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 .
This communication is in response to Applicant’s Arguments filed 04/29/2026 regarding application 18/146,181. Claims 1-2, 7-8, and 13-14 are amended and hereby entered. Claims 3, 5, 9, 11, 15, and 17 are canceled.
Response to Arguments
Regarding 35 USC 101:
The applicant submits the judicial exception is integrated into a practical application because the generation of data elements connected through nodes and edges recites an improvement to the functioning of a computer system. However, the limitations broadly recite generation steps that comprise the abstract idea, not additional elements that integrate the judicial exception into practical application. Generating information regarding user connections with other users can be performed mentally by humans. For example, the human brain mentally maps relationships between people through nodes representing people and edges representing how they are connected. Further, deriving weights or grouping connections of users that have stronger or higher quantities of connections can also be performed mentally before recommending engagements with others. For example, a person can mentally give more weight to a user being a part of a specific group when recommending the user collaborate with another similar group. These claim limitations do not improve the underlying computing technology itself; rather, they use a computer to perform what a human would otherwise perform mentally when making evaluations and recommendations, see MPEP 2106.05(f). Therefore, the applicants’ arguments are unpersuasive, and the rejection is maintained.
Regarding 35 USC 103:
The applicant’s amendments have necessitated new prior art. The new rejection relies on a different combination of prior art, rendering the applicant’s arguments moot.
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.
Claim (1, 7, 13), (2, 8, 14), (4, 10, 16), and (6, 12, 18) are rejected under 35 U.S.C. 101 because the claimed invention is directed to judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) with no practical application and without significantly more.
The claimed invention is directed to an abstract idea in that the instant application is directed to a mental process (See MPEP 2106.04(a)(2)(III)). The independent claims (1, 7, and 13) recite a method and systems to evaluate data associated with various entities and make recommendations based on the processed data. These claim elements are being interpreted as concepts performed in the human mind (including observation, evaluation, judgement, and opinion). Using social graph data to make recommendations can equivalently be achieved by human observation and evaluation of communications and entity information. For example, analyzing data and other information related to users can be done by a human to make recommendations and suggestions. The claims recite an abstract idea consistent with the “mental process” grouping set forth in the MPEP 2106.04(a)(2)(III).
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites an “apply it” (or an equivalent) with the judicial exception, or merely includes instructions to implement an abstract idea. The instant application is directed towards a method and systems to implement the identified abstract idea of receiving information, processing information, and displaying the result of the analysis (i.e. processing graphical data structure information to recommend collaboration connections and the like) in a general-purpose computing environment. The independent claims recite the additional elements “a processor” and “a non-transitory computer-readable medium”. These claim elements are recited at a high level of generality such that it amounts to no more than mere instructions to apply the exception using general-purpose computing components. The machines merely act as a modality to implement the abstract idea and are not indicative of integration into a practical application (i.e., the additional elements are simply used as a tool to perform the abstract idea), see MPEP 2106.05(f).
The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. As discussed in Step 2A Prong Two analysis, the additional elements in the claims amount to no more than mere instructions to apply the exception using generic computer components. The same analysis applies here in 2B and does not provide an inventive concept.
Regarding the dependent claims:
Claims (2, 8, 14), (4, 10, 16), and (6, 12, 18) recite further embellishments of the existing abstract idea and do not introduce any new abstract ideas or new additional elements impacting analysis under 35 USC 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims (1, 7, 13), (2, 8, 14), (4, 10, 16), and (6, 12, 18) are rejected under 35 U.S.C. 103 as being unpatentable over Bhat (US 20130297689 A1) in view of McGarr (US 20220351142 A1).
