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 . In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
DETAILED ACTION
This Final Office Action is in response Applicant communication filled on 08/14/2025.
Status of Claims
Claims 1,6,7,8 have been amended, Claims 2,11, 17 have been canceled.
Claims 1, 3-10, 12-16, 18-20 are currently pending and have been rejected as follows.
Response to Amendments / Arguments
Applicant’s 08/14/2025 amendment necessitated new grounds of rejection in this office action.
Objection to Claim 1
Objection to claim 1 is withdrawn in view of Applicant’s amendment.
Response to 35 USC 112(b) rejection
35 USC 112(b) rejection in the prior act is withdrawn in view of Applicant’s amendment as suggested by Examiner.
Response to 35 USC 101 rejection
Step 2A prong one: Remarks 08/14/2025 p.9 ¶2 argues newly amended limitation of: “the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users” as amended at independent Claims 1,6,7, cannot be practically performed in the human mind.
Examiner fully considered the step 2A prong one argument but respectfully disagrees.
Here, while the server performing the collecting of data as in “the extraction further comprises collecting data from a server” (independent Claims 1,6,7) cannot be solely substituted by the human mind, it could nonetheless be argued that such “server” represents a computer environment (MPEP 2106.04(a)(2) III C #2), a computer tool (MPEP 2106.04(a)(2) III C #3) or a generic computer component (MPEP 2106.04(a)(2) III C #1) to perform the abstract collecting, within the combination of collecting information, analyzing it, and displaying certain results of the collection and analysis1, as enunciated by MPEP 2106.04(a)(2) III A, 5th bullet point. While the participation of the “server” as a computer aid (MPEP 2106.04(a)(2) III C #1,#2, #3) in “collecting data” can be debated, its associated provi[sioning] of “services to the plurality of users and interactions between the plurality of users and a plurality of followed users” is undeniably abstract and ineligible because it recites, describes or sets forth fundamental practices (MPEP 2106.04(a)(2) A) and/or commercial interactions (MPEP 2106.04(a)(2) B) namely service-based commercial practices or interactions. It thus becomes increasingly clear that the recitation of “the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users”, does not preclude the claims from reciting, describing or setting forth the abstract exception.
As explained above, the use of “server” could be argued right from the onset as a computer aid, as tested per MPEP 2106.04(a)(2) III C #1,#2,#3, to aid in the collect[ion] and provi[sioning] of “services to the plurality of users and interactions between the plurality of users and a plurality of followed users” thus integral to the abstract exception . At the very most, the use of such “server”, if more granularly tested, at the subsequent steps, would represent a computer-based additional element to be more granularly tested at the subsequent steps below.
Step 2A prong two: Remarks 08/14/2025 p.9 last ¶ - p.10 ¶3 argues that “the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users” as amended at each of independent Claims 1,6,7 help to collect mass amount of data in a cost efficient way (Remarks 08/14/2025 p.10 ¶1) reflecting an improvement in technology (Remarks 08/14/2025 p.10 ¶3).
Examiner fully considered the Step 2A prong two argument but respectfully disagrees finding it unpersuasive because according to MPEP 2106.04(d)(1) ¶2 the specification must set forth a technological improvement, in more than a conclusionary manner, and the claim itself must reflect such a technological improvement. Here, the Applicant’s rebuttal fails on both ends as the Original Specification does not set forth, and the claims themselves do not reflect the cost-efficient way in which the data is collected. Even if they did, such improvement directed to cost efficiency remains entrepreneurial, cost-asserted, improvement in the abstract idea itself, not improvement in actual technology. This fact is important since MPEP 2106.05(a) II is clear that improvement in the abstract idea itself is not improvement in technology. Similarly MPEP 2106.04 I cites “Myriad, 569 U.S. at 591, 106 USPQ2d at 1979” to stress that even a “groundbreaking, innovative, or even brilliant discovery does not by itself satisfy the §101 inquiry”. Thus, Examiner submits, in the arguendo, that even a groundbreaking, innovative or brilliant apparatus, method and product that would provide[...] “services to the plurality of users and interactions between the plurality of users and a plurality of followed users” by “collecting data from a server”, should similarly not render the claims eligible. Simply put, when tested per MPEP 2106.04(a)(2) II A, the provi[sion] [of] “services to the plurality of users and interactions between the plurality of users and a plurality of followed users” would still represent a fundamental economic practice or principle and/or building block of modern economy which still falls well-within the realm of the abstract idea, no matter of the involvement of the “ server” [in] “collecting data”. The “Myriad” rationale was corroborated by SAP Am, Inc v InvestPic as cited by MPEP 2106.04 (a)(2) I.C (i). Specifically, in SAP Am Inc v InvestPic, LLC, 898 F.3d 1161, 127 U.S.P.Q.2d 1597 (Fed. Cir. 2018), the Federal Circuit ruled that “even if one assumes that the techniques claimed are groundbreaking, innovative, or even brilliant those features are not enough for eligibility because their innovation is innovation in ineligible subject matter. An advance of that nature is ineligible for patenting”. “no matter how much of an advance in the field the claims [would] recite, the advance [would still] lie entirely in the realm of abstract ideas with no plausibly alleged innovation in non-abstract application realm”.
Here, similar to “SAP”, Remarks 08/14/2025 p.9 last ¶ - p.10 ¶3, argue in favor of a cost efficient way in collecting data, and thus should be interpreted as improvement of the abstract idea itself as opposed to improvement to actual technology. In fact MPEP 2106.05(a) I cites BSG Tech LLC v. Buyseasons, Inc. 899 F.3d 1281, 1287-88, 127 USPQ2d 1688,1693-94 (Fed. Cir. 2018); to show that providing historical usage information to users while they are inputting data, to improve the quality and organization of information added to a database, would at most represent an improvement to information stored by a database, not a technological improvement in the database’s functionality. In a similar vein, MPEP 2106.04(a)(2) II C cites Interval Licensing LLC, v. AOL, Inc., 896 F.3d 1335, 127 USPQ2d 1553 (Fed. Cir. 2018) to state that social activities such as providing information to a person without interfering with the person’s primary activity, still set forth the abstract idea. It then follows that the computerization used here in “extracting” or “collecting” “data” “associated with posting data of a topic”, as evidence of abstract social activities that “provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users” would analogously still set forth the abstract idea,
Also here, following the same MPEP 2106.04(d)(1) ¶2, Examiner finds that, the Original Specification does not set forth, and the claims themselves do not reflect any indication of a mass amount of data, as alleged by Remarks 08/14/2025 p.10 ¶ 1, let alone technological details of collecting such mass amount of data. In fact, MPEP 210.05(f)(2) ¶1 is clear that use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data)2 represent mere invocation of computers or other machinery merely as a tool to perform an existing process, which does not integrate the abstract idea into a practical application. For example, MEP 2106.05(f)(2) iii cites FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293,1296 (Fed. Cir. 2016), to state that a process for monitoring audit log data that is executed on a general-purpose computer where the increased speed in the process comes solely from the capabilities of the general computer, represent mere invocation of computers or machinery as a tool, which does not integrate the abstract idea into a practical application. In a similar vein MEP 2106.05(a) I cites the same FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089,1095, 120 USPQ2d 1293,1296 (Fed Cir 2016) to state that accelerating a process of analyzing audit log data when the increased speed comes solely from the capabilities of a general-purpose computer, may not be sufficient to show an improvement in computer-functionality.
Examiner follows such guidelines and further investigates FairWarning supra, and finds that the Federal Circuit found unpersuasive an argument that that requiring large number of calculations would render the claims eligible because the 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, even in situations of “inability for the human mind to perform each claim step does not alone confer patentability. As we have explained, “the 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” citing Bancorp Servs., 687 F.3d at 1278.
