the above figure, at (614), activity keywords are shown (Patents, EE, Leadership, Patent Prosecution).
Regarding claim 14, KENTHAPADI in view of ALKAN teaches the limitations of claim 8. Further, KENTHAPADI teaches “wherein the activity data includes activity data associated with the following types of activities: a meeting activity including multiple users in attendance; a task activity associated with a task assigned to a user; a document generation activity in which a user engaged; or a communication activity between multiple users.”:
([0045] “As members interact with the social networking service provided by the social networking server 112, the social networking server 112 is configured to monitor these interactions (collecting activity data). Examples of interactions include, but are not limited to, commenting on posts entered by other members (a communication activity between multiple users), viewing member profiles, editing or viewing a member's own profile, sharing content from outside of the social networking service (e.g., an article provided by an entity other than the social networking server 112) (a communication activity between multiple users), updating a current status, posting content for other members to view and comment on (a communication activity between multiple users), suggesting jobs for the members (a communication activity between multiple users), conducting job-post searches, and other such interactions. In one embodiment, records of these interactions are stored in the member activity database 116 (collecting activity data associated with the activity), which associates interactions made by a member with his or her member profile stored in the member profile database 120. In one example embodiment, the member activity database 116 includes the posts created by the members of the social networking service for presentation on member feeds (a communication activity between multiple users).”)
Regarding claim 15, KENTHAPADI teaches “One or more computer storage media having computer-executable instructions” ([0023] “FIG. 19 is a diagrammatic representation of a machine in the form of a computer system within which a set of instructions may be executed for causing the machine to perform any one or more of the methodologies discussed herein (computer-executable instructions), according to an example embodiment.”)
Further, [Fig. 19]
The cited figure of the system shows that it comprises processors (1904) and a memory storage (1906) storing instructions/code (1910.)
Further, claim 15 comprises similar additional limitations as claim 1, and is rejected under the same rationale.
Regarding claims 17-21, KENTHAPADI in view of ALKAN teaches the limitations of claim 15. Further, claims 17-21 recite similar additional limitations as claims 10-14, respectively, and are rejected under the same rationale.
Regarding claim 22, KENTHAPADI in view of ALKAN teaches the limitations of claim 4. Further, KENTAHPADI teaches “wherein the adding of the candidate virtual team to the set of publicly viewable virtual teams comprises indexing the candidate virtual team in an enterprise search index to enable discovery by other users of the system”:
([Figure 11] We can see in the cited figure, that search query 1102, allows a job search at 1104, which searches the published jobs of 1106, meaning that the published teams as cited above can be searched, meaning that they are indeed indexed in a database of some kind.)
Regarding claim 23, KENTHAPADI in view of ALKAN teaches the limitations of claim 11. Further, claim 23 comprises similar additional limitations as claim 22, and is rejected under the same rationale.
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 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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/Matthew Lee Lewis/Examiner, Art Unit 2144
/TAMARA T KYLE/Supervisory Patent Examiner, Art Unit 2144