Regarding Claim 1, 7 and 13 (substantially similar in scope and language), Bhat teaches:
A method for data processing, comprising: retrieving, from a first data source associated with a group-based communication platform associated with an entity, connection information of a plurality of users of the group- based communication platform, the connection information corresponding to inclusion of individual users of the plurality of users in one or more communication channels of the group- based communication platform; [see at least Bhat: (Para 0008) “A social graph is constructed to be representative of a social network by including nodes and edges representing activities in the social network. Activities in communication transactions of a communication network are identified and activity stream tuning parameters are determined for a user of the social network from the identified relationships”, (Para 0009) “An example social network 100 is illustrated in FIG. 1 wherein various users 110a-110n, representatively referred to herein as user 110, interact through communication channels 120a-120n, representatively referred to as communication channels 120, formed in a communication network. It is to be understood that while only individual channels are illustrated at each user 110a-110n, users may communicate through multiple, concurrently active communication channels, as will be recognized by the ordinarily skilled artisan upon review of this disclosure. Facilitating the aforementioned interactions are user interfaces 130a-130n, representatively referred to as user interfaces 130, which afford user interaction with social network 100, and convert and/or format human intelligible user content into data that can be transported over a communication network, and vice-versa”]
generating a graphical data structure based on the connection information and the membership information, wherein generating the graphical data structure comprises: [The limitation recites generating a graphical data structure based on both connection and membership information. Prior art Bhat shows a social graph formed on connection information and inclusion of social network services. The social network services include membership information; see at least Bhat: (Figure 3B-3C), (Para 0018) “Communication analysis layer 264 implements computational and data processing resources to perform content analysis on the analysis data. Example processes by which such analysis is achieved is described in detail below with reference to FIG. 4. Briefly, communication analysis layer categorizes/classifies communication events to produce therefrom a data structure referred to herein as a social graph.”, (Para 0019) “Communication analysis layer 264 constructs and updates a social graph formed from such semantic relationships across a wide array of entities, e.g., individuals, organizations, topics, etc. An example social graph is illustrated as social graph 340 in FIG. 3B. It is to be understood that a social graph, when describing numerous users, different activities of those users, topics, communication channels, social network channels, etc., may be extensive and complex. Social graph 340 may also be extensible, allowing expansion as users, communication channels, social network services, and so on, are added”, (Para 0010) “Social network 100 may include one or more social networking services 140a-140z, which distribute the user content among social network members subscribing thereto”,]
generating one or more user nodes corresponding to identifiers for the plurality of users, [see at least Bhat: (Para 0020) “As illustrated in FIG. 3B, nodes of social graph 340 may represent various entities that interact or are acted upon, such as persons, organizations, meetings, topics, shared content, essentially without limit”]
generating one or more group nodes corresponding to the one or more groups associated with the entity, [see at least Bhat: (Para 0020) “As illustrated in FIG. 3B, nodes of social graph 340 may represent various entities that interact or are acted upon, such as persons, organizations, meetings, topics, shared content, essentially without limit”]
generating one or more connection edges corresponding to the connection information, [see at least Bhat: (Para 0020) “For example, the relationships formed by nodes 342 and 344, and edges 343 and 345 represent or indicate that Persons A and B simply emailed one another”]
generating one or more membership edges corresponding to the membership information, [see at least Bhat: (Para 0010) “Social network 100 may include one or more social networking services 140a-140z, which distribute the user content among social network members subscribing thereto”, (Para 0029) “Stream tuning process 420 may, for example, evaluate new edges formed in the social graph that share nodes representing activity the particular user is already following”]
generating one or more communication channel passthrough nodes corresponding to respective communication channels of the one or more communication channels, wherein the one or more communication channel passthrough nodes organize the one or more connection edges into groups based at least in part on one or more associations between the respective communication channels and the connection information, [see at least Bhat: (Figure 3C), (Para 0009) “An example social network 100 is illustrated in FIG. 1 wherein various users 110a-110n, representatively referred to herein as user 110, interact through communication channels 120a-120n, representatively referred to as communication channels 120, formed in a communication network. It is to be understood that while only individual channels are illustrated at each user 110a-110n, users may communicate through multiple, concurrently active communication channels”, (Para 0027) “For example, cluster analysis may reveal that multiple conversations in which the same topic was discussed occurred between the same group of people. Accordingly, edges between the nodes representing such people may be formed and weighted so as to indicate an interest in the topic”]
deriving, for each communication channel passthrough node of the one or more communication channel passthrough nodes, a weight based at least in part on a respective quantity of the one or more connection edges that pass through the communication channel passthrough node, [see at least Bhat: (Figure 3C), (Para 0023) “As illustrated in FIG. 3C, interactions between or pertaining to other entities determined by communication analysis layer 264, representatively illustrated at arrows 363, 365, can influence the weighting of certain edges, representatively illustrated at edges 366”]
whether one or more users of the individual users voluntarily joined the respective communication channel corresponding to the communication channel passthrough node, and [see at least Bhat: (Para 0023) “based on a user joining, actively participating during and after a meeting (e.g., the user has seen enough)”]
weighting the one or more connection edges based at least in part on the weight derived for each communication channel passthrough node; and [see at least Bhat: (Figure 3C), (Para 0023) “As illustrated in FIG. 3C, interactions between or pertaining to other entities determined by communication analysis layer 264, representatively illustrated at arrows 363, 365, can influence the weighting of certain edges, representatively illustrated at edges 366.”]