The Federal Circuit’s rationale in FairWarning corroborates Planet Bingo LLC v. VKGS LLC U.S. Court of Appeals, Federal Circuit 2013-1663 August 26,2014, 576 Fed Appx 1005, 2014 BL 235907, where Planet Bingo unpersuasively argued that handling millions of preselected Bingo numbers by computer program makes it impossible for the invention to be carried out manually. Both “FairWarning” and “Planet Bingo” follow the Supreme Court’s decisions which made it clear that judicial exceptions need not be old or long-prevalent, and that even newly discovered or novel judicial exceptions are still exceptions. See MPEP 2106.04 I ¶5. For example, the Federal Circuit echoed the Supre Court ruling in Fairwarning Ip, LLC v. Iatric Sys., Inc., 839 F.3d 1089, 120 U.S.P.Q.2d 1293 (Fed. Cir. 2016), Court Opinion, as cited by MPEP 2106.04, by finding that Fairwarning’s alleged compilation and combination of disparate information sources to make it possible to generate a full picture of a user's activity, identity, frequency of activity, and the like in a computer environment, is representative of merely selecting information, by content or source, for collection, analysis, and announcement which does not differentiate the process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information based category of abstract ideas. Elec. Power, 830 F.3d 1350, [2016 BL 247416], 2016 WL 4073318, at *4. It then follows that here the similarly amended “extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users”, as raised by Applicant at Remarks 08/14/2025 p.9 last ¶ - p.10 ¶3 would also not render the claims patent eligible.
Step 2B: Remarks 08/14/2025 p.11 ¶3-¶4 argues the recitation of “the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users” represents a particular solution or particular machine to address the computer-centric challenge of accurately evaluate the influence of a user in a specific group.
Examiner follows the guidelines for the particular machine test of MPEP 2106.05(b) ¶3, and
evaluates and reincorporates findings such as the mere instructions to apply an exception consideration of MPEP 2106.05(f). Examiner further follows MPEP 2106.05(d) II guidelines and carries over the above findings tested per MPEP 2106.05 (f) to submit that as shown above, the argued server, as an additional computer-based element, merely apply the abstract idea. For these same reasons, said computer-based additional element also does not provide significantly more than the abstract idea itself, in light of MPEP 2106.05(f) as sufficient option for evidence without having to rely on the well understood routine and conventional test of MPEP 2106.05(d).
Yet, assuming arguendo that further evidence would still be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further point to MPEP 2106.05(d) to demonstrate that said additional elements remain well-understood, routine, conventional. In such case, Examiner would rely on case law and Original Specification ¶ [0025], ¶ [0035] reciting at a high level of generality a server providing the social network services (SNS).
Thus, Examiner provided a preponderance of legal and/or factual evidence showing that, far from proving a particular machine or particular solution, the “server” of claims 1,6,7, as raised by Remarks 08/14/2025 p.11 ¶3-¶4, merely applies the abstract idea, without providing anything more than what was already identified as the abstract idea. Thus, the use of “server” in expression “the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users”, does not prove significantly more than what has already been established as the abstract idea.
Therefore, the claims remain patent ineligible despite the amendment of “the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users”.
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Response to prior art Arguments
Remarks 08/14/2025 p.13 last ¶-p.14 ¶1 argues Lozano et al, US 20120143815 A1 does not teach or suggests “wherein the calculating further comprises calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user and determine a magnitude relation of numeral values representing influence which is compared against a threshold”.
Examiner fully considered the argument but respectfully disagrees finding it unpersuasive:
Examiner first reminds the Applicant that the test for obviousness is not whether the claimed invention must be expressly suggested in any one or all of the references. Rather, the test is what the combined teachings of the references would have suggested to those of ordinary skill in the art. See In re Keller, 642 F.2d 413, 208 USPQ 871 (CCPA 1981).
Based on such guidleines, the Examiner points to Lozano who recites at ¶ [0045]:
“The term jointly means that the influence of all the bloggers on each other” [as example of bidirectional or two-way influence] “is assessed based on a joint consideration of all bloggers into a joint model, rather than performing isolated pair-wise tests between pairs of bloggers disregarding the presence and impact of other bloggers. As described with respect to Fig.4, this joint consideration of all bloggers involve a loop over all bloggers, where at each iteration one focuses on a specific blogger and the influence of all bloggers is jointly assessed on the specific blogger under consideration, rather than literal joint processing”.
Thus, Lozano ¶ [0045] teaches or at the very least suggests: “wherein the calculating further comprises calculating the influence obtained”… “from the other user having a bidirectional link with respect to the target user” as amended at each of current independent Claims 1,6,7.
Next, Lozano ¶ [0063] and ¶ [0083] clarifies that the influence of one user (i.e. blogger) over another user (i.e. other blogger) refers to content [i.e. words] of one user (i.e. blogger) over another user (i.e. other blogger). For example, Lozano ¶ [0083] 4th sentence states: …”if a blogger Bi influences a blogger Bj, the impact of Bi should be felt throughout the entire content of Bj “.
Now that it is clear that the Lozano’s impact or influence refers to content [i.e. words], it becomes clear that elimination of overly frequent words in the content that blogger A influences the other blogger B, as taught by Lozano ¶ [0063] 1st sentence, and similarly, the decision not to select or exclude the Bi's past content as predictor for Bj's future content at Lozano ¶ [0083] last two sentences, would teach or at the very least suggests “eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user” as amended at each of current independent Claims 1,6,7.
Thus, it is obvious that Lozano ¶ [0045], [0063], [0083], [0093] teaches or suggests the variables’ calculation of word frequencies that one blogger impacts or influences over another blogger, including the elimination, reduction or exclusion of overly frequent words or influence that one blogger impacts or influences over another blogger, renders obvious expression “calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user” as amended at each of independent Claims 1,6,7.
-> As per, “determine a magnitude relation of numeral values representing influence which is compared against a threshold”, as amended at each of independent Claims 1,6,7, the Examiner finds that this limitation necessitates new grounds of rejection, as follows:
Kim et al US 20150120717 A1 recites ¶ [0199] 2nd sentence: Fig.17D shows… analysis for community detection (Modularity), and influence (using PageRank) [interpreted as benchmark or threshold]. For PageRank definition see Kim ¶ [0071] last sentence, ¶ [0249] 2nd sentence.
Kim ¶ [0074] noting an example where: Amy is clearly the top influencer with the greatest number of followers and highest PageRank [benchmark or threshold] score. Although Carol has 2 followers, she has lower PageRank metric than Brian who has one follower. However, Brian's one follower is the most-influential Amy (with 4 followers), while Carol's 4 followers are low influencers with (0 followers each). The intuition is that, if a few experts consider someone an expert, then s/he is also an expert. However, the PageRank algorithm gives a better measure of influence than only counting the number of followers. As will be described below, the PageRank algorithm and other similar ranking algorithms can be used with the proposed systems and methods described herein.
Kim ¶ [0099] last sentence: the display of the listing of users in the community is ranked [or compared] according to degree of influence within the community and/or within all communities [as another example of benchmark] for topic T (e.g. as provided to display screen 125 of Fig.1). In accordance with block 308, users UT are then split up into their community graph classifications such as UC1, UC2,… UCn. ¶ [0141], ¶ [0146], ¶ [0167] 2nd-3rd sentences, ¶ [0170], ¶ [0171], ¶ [0179]- ¶ [0181], ¶ [0188], ¶[0192], ¶ [0194] 5th sentence, ¶ [0268] last sentence, ¶ [0305] last sentence.
Lozano also, independently from the primary reference, teaches or at least suggests:
- “determine a magnitude relation of numeral values representing influence which is compared against a threshold” starting with
Lozano ¶ [0065]: “Granger Causality: A collection of bloggers is said to influence Blogger Bi if their collective past content (e.g. blog posts), together with the past content of Blogger Bi, is predictive of the future content of Blogger Bi, more so than” [thus compared against] “the past content of Blogger Bi alone” [as a threshold]. see Lozano ¶ [0066] for additional details. Also,
Lozano ¶ [0080] A natural extension of the Granger-causality test would be to assert that blogger Bi influences blogger Bj if the past content of Bi is significantly helpful if predicting the future content of Bj, compared to [threshold of] using the past content of Bj alone. More formally, the multivariate regression for Bjt in terms of Bjt-1,…,Bjt-d and Bit-1,…,Bi t-d will be compared to the multivariate regression [or threshold] for Bjt in terms of Bj t-1…Bj t-d alone. Similarly,
Lozano ¶ [0091] 6th sentence: GrangerRank is compared against the citation count to measure how well GrangerRank aligns with the more common measure in this domain. Also, Lozano ¶ [0099] In this domain, number of citations is commonly viewed as a valid measure of authority given disciplined scholarly practice of citing prior related work. Thus, citation-count based ranking is considered as the standard for comparison of new techniques. Table 2 shows that GrangerPageRank and GrangerOutDegree have high positive rank correlation with citation counts. This experiment confirms that the present embodiment is able to identify key influencers in this domain, and is in agreement with how this community recognizes its authorities.
Accordingly, there is not only a single but actually a preponderance of factual evidence showing that the prior art of Kim and Lozano teaches or suggests the contested limitation.
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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 1,3-10,12-16,18-20 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ) 2nd 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 pre-AIA the applicant regards as the invention.
Claims 1,6,7 have each been amended to each recite, among others:
“wherein the calculating further comprises calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user and determine a magnitude relation of numeral values representing influence which is compared against a threshold”
“causing”… “display of the influence of the target user based on the calculation result”
Claims 1,6,7 as amended are rendered vague and indefinite because now it is unclear if
- “the influence of the target user” as subsequently recited at last limitation of “causing”, “display” refers to antecedently recited “the influence obtained”, “an influence” eliminat[ed] or reduc[ed], or [an] “influence” represent[ed] “by numeral values” as newly amended.
Claims 1,6,7 as amended are rendered vague and indefinite because there is insufficient antecedent basis for the calculation result” [bolded emphasis added].
Claims 1,6,7 are recommended to be amended to recite, among others, as example only:
wherein the calculating further comprises calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user and determine a magnitude relation of numeral values representing influence which is compared against a threshold
causing…display of the influence of the target user based on the
calculated influence.
Claims 3-5,8,9,10[?] are rejected based on rejected parent independent Claim 1.
Claims 12-16 are rejected based on rejected parent independent Claim 6.
Claims 18-20 are rejected based on rejected parent independent Claim 7.
Claim 10 still recites, among others: “The influence calculation apparatus according to claim 2” rendering claim 10 vague and indefinite because claim 2 has now been canceled, and thus it is now unclear upon which claim does claim 10 depends upon.
Claim 10 is recommended to be amended to depend upon parent claim 1.
Clarification and correction are required.
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Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(d):
(d) REFERENCE IN DEPENDENT FORMS.—Subject to subsection (e), a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
The following is a quotation of pre-AIA 35 U.S.C. 112, fourth paragraph:
Subject to the following paragraph [i.e., the fifth paragraph of pre-AIA 35 U.S.C. 112], a claim in dependent form shall contain a reference to a claim previously set forth and then specify a further limitation of the subject matter claimed. A claim in dependent form shall be construed to incorporate by reference all the limitations of the claim to which it refers.
Claims 10 is rejected under 35 U.S.C. 112(d) or pre-AIA 35 U.S.C. 112, 4th paragraph, as being of improper dependent form for failing to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Claim 10 is dependent and has been amended to depend upon canceled parent Claims 2. Thus claims 10 fails to further limit the subject matter of the claim upon which it depends, or for failing to include all the limitations of the claim upon which it depends. Applicant may cancel the claim(s), amend the claim(s) to place the claim(s) in proper dependent form, rewrite the claim(s) in independent form, or present a sufficient showing that the dependent claim(s) complies with the statutory requirements. Clarification and correction are required.
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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, 3-10, 12-16, 18-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea, here abstract idea) without significantly more. The claim(s) recite(s) describe or set forth the abstract idea of “influence calculation” as summarized in the preamble of each of independent Claims 1,6, which falls within abstract Certain method of organizing human activities grouping implemented through equally abstract Mental Processes3. Specifically, Claims 1,9,17 recite, describe or set forth fundamental economic practices or principles [MPEP 2106.04 (a)(2) II A] performed on data received from commercial interactions [MPEP 2106.04(a)(2) II B].
Such fundamental economic practices/principles and/or commercial interactions are recited, described or set forth as an example of behavior related market segmentation expressed by the limitations “retrieving a plurality of posting data of the target user over a network”, “retrieving the posting data from the posting database” at dependent Claims 8,15,20, “extracting a subset of a set of a plurality of users from the set of the plurality of users, wherein the subset of the plurality of users is associated with posting data of a topic”, “provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users” and “an influence of a target user in the subset on the basis of a relationship between the target user included in the subset and another user included in the subset” at each of independent Claims 1,6,7, “wherein the posting database includes a plurality of sets of a user name of the target user and a piece of posting data of the target user, and the piece of posting data comprises an identifier of the piece of posting data, a posting date and time, a number of impressions in a group, a number of interactions of followers in the group, and a number of interactions of followers outside the group” at dependent Claims 9,16,20; “other user having a bidirectional link with respect to the target user”
at independent Claims 1,6,7, “wherein the relationship indicates a frequency of interaction performed by the other user with respect to a message transmitted by the target user” at dependent Claims 3,12,18. Such market segmentation of retriev[ed] “posting data” is associated with equally abstract mitigative forms of the influence expressed as “eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user” at independent Claims 1,6,7, “reducing the influence from the other user in accordance with the frequency of interaction” at dependent Claims 3,12,18; “reducing the influence from the other user in accordance with the frequency of interaction and a number of users for whom there are one-way or two-way links with the other user” at dependent Claims 4,13,19
Examiner also points to MPEP 2106.04(a)(2) II ¶6, 4th sentence to submit that the sub-groupings of Certain methods of organizing human activity encompass both activity of a single person and activity that involves multiple people, and thus, certain activity between a person and a computer may fall within the "certain methods of organizing human activity" grouping.
Here, such activity is recited with respect to “posting data” at independent Claims 1,6,7 and the subsequent recitations of “retrieving a plurality of posting data of the target user over a network; and storing the plurality of posting data of the target user in a posting database, wherein the calculating further comprises: retrieving the posting data from the posting database” at dependent Claims 8,15,20, “wherein the posting database includes a plurality of sets of a user name of the target user and a piece of posting data of the target user, and the piece of posting data comprises an identifier of the piece of posting data, a posting date and time, a number of impressions in a group, a number of interactions of followers in the group, and a number of interactions of followers outside the group” at dependent Claims 9,16,20, which similarly would not preclude the claims from setting forth certain method of organizing human activities.
Also, such fundamental economic or commercial practices could be also argued as implementable through equally abstract Mental Processes, either by pen and paper or by computer-aided evaluation and judgement typical to MPEP 2106.04(a)(2) III. For example here, as in Electric Power Group v Alstom, S.A., 830 F.3d 1350,1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016), as cited by MPEP 2106.04(a)(2) III A, 5th bullet point, the Examiner finds that the claims recite the abstract combination of collecting analyzing information and displaying certain results of the collection and analysis4 that also correspond to the “Mental Processes” grouping.
The collection is set forth as “collecting data” at independent Claims 1,6,7, “retrieving a plurality of posting data of the target user over a network” and “retrieving the posting data from the posting database” at dependent Claims 8,15,20.