generating, based at least in part on the connection information, the membership information, and the weighted one or more connection edges of the graphical data structure, an engagement recommendation for users of the plurality of users included in a first group of the one or more groups to engage with users of the plurality of users included in a second group of the one or more groups. [see at least Bhat: (Paragraph 0023) “Following the interactions represented in social graph 340 and weighting corresponding edges in user graph 360 can be used to enrich the user 362 activity feed with highly relevant information: … following a person based on frequency, duration and quality of interactions with an individual; following a community based on frequency, duration and quality of interactions with a group of individuals”, (Para 0030) “Activity tuning process 400 may include a feedback process 460 to adjust the user's tuning parameters in accordance with actions taken by the user on the activity stream in the presentation layer. For example, an event may be presented in the user's activity stream and the user may dismiss the event as being uninteresting or irrelevant. Feedback process 460 may insert an edge between the appropriate nodes to indicate to the analysis processes 415 and 430 that the particular user has expressly dismissed the event and/or may modify user-specific edge weights accordingly. Analysis processes 415 and 430 may note the added or weighted edge in the social graph and avoid computing unnecessary recommendation data based on the user's expressed dismissal”]
While Bhat teaches a method in which the connection data (e.g. edges) identifies users as being members of a groups, it does not explicitly teach directly retrieving membership information that identifies users as members of groups, or deriving weights based on periods of time between interactions. However, McGarr teaches:
retrieving, from a second data source associated with the entity, membership information of the plurality of users, the membership information corresponding to inclusion of the individual users of the plurality of users in one or more groups of a plurality of groups associated with the entity; [see at least McGarr: (Figure 3), (Para 0006) “FIG. 3 illustrates an example user interface for identifying members of an organization based on one or more attributes of the members, as described herein.”]
a period of time since a last interaction event occurred in a respective communication channel associated with the communication channel passthrough node, and [see at least McGarr: (Para 0017) “ In various examples, the communication platform can be configured to assign a weight to messages transmitted via the communication platform between users of an organization. In some examples, the weight can be determined based on the content of the message. As non-limiting examples, the weight can be determined based on… times (e.g., timestamps) associated with messages (e.g., initial message, response time, etc.), (Para 0018) “In some examples, the interaction graph can represent the communications and data flow associated with a particular team, workspace, and/or channel, such as a representation of a frequency of messages and respective response times thereto”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine the data processing method (Bhat) with specific membership and event information (McGarr). One of ordinary skill would have recognized using known information in a social graph would allow for better representations of connections and information. See at least Bhat Paragraph 8 stating, “A social graph is constructed to be representative of a social network by including nodes and edges representing activities in the social network”. Therefore, the combination would have yielded predictable results to one of ordinary skill, rendering the combination obvious.
Regarding Claim 2, 8, and 14, the combination of Bhat and McGarr teach the limitations of claim 1. Bhat further teaches:
The method of claim 1, further comprising: generating communication connection strength values between the individual users indicated in the connection information based at least in part on the one or more connection edges, [see at least Bhat: (Claim 5) “assigning values to the weights based on a quality of the respective activities represented by the edges” (Para 0002) “The volume of information contained in activity streams depends on the number of people in one's network, the number of one's group associations and the activity level thereof, and the strength of one's connections”, (Para 0023) “Values assigned to weights 364 can be used both positively to prioritize related activities and negatively to de-prioritize related activities displayed to an end user”]
wherein the engagement recommendation is generated based at least in part on generating the communication connection strength values. [see at least Bhat: (Claim 5) “assigning values to the weights based on a quality of the respective activities represented by the edges”, (Paragraph 0023) “Following the interactions represented in social graph 340 and weighting corresponding edges in user graph 360 can be used to enrich the user 362 activity feed with highly relevant information: … following a person based on frequency, duration and quality of interactions with an individual; following a community based on frequency, duration and quality of interactions with a group of individuals”]
Regarding Claim 4, 10, and 16, Bhat teaches the limitations of claim 2. Bhat further teaches:
The method of claim 2, further comprising: generating group connection strength values between individual groups of the plurality of groups associated with the entity based at least in part on the membership information and the communication connection strength values between users of the individual groups; [see at least Bhat: (Figure 3C), (Para 0023) “Edges of user graph 360 may be weighted, representatively illustrated at weight 364, where the weighting is based on, for example, frequency, duration, timing and quality of interactions in the follow-model. Values assigned to weights 364 can be used both positively to prioritize related activities and negatively to de-prioritize related activities displayed to an end user… following a person based on frequency, duration and quality of interactions with an individual; following a community based on frequency, duration and quality of interactions with a group of individuals”]
While Bhat teaches generating grouped connection strength values based on individual users it does not explicitly teach generating an engagement recommendation based on connection strength between two different groups.
However, McGarr teaches:
wherein the engagement recommendation is generated based at least in part on a first group connection strength value of the group connection strength values that is associated with the first group and the second group [see at least McGarr: (Para 0011) “recommending an action to perform with respect to a group of users based on a frequency of messages received by the group of users”, (Para 0014) “interaction between the one or more users of the first group and the user of the second group is above a threshold level of interaction, the communication platform can provide a recommended action for the user to perform with respect to the communication platform”]
Further, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to combine a social graph (Bhat) with generating group to group recommendations (McGarr). One of ordinary skill would have recognized that grouping users together through cluster analysis and collaborative filtering in a social graph could be used to make relevant recommendations for the entities within the graph. See at least Bhat paragraph 23 stating, “Following the interactions represented in social graph 340 and weighting corresponding edges in user graph 360 can be used to enrich the user 362 activity feed with highly relevant information”. See at least McGarr paragraph 12 stating, “FIG. 9 illustrates an example process for recommending an action to perform to increase collaboration between groups of users, as described herein” and paragraph 27 stating, “The email service provider can then generate an interaction graph associated with the organization based on the processed emails and provide recommendations for actions to perform based on the interaction graph”. Therefore, the combination of a social graph with group-to-group recommendations would have yielded predictable results to one of ordinary skill, rendering the combination obvious.
Regarding Claims 6, 12, and 18, the combination of Bhat and McGarr teach the limitations of claim 1. Bhat further teaches:
The method of claim 1, wherein the one or more groups associated with the entity correspond to one or more functional roles of the plurality of users within the entity. [see at least Bhat: (Para 0020) “As illustrated in FIG. 3B, nodes of social graph 340 may represent various entities that interact or are acted upon, such as persons, organizations, meetings, topics, shared content, essentially without limit… Person A is a consultant on Topic A, is a consultant to an organization which produces products with Topic A, and so on”]
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Examiner Benjamin Truong, whose telephone number is 703-756-5883. The examiner can normally be reached on Monday-Friday from 9 am to 5 pm (EST).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Nathan Uber SPE can be reached on 571-270-3923. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300 Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/B.L.T. /Examiner, Art Unit 3626
/SANGEETA BAHL/Primary Examiner, Art Unit 3626
Prosecution Insights