The analysis is set forth as “extracting a subset of a set of a plurality of users from the set of the plurality of users, wherein the subset of the plurality of users is associated with posting data of a topic”, “calculating an influence of a target user in the subset on the basis of a relationship between the target user included in the subset and another user included in the subset wherein the calculating further comprises calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user and determine a magnitude relation of numeral values representing influence which is compared against a threshold” at independent Claims 1,6,7, “calculating the influence obtained by reducing the influence from the other user in accordance with the frequency of interaction” at dependent Claims 3,12,18, “calculating the influence obtained by reducing the influence from the other user in accordance with the frequency of interaction and a number of users for whom there are one-way or two-way links with the other user” at dependent Claims 4,13,19; “calculating, based the plurality of posting data in the posting database, the influence of the target user in the subset” at Claims 8,15,20;
The display is set forth as “causing” “display the influence of the target user based on” [certain results of the collection and analysis set forth here as] “the calculation result” at independent Claims 1,6,7.
Equally important, MPEP 2106.04(a)(2) III C states that #1 Performing mental process on generic computer, #2 Performing mental process in computer environment, #3 Using computer as tool to perform a mental process does not preclude a claim from reciting the abstract idea.
It then follows that here, as in MPEP 2106.04(a)(2) III C, nominal recitation of “wherein the extraction further comprises collecting data from a server” at independent Claims 1,6,7, “storing the plurality of posting data of the target user in a posting database” at dependent Claims 8,15,20,
and “wherein the causing display further comprises causing display in either the influence calculation apparatus or another apparatus connected to the influence calculation apparatus via a communication network” at dependent Claim 10, would similarly represent a generic computer,
computer environment, or computer tool from which the user data is gathered or obtained or displayed to, which, as tested per MPEP 2106.04(a)(2) III C would not preclude the claims, from reciting, describing or setting froth the abstract idea.
In an abundance of caution, the aforementioned “server” (Claims 1,6,7), “database” (Claims 8,9,15,16,20) and “display unit” (Claims 6,7), “network” (Claims 5,8,10,14,15,20) will be more granularly investigated below, beyond mere use of physical aids.
For now, it is clear that, given is a preponderance of legal evidence above, the claims’ character as a whole remains undeniably abstract.
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This judicial exception is not integrated into a practical application because per Step 2A prong two, because the individual or combination of the additional, computer-based elements is found to merely apply the already recited abstract idea. Here, the additional computer-based elements could be viewed as the “server” (Claims 1,6,7), “processor” (Claims 1,7), and possibly the “database” (Claims 8,9,15,16,20), “display unit” (Claims 6,7), and “network” (Claims 5,8,10,14,15,20) if not already construed as physical aids in the abstract processes identified above. Specifically here, at Step 2A prong two, when tested per MPEP 2106.05(f)(2), such additional, computer-based elements are merely used as tools to apply the aforementioned abstract idea5, and to perform economic tasks or other tasks to receive, store and transmit data6 and possibly to monitor audit log data executed on a general-purpose computer7 as well as to require use of software or other computer components to tailor information8. Specifically, here, the “processor” of independent Claims 1,7, when tested per MPEP 2106.05(f)(2)(i)9 merely applies the aforementioned abstract, business processes and possibly an underlining algorithm for “calculating an influence of a target user in the subset on the basis of a relationship between the target user included in the subset and another user included in the subset” at 2nd limitation of independent Claims 1,7. Also here, the “processor” of Claims 1,7, when tested per MPEP 2106.05(f)(2)(v)10 merely tailors information and provides it to the user on a generic computer, as evidence by the last limitation of causing, on a display device/unit display the influence of the target user at independent Claims 1,6,7. As per the recitation of “collecting data from a server” at independent Claims 1,6,7, examiner points to MPEP 210.05(f)(2) ¶1 which states that use of a computer or other machinery in its ordinary capacity for economic or other tasks (e.g., to receive, store, or transmit data) represent mere invocation of computers or other machinery merely as a tool to perform an existing process, which does not integrate the abstract idea into a practical application. For example, MEP 2106.05(f)(2) iii cites FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293,1296 (Fed. Cir. 2016), to state that a process for monitoring audit log data represents mere invocation of computer components or machinery to apply the abstract exception, without integrating it into a practical application. Finally, here, the recitations of “storing the plurality of posting data of the target user in a posting database” and “retrieving the posting data from the posting database” at dependent Claims 8,15,20, could be viewed, per MPEP 2106.05(f)(2), as additional computer-based elements merely used to perform economic tasks or other tasks to receive, store and transmit data11.
Alternatively, when tested per MPEP 2106.05(h) (ii),(vi), such computerized implementation via “server”, “processor”, “database”, “display unit” as identified supra, could be viewed as a form of narrowing the identified abstract idea to a field of use or technological environment in a manner not meaningfully different than identifying participants [here “set” and “subset” of “plurality of users”] as well as narrowing the combination of collection of collecting information, analyzing it, and displaying certain results of the collection and analysis, as in Electric Power Group, LLC v Alstom S.A., 830 F.3d 1350,1354, 119 USPQ2d 1739,1742 (Fed. Cir. 2016). These do not integrate the abstract idea into a practical application. As per recitations of “a network” as in “retrieving a plurality of posting data of the target user over a network” (dependent Claims 8,15,20), and “another apparatus connected to the influence calculation apparatus via a communication network” (dependent Claims 5,10,14), MPEP 2106.05(h) (viii)12 is clear that language specifying that the abstract idea of budgeting was to be implemented using a communication medium that broadly included a generally disclosed network, is merely a requirement to use the exception to a particular technological environment, which also does not integrate the abstract idea into a practical application.
Accordingly, no matter which of the MPEP 2106.05(f) or MPEP 2106.05(h) test is used, the result is the same, namely that the additional computer-based elements, do not integrate the abstract idea into a practical application. Step 2A prong two.
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The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because, Examiner follows MPEP 2106.05 (d) II guidelines and carries over the findings tested per MPEP 2106.05 (f) and/or (h) to submit that as shown above, the additional computer-based elements merely apply the already recited abstract idea [MPEP 2106.05(f)] and/or provide a narrowing of the abstract idea to a field of user or technological environment [MPEP 2106.05(h)]. For these reasons, said computer-based additional elements also do not provide significantly more than the abstract idea itself, in light of MPEP 2106.05(f) and/or (h) as sufficient option(s) for evidence. For example, the Examiner investigated FairWarning IP, LLC v. Iatric Sys, 839 F3d 1089,1095, 120 USPQ2d 1293,1296 (Fed Cir 2016), cited by MPEP 2106.05(f)(2), and found that capabilities of the additional, computer-based elements to monitor audit log data executed on a general-purpose compute, are examples of applying the abstract idea, which does not integrate it into a practical application. Step 2A prong two. Further, upon closer investigation of “FairWarning” supra, Examiner states that the Federal Circuit found unpersuasive an argument of compilation, combination and accessing of disparate information sources to make it possible to generate a full picture of frequency of activity and the like in a computer environment. Thus, Examiner reasons that utilization of “server”, “processor”, “database”, “network” and “display unit” for respective “retrieving a plurality of posting data , “extracting users” and “display the influence of the target user” (independent Claims 1,6,7) to subsequently picture or “indicate” “a frequency of interaction performed by the other user with respect to a message transmitted by the target user” (dependent Claims 3, 12, 18), “reducing the influence from the other user in accordance with the frequency of interaction and a number of users for whom there are one-way or two-way links with the other user” (dependent Claims 4,13,19) would also be unpersuasive in rendering the claims eligible. As the Court explained in FairWarning, selecting information, by content or source, for collection, analysis and announcement, does nothing significant to differentiate a process from ordinary mental processes, whose implicit exclusion from 101 undergirds the information-based category of abstract idea. Also as tested per MPEP 2106.05(h)13 the narrowing of combination of “retrieving” or collecting information, “calculating” or analyzing it and displaying certain results of the collection and analysis, to a field of use or technological environment, represented here by a computerized learning environment, does also not provide significantly more.
Based on such legal evidence conferred by the MPEP 2106.05(f) and/or (h) tests above, Examiner submits that the additional computer-based elements do not provide significantly more. Yet, assuming arguendo, that further evidence would be required to demonstrate conventionality of the additional, computer-based elements, the Examiner would further point to MPEP 2106.05(d) to demonstrate that said additional elements remain well-understood, routine, conventional. In such case, Examiner would rely as evidence on Applicant’s own Original Specification as follows:
Per MPEP 2106.05(d)(I)(2) Examiner points to Applicant’s own Specification as follows:
- Original Specification ¶ [0016] reciting at high level: “As shown in Fig. 1, the influence calculation apparatus 10 according to the present embodiment is realized by a hardware configuration of a general computer or computer system and includes an input device 101, a display device 102, an external I/F 103, a communication I/F 104, a processor 105, and a memory device 106. These pieces of hardware are connected via a bus 107 such that they can communicate with each other”.
- Original Specification ¶ [0017] 2nd sentence reciting at high level of generality: “The display device 102 is, for example, a display or the like”.
- Original Specification ¶ [0019] 1st sentence reciting at high level of generality: “The communication I/F 104 is an interface for connecting the influence calculation apparatus 10 to a communication network such as the Internet”.
- Original Specification ¶ [0025], ¶ [0035] reciting at a high level of generality a server providing the social network services (SNS).
Additionally or alternatively, per MPEP 2106.05(d)(II), the additional “processor”, “database”, “network”, “display unit” could also be viewed to perform well-understood, routine or conventional functions to gather statistics14 / electronic recordkeeping15 [here “posting database includes a plurality of sets of a user name of the target user and a piece of posting data of the target user, and the piece of posting data comprises an identifier of the piece of posting data, a posting date and time, a number of impressions in a group, number of interactions of followers in the group, and a number of interactions of followers outside the group” at Claims 9,16,20] / store and retrieve information in memory16 [here “collecting data from a server” at independent Claims 1,6,7, “retrieving a plurality of posting data of the target user over a network”; “storing the plurality of posting data of the target user in a posting database” at dependent Claims 8,15,20] receiving or transmitting data over a network17 (here “retrieving a plurality of posting data of the target user over a network” at dependent Claims 8,15,20), including utilizing intermediary computer to forward information (here “the causing display further comprises causing display in either the influence calculation apparatus or another apparatus connected to the influence calculation apparatus via communication network” at dependent Claims 5,10,14); as well as arrange hierarchy of groups and sort information18 (here “extracting a subset of a set of a plurality of users from the set of the plurality of users, wherein the subset of the plurality of users is associated with posting data of a topic” at dependent Claims 1,6,7), and performing repetitive calculations19 (here the further “calculating” at dependent Claims 3,4,12,13,18,19).
All of these demonstrate that the additional computer-based elements fail to provide anything significantly more than what is already well-understood, routine and conventional in light MPEP 2106.05(d). Thus, Claims 1, 3-10, 12-16, 18-20, although directed to statutory categories (here “apparatus” or machine at Claims 1,3-5,8,9,10[?], “method” or process at Claims 6,12-16, “computer-readable non-transitory recording medium” or computer product at Claims 7,18-20, they still recite, or at least set forth the abstract idea (Step 2A prong one), with their additional, computer-based elements not integrating the abstract idea into a practical application (Step 2A prong 2) or providing significantly more (Step 2B). Thus, Claims 1,3-10,12-16,18-20 are ineligible.
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Rejections under 35 § U.S.C. 103
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 of this title, 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.
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.
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.
Claims 1,3-8,10[?],12-15,18,19 rejected under 35 U.S.C. 103 as being unpatentable over:
Kim et al US 20150120717 A1 hereinafter Kim, in view of
Lozano et al, US 20120143815 A1 hereinafter Lozano. As per,
Claims 1,6,7 Kim teaches “An influence calculation apparatus comprising a processor configured to execute operations comprising”: / “An influence calculation method, comprising” / “A computer-readable non-transitory recording medium storing computer-executable program instructions that when executed by a processor cause a computer to execute operations comprising”:
(Kim ¶ [0079], 1st-2nd sentences, ¶ [0090] 1st sentence, ¶ [0092] ¶ [0224], ¶ [0291])
- “extracting a subset of a set of a plurality of users from the set of the plurality of users, wherein the subset of the plurality of users is associated with posting data of a topic wherein the extraction further comprises collecting data from a server that provides services to the plurality of users and interactions between the plurality of users and a plurality of followed users”
(Kim teaches several examples as follows: Fig. 3 step 302 and ¶ [0093] 2nd-7th sentence: The process in Fig.3 assumes social network data is available to the server 100, and the social network data includes multiple users that are represented as a set U. At block 301, the server 100 obtains a topic represented as T. For example, a user may enter in a topic via a GUI displayed at the computing device 101, and the computing device 101 sends the topic to the server 100. At block 302, server uses the topic to determine users from social network data which are associated with the topic. This determination can be implemented in various ways and will be discussed below. The set of users associated with the topic is represented as UT, where UT is a subset of U.
Kim ¶ [0118] the operation of determining users related to a topic (e.g. blocks 302 and 402) includes using a topic to identify popular documents within a certain time interval. It is recognized that this process also identify users related to a topic. In particular, when a topic (e.g. a keyword) is provided to the system of for text analysis, the system returns documents (e.g. posts, blogs, tweets, messages, articles, etc.) that are related and popular to the topic. Using the proposed systems and methods described herein, the instructions include server 100 determining the author or authors of the popular documents. In this way, the author or authors are identified as the top users who are related to the given topic. An upper limit n may be provided to identify top n users who are related to the given topic, where n is an integer. In an example embodiment, n is 5000, although other numbers can be used. The top n users may be determined according to a known or future known ranking algorithm, or using known or future known authority scoring algorithm for social media analytics. For each of the top n users, the server determines the users who follow each of the top n users. Those users that are not considered as part of top n users, or do not follow top n users are not part of users UT in the topic network. In an example embodiment, the set of users UT includes top n users and their followers.
Kim ¶ [0119] In another example performing the operation of determining users related to a topic (e.g. block 302 and block 402), the computer executable instructions include: determining documents (posts, articles, tweets, messages) correlated with the given topic; determining the author or authors of the documents; and establishing the author or authors as users UT associated with the given topic. ¶ [0120] In another example embodiment of performing the operation of determining users related to a topic (e.g. block 302 and block 402), the operation includes identifying an expertise vector of a user. This example embodiment is explained using Figs.8-11.
Kim ¶ [0135] 3rd-6th sentences: At block 1102, server 100 identifies users having Topic A (e.g. topic being queried) listed in their expertise vector. At block 1103, of the identified users, the server determines which users appear on the highest number of lists associated with Topic A. At block 1104, the top n users who appear on the highest number of lists are the experts of Topic A. In other words, the server creates the set of users UT to include the top n users and their followers. Similarly, Fig. 12 step 1202, Fig. 22 step 2203, etc. ¶ [0299] 3rd-4th sentences: text sources include weblogs, blogs, newsgroup articles, email, forums, news sources, social networking sites or social media networks, collaborative wikis, micro blogging services, instant messaging services, SMS messages, and the like. Individually, each of such items may be referred to as a data object);
- “calculating an influence of a target user in the subset on the basis of a relationship between the target user included in the subset and another user included in the subset”
(Kim ¶ [0061] Most past approaches determining influencers focused on easily calculable metrics such as the number of followers or friends, or number of posts. While the aggregated followers or friends count approximate the overall social network, it provides little data in computing metrics that indicate the influence of a user or individual with respect to a company or brand leading to noisy influencer results and wasted time sifting through the massive volume of potential users. ¶ [0067] The proposed systems and methods dynamically calculate influencers with respect to query topic and account for the influence of their followers. For example, per
Kim ¶ [0188] At block 1603, the server calculate scoring for each of nodes (influencers) and edges according to a pre-defined degree of interconnectedness (resolution). That is, in one example, each user handle is assigned a Modularity class identifier (Mod ID) and a PageRank score (defining a degree of influence). In one aspect, the resolution parameter control the density and the number of communities identified. In a preferred aspect, a default resolution value of 2 which provides 2 to 10 communities is utilized by the server. In another aspect, the resolution value is user defined (via computing device 101 in Fig.2) to generate higher or lower granularity of communities as desired for visualization of the community information. ¶ [0071] In an example aspect of determining influencers, consider the simplified follower network for a particular topic in Fig.1. Each user, actually a user account or a username associated with a user account or user data address, is shown in relationship to the other users. The lines between the users, also called edges, represent relationships between the users. For example, an arrow pointing from user account Dave to the user account Carol means Dave reads messages published by Carol. In other words, Dave follows Carol. A bi-directional arrow between Amy and Brian means, Amy follows Dave and Dave follows Amy. Beside each user account in Fig.1, a PageRank score is provided. The PageRank algorithm is a known algorithm used by Google to measure the importance of website pages in a network and can be also applied to measuring the importance of users in a social data network. ¶ [0072] Continuing with Fig.1, Amy has greatest number of followers (i.e. Dave, Carol, and Eddie) and is most influential user in this network (i.e. PageRank score of 46.1%). Yet, Brian, with only 1 follower (i.e. Amy), is more influential than Carol with 2 followers (i.e. Eddie and Dave), primarily because Brian has significant portion of Amy’s mindshare. In other words, using the proposed systems and methods herein, although Carol has more followers than Brian, she does not necessarily have greater influence than Brian. Hence, using the proposed systems and methods herein, the number of followers of a user is not the sole determination for influence. In an example embodiment, identifying who are the followers of a user may also be factored into the computation of influence. Further see Table 1 at ¶ [0073] below
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Kim ¶ [0074] Amy is clearly the top influencer with the greatest number of followers and highest PageRank score. Although Carol has 2 followers, she has a lower PageRank metric than Brian who has 1 follower. Yet, Brian's one follower is the most-influential Amy (with 4 followers), while Carol's two followers are low influencers with (0 followers each). The intuition is that, if a few experts consider someone an expert, then s/he is also an expert. However, the PageRank algorithm gives a better measure of influence than only counting the number of followers. As will be described below, the PageRank algorithm and other similar ranking algorithms can be used with the proposed systems and methods described herein. Another example at ¶ [0252]);
“” (Kim ¶ [0199] 2nd sentence: Fig.17D shows… analysis for community detection (Modularity), and influence (using PageRank) [interpreted as benchmark or threshold]. For PageRank definition see Kim ¶ [0071] last sentence, ¶ [0249] 2nd sentence. Kim ¶ [0074] noting an example where: Amy is clearly the top influencer with the greatest number of followers and highest PageRank [benchmark or trheshold] score. Although Carol has 2 followers, she has lower PageRank metric than Brian who has one follower. However, Brian's one follower is the most-influential Amy (with 4 followers), while Carol's 4 followers are low influencers with (0 followers each). The intuition is that, if a few experts consider someone an expert, then s/he is also an expert. However, the PageRank algorithm gives a better measure of influence than only counting the number of followers. As will be described below, the PageRank algorithm and other similar ranking algorithms can be used with the proposed systems and methods described herein. ¶ [0099] last sentence: the display of the listing of users in the community is ranked [or compared] according to degree of influence within the community and/or within all communities [as another example of benchmark] for topic T (e.g. as provided to display screen 125 of Fig.1). In accordance with block 308, users UT are then split up into their community graph classifications such as UC1, UC2,… UCn. ¶ [0141], ¶ [0146], ¶ [0167] 2nd-3rd sentences, ¶ [0170], ¶ [0171], ¶ [0179]- ¶ [0181], ¶ [0188], ¶[0192], ¶ [0194] 5th sentence, ¶ [0268] last sentence, ¶ [0305] last sentence) “and”
- “causing, on a display device, display of the influence of the target user based on the calculation result” (Kim teaches several examples: ¶ [0073]- ¶ [0074] Fig.16 steps 1603->1604->1605->1606, ¶ [0188] 1st sentence: At block 1603, the server calculate scoring for each of nodes (e.g. influencers) and edges according to the pre-defined degree of interconnectedness.
Kim ¶ [0191] At block 1606, the server visually depict and differentiate each community cluster (by color coding or other visual identification to differentiate one community from another). In a further aspect, at 1606, the server provide a set of top influencers in each of the communities visually linked to the respective community. In yet a further aspect, the server at block 1606, vary the size of each node of the community graph to correspond to the score of the respective influencer (score of influence). As output from block 1606, the edges from the nodes show connections between each users, within their community and across other communities. Further
Kim ¶ [0194] Referring to Figs.17A-D shown are screen shots as provided from GUI module [or display device] 106 of the server and output to display screen 125 of computing device (Fig.2) for visualization of community clusters from a topic network and visualization of popular characteristics in each community. As shown in Figs.17A-D, the server provides interactive interface for selecting communities and/or nodes within the topic network/particular community for visually revealing details about each node (e.g. user, degree of influence). Thus, Figs.17A-D illustrate the interactive visualization of Influencer Communities and their characteristic (e.g. conversations for each community in WordCloud visualization technique). As also in Figs.17A-D, each community (e.g. consisting of edges and nodes) is visually differentiated from other community (by color coding) and each node is sized according to degree of influence within the entire topic network. The degree of influence of a user, corresponds to the ranking of a user account within a community or the entire topic network. Further, by selecting a particular community (e.g. visual selection using a mouse or pointer of community from the topic network), the community values are then depicted (highlighting the community within the topic network graph, revealing the top influencers within the community, and revealing popular characteristic values for top topics of conversation for the selected community). In Figs.17A-17D, the visualization of popular characteristic values on the display screen (e.g. screen of computing device 101 in Fig.2) is shown as a word cloud which depicts top conversation topics within the selected community as well as indication of frequency of use of each topic within all users of the community. ¶ [0099] last 3 sentences: In one aspect, the community graph displays both a visual representation of the users in community (e.g. as nodes) with the community graph and a textual listing of the users in the community (e.g. as provided to display screen 125 of Fig.1). In yet a further aspect, the display of the listing of users in the community is ranked according to degree of influence within the community and/or within all communities for topic T (e.g as provided to display screen 125 of Fig.1). In accordance with block 308, users UT are then split up into their community graph classifications such as UC1, UC2, … UCn. Similarly, ¶ [0268] last sentence).
Kim does not explicitly recite to clearly anticipate “wherein the calculating further comprises calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user” as claimed.
Lozano however in analogues art of calculating influence teaches or suggests: “wherein the calculating further comprises calculating the influence obtained by eliminating or reducing an influence from the other user having a bidirectional link with respect to the target user”
(Lozano ¶ [0065]: “Granger Causality: A collection of bloggers is said to influence Blogger Bi if their collective past content (e.g. blog posts), together with the past content of Blogger Bi, is predictive of the future content of Blogger Bi, more so than” [thus compared against] “the past content of Blogger Bi alone” [as a threshold]. see Lozano ¶ [0066] for additional details. Also,
Lozano ¶ [0080] A natural extension of the Granger-causality test would be to assert that blogger Bi influences blogger Bj if the past content of Bi is significantly helpful if predicting the future content of Bj, compared to [threshold of] using the past content of Bj alone. More formally, the multivariate regression for Bjt in terms of Bjt-1,…,Bjt-d and Bit-1,…,Bi t-d will be compared to the multivariate regression [or threshold] for Bjt in terms of Bj t-1…Bj t-d alone. Similarly,
Lozano ¶ [0091] 6th sentence: GrangerRank is compared against the citation count to measure how well GrangerRank aligns with the more common measure in this domain. Also, Lozano ¶ [0099] In this domain, number of citations is commonly viewed as a valid measure of authority given disciplined scholarly practice of citing prior related work. Thus, citation-count based ranking is considered as the standard for comparison of new techniques. Table 2 shows that GrangerPageRank and GrangerOutDegree have high positive rank correlation with citation counts. This experiment confirms that the present embodiment is able to identify key influencers in this domain and is in agreement with how this community recognizes its authorities).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Kim’s “apparatus /method/non-transitory medium” to includ Lozano’s teachings to have provided a more rigorous algorithm to better assess the influence among users based on a joint consideration of all bloggers into a joint model, rather than performing isolated pair-wise tests between pairs of users disregarding the presence and impact of other users (Lozano [0045], [0063], [0083] in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kim ¶ [0056], ¶ [0528] in view of Lozano ¶ [0067], ¶ [0105].
Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor that deals with calculating influence. In such combination each element merely would have performed the same analytical and econometric function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given existing technical ability to combine the elements as evidenced by Kim in view of Lozano, the to be combined elements would have fitted together, like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus, it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
Lozano also, independently from the primary reference, teaches or at least suggests:
- “determine a magnitude relation of numeral values representing influence which is compared against a threshold” (Lozano ¶ [0065]: “Granger Causality: A collection of bloggers is said to influence Blogger Bi if their collective past content (e.g. blog posts), together with the past content of Blogger Bi, is predictive of the future content of Blogger Bi, more so than” [thus compared against] “the past content of Blogger Bi alone” [as a threshold]. see Lozano ¶ [0066] for additional details. Also, ¶ [0080] A natural extension of the Granger-causality test would be to assert that blogger Bi influences blogger Bj if the past content of Bi is significantly helpful if predicting the future content of Bj, compared to [threshold of] using the past content of Bj alone. More formally, the multivariate regression for Bjt in terms of Bjt-1,…,Bjt-d and Bit-1,…,Bi t-d will be compared to the multivariate regression [or threshold] for Bjt in terms of Bj t-1…Bj t-d alone. Similarly,
Lozano ¶ [0091] 6th sentence: GrangerRank is compared against the citation count to measure how well GrangerRank aligns with the more common measure in this domain. Also, Lozano ¶ [0099] In this domain, number of citations is commonly viewed as a valid measure of authority given disciplined scholarly practice of citing prior related work. Thus, citation-count based ranking is considered as the standard for comparison of new techniques. Table 2 shows that GrangerPageRank and GrangerOutDegree have high positive rank correlation with citation counts. This experiment confirms that the present embodiment is able to identify key influencers in this domain, and is in agreement with how this community recognizes its authorities)
Claims 3,12,18 Kim / Lozano teaches all the limitations in claims 1,6,7 above.
Kim ¶ [0071] 4th-6th sentences recites: For example, an arrow pointing from the user account Dave to the user account Carol means Dave reads messages published by Carol. In other words, Dave follows Carol. A bi-directional arrow between Amy and Brian means, for example, Amy follows Dave and Dave follows Amy.
Kim however does not explicitly recite to clearly anticipate: “wherein the relationship indicates a frequency of interaction performed by the other user with respect to a message transmitted by the target user, and the calculating further comprises
- calculating the influence obtained by reducing the influence from the other user in accordance with the frequency of interaction” as claimed..
Lozano however in analogues art of calculating influence teaches or suggests: “wherein the relationship indicates a frequency of interaction performed by the other user with respect to a message transmitted by the target user, and the calculating further comprises
- calculating the influence obtained by reducing the influence from the other user in accordance with the frequency of interaction” (Lozano ¶ [0063] The term simultaneous then refers to the concept that if blogger A influences another blogger B, all content of A should be predictive of all content of B, though filtering may be used to eliminate overly frequent words. The decision of whether a blogger A is influencing another blogger B is thus based on examining the relevance of all the content of A on all the words of B, "simultaneously"! at once. ¶ [0083] The use of such variable selection methods should be guided by the fact that it would be more desirable to know whether past content of blogger B, as a whole is predictive of the future content of blogger rather than whether the frequency for a certain word for Bi is predictive, with a certain time lag, of frequency of a certain word for Bj. Hence the past word frequencies of a given blogger should be treated as an input group, and the selection process should be done with respect to variable groups, i.e., {B1t-l}l=1 d, {B2t-1}l=1d, …, {BGt-1}l=1d rather than the individual variables, i.e., {wik,t-1, k ∈ {1,…, K}, l ∈ {1,…d}, i ∈ {1,…, G}}. This is done within the procedure denoted MG-OMP (namely in the VARSEL subprocedure), which is an iterative procedure including variables one block at a time, rather than one single variable at a time. In addition, it is reasonable to assume that if blogger Bi influences a blogger Bj, the impact of Bi should be felt throughout the entire content of Bj. Hence the decision of whether or not to select Bi's past content as predictor for Bj's future content, should be made simultaneously across the K regressions for the word frequencies of in the multivariate regression. That is, the input variable group corresponding to Bj will be either simultaneously included in the K models forming the multivariate regression for Bj (albeit with different regression coefficients for each model), or excluded from all of them, and in that sense the target variables in regressions for Bj will be treated as an output group. Additional details at ¶ [0093]).
Rationales to have modified/combined Kim / Lozano are above and reincorporated.
Claims 4,13,19 Kim / Lozano teaches all the limitations in claims 3,12,18 above.
Kim does not explicitly recite:
- “calculating the influence obtained by reducing the influence from the other user in accordance with the frequency of interaction and a number of users for whom there are one-way or two-way links with the other user” as claimed.
Lozano however in analogues art of calculating influence teaches or suggests:
- “calculating the influence obtained by reducing the influence from the other user in accordance with the frequency of interaction and a number of users for whom there are one-way or two-way links with the other user” (Lozano ¶ [0045] The term jointly means that the influence of all the bloggers on each other [as example of two-way influence] is assessed based on a joint consideration of all bloggers into a joint model, rather than performing isolated pair-wise tests between pairs of bloggers disregarding the presence and impact of other bloggers. As described below with respect to Fig.4, this joint consideration of all bloggers involve a loop over all the bloggers, where at each iteration one focuses on a specific blogger and the influence of all bloggers is jointly assessed on the specific blogger under consideration, rather than literal joint processing. ¶ [0063] The term simultaneous then refers to the concept that if blogger A influences another blogger B, all content of A should be predictive of all content of B, though filtering may be used to eliminate overly frequent words. The decision of whether a blogger A is influencing another blogger B is thus based on examining the relevance of all the content of A on all the words of B, "simultaneously"! at once. ¶ [0083] use of such variable selection methods should be guided by the fact that it would be more desirable to know whether past content of blogger B, as a whole is predictive of the future content of blogger rather than whether the frequency for a certain word for Bi is predictive, with a certain time lag, of frequency of a certain word for Bj. Hence the past word frequencies of a given blogger should be treated as input group, and the selection process should be done with respect to variable groups, i.e., {B1t-l}l=1 d, {B2t-1}l=1d, …, {BGt-1}l=1d rather than the individual variables, i.e., {wik,t-1, k ∈ {1,…, K}, l ∈ {1,…d}, i ∈ {1,…, G}}. This is done within the procedure denoted MG-OMP (namely in the VARSEL subprocedure), which is an iterative procedure including variables one block at a time, rather than one single variable at a time. In addition, it is reasonable to assume that if blogger Bi influences a blogger Bj, the impact of Bi should be felt throughout the entire content of Bj. Hence the decision of whether or not to select Bi's past content as predictor for Bj's future content, should be made simultaneously across the K regressions for the word frequencies of in the multivariate regression. That is, the input variable group corresponding to Bj will be either simultaneously included in the K models forming the multivariate regression for Bj (albeit with different regression coefficients for each model), or excluded from all of them, and in that sense the target variables in regressions for Bj will be treated as an output group. Additional details at ¶ [0093]).
Rationales to have modified/combined Kim / Lozano are above and reincorporated.
Claims 5,14 Kim/Lozano teaches all the limitations in claims 1,6 above.
Kim further teaches “wherein the causing display further comprises causing display in either the influence calculation apparatus or another apparatus connected to the influence calculation apparatus via a communication network” (Kim ¶ [0188] 1st sentence: at 1603, server calculate scoring for each of nodes (influencers) and edges according to pre-defined degree of interconnectedness. Further at ¶[0194] referring to Figs.17A-D screen shots as provided from GUI module 106 of the server [supra] and output to display screen 125 of computing device (Fig.2) for visualization of community clusters from a topic network and visualization of popular characteristics in each community. As shown in Figs.17A-D, server provides interactive interface for selecting communities and/or nodes within the topic network/particular community for visually revealing details about each node (user, degree of influence). Thus, Figs.17A-D illustrate interactive visualization of Influencer Communities and their characteristic (e.g. conversations for each community in WordCloud visualization technique). As also in Figs.17A-D, each community (e.g. consisting of edges and nodes) is visually differentiated from other community (by color coding) and each node is sized according to degree of influence within entire topic network. The degree of influence of a user, corresponds to the ranking of a user account within a community or entire topic network. Further, by selecting a particular community (e.g. visual selection using a mouse or pointer of community from the topic network), the community values are then depicted (highlighting community within the topic network graph, revealing the top influencers within the community, and revealing popular characteristic values for top topics of conversation for the selected community). In Figs.17A-17D, the visualization of popular characteristic values on the display screen (e.g. screen of computing device 101 in Fig.2) is shown as a word cloud which depicts top conversation topics within the selected community as well as indication of frequency of use of each topic within all users of the community. ¶ [0099] last 3 sentences: In one aspect, the community graph further displays both a visual representation of the users in the community (e.g. as nodes) with the community graph and a textual listing of the users in the community (e.g. provided to display screen 125 of Fig.1). In yet a further aspect, the display of the listing of users in the community is ranked according to degree of influence within the community and/or within all communities for topic T (e.g as provided to display screen 125 of Fig.1). In accordance with block 308, users UT are then split up into their community graph classifications such as UC1,UC2, … UCn. Similarly, ¶ [0268] last sentence).
Claims 8, 15 Kim / Lozano teaches all the limitations in claims 1,6 above. Furthermore,
Kim teaches “further comprising”: / “the processor is configured to further execute”
- “retrieving a plurality of posting data of the target user over a network” (Kim ¶ [0109] 1st-2nd sentences: Fig.5 shows obtaining social network data. The data may be received as a stream of data, including messages and meta data, in real time); “and”
- “storing the plurality of posting data of the target user in a posting database” (Kim ¶ [0109] 1st-3rd sentences: Turning to Fig.5, an example s for obtaining social network data. The data may be received as a stream of data, including messages and meta data, in real time. This data is stored in data store 116, using a compressed row format (block 501), wherein the calculating further comprises:
= “retrieving the posting data from the posting database” (Kim ¶ [0111] 2nd,5th-6th sentences: the indexer process is a separate process from the storage process that includes scanning the messages as they materialize in the data store 116. The indexer process is, a multi-threaded process that materializes a table of indexed data for each day, or for some other given time period. The indexed data is outputted and stored in index store 117 (block 504). ¶ [0112] 1st sentence: Turning to Fig.6, which shows an example index store 117, each row in the table is a unique user account identifier and a corresponding list of all message identifiers that are produced that day, or that given time period. In an example millions of rows of data can be read and written in the index store 117 each day, and this process occur as new data is materialized or added to the data store 116. ¶ [0253] At block 2201, server 100 obtains a topic represented as T. For example, a user may enter in a topic via a GUI displayed at the computing device 101, and computing device 101 sends the topic to server 100. At block 202, the server uses the topic to identify all posts related to the topic. These set of posts are collectively denoted as PT. In an example embodiment, additional search criteria are used, such as a specified time period. In other words, the server may only be examining posts related to the topic within a given period of time. Finding posts related to a certain topic can be implemented in various ways and will be discussed in further detail below), “and”
= “calculating, based the plurality of posting data in the posting database, the influence of the target user in the subset” (Kim ¶ [0253] Turning to Fig.22, an example shown for determining one or more influencers of a given topic. The process in Fig.22 assumes that social network data is available to server 100, and the social network data includes multiple users. At block 2201, server 100 obtains a topic represented as T. For example, a user enter in a topic via GUI displayed at device 101, and device 101 sends the topic to server 100. At block 202, the server uses the topic to identify all posts related to the topic. These set of posts are collectively denoted as PT. In an example embodiment, additional search criteria are used, such as a specified time period. In other words, the server may only be examining posts related to the topic within a given period of time. Finding posts related to a certain topic can be implemented in various ways and will be discussed below. ¶ [0254] 1st-4th sentences: Continuing with Fig.2, the server obtains authors of the posts PT and identifies the top N authors based on rank (block 2203). The set of top ranked authors is represented by AT. In an example embodiment, the top N authors are identified using the Authority Score. Other methods and processes may be used to rank the authors. For example, the server uses PageRank to measure importance of a user within the topic network and to rank the user based on the measure. ¶ [0256] At block 2204, the server characterizes each of the posts PT as a `Reply`, `Mention`, or `Re-Post`, and respectively identifies the user being replied to, the user being mentioned, and the user who originated the content that was re-posted (e.g. grouped as replied to users UR, mentioned users UM, and re-posted content from users URP). The time stamp of each reply, mention, re-post, etc. may also be recorded to determine whether an interaction between users is recent, or to determine a `recent` grading. ¶ [0245] As an example, consider the simplified follower network for a particular topic in Fig.21. Fig. 21 depicts a social network with several kinds of links: a follower-following relationship; a re-post relationship, and another is reply relationship. The mention relationship is applicable, although not shown in Fig.21. It is shown that Ray is fairly influential since he has largest number of followers in the network. However, Rick and Brie also have significant influence as Ray follows them both. Between Rick and Brie, Rick is likely a stronger influencer since Ray has also re-posted and replied to Rick's posts (e.g. tweets or messages). In the given network, the influencers are likely Rick and Ray. ¶ [0268] last sentence: In yet a further aspect, the display of the listing of users in the community is ranked according to degree of influence within the community and/or within all communities for topic T (e.g as provided to display screen 125 of Fig.1). In accordance with block 2212, users UT are then split up into their community graph classifications such as UC1, UC2,… UCn).
Claim 10 Kim / Lozano teaches all the limitations in claim [?] 1 [?] above.
Kim further teaches “the causing display further comprises causing display in either the influence calculation apparatus or another apparatus connected to the influence calculation apparatus via a communication network” (Kim Fig.16 steps 1603->1604->1605->1606, ¶ [0188] 1st sentence: At block 1603, the server calculate scoring for each of nodes (influencers) and edges according to the pre-defined degree of interconnectedness. Further at ¶ [0194] Referring to Figs.17A-D show screen shots as provided from GUI module 106 of the server and output to display screen 125 of computing device (Fig.2) for visualization of community clusters from a topic network and visualization of popular characteristics in each community. As shown in Figs.17A-D, the server provides interactive interface for selecting communities and/or nodes within the topic network/particular community for visually revealing details about each node (e.g. user, degree of influence). Accordingly, Figs.17A-D illustrate the interactive visualization of the Influencer Communities and their characteristic (e.g. conversations for each community in a WordCloud visualization technique). As also in Figs.17A-D, each community (e.g. consisting of edges and nodes) is visually differentiated from other community (by color coding) and each node is sized according to degree of influence within the entire topic network. The degree of influence of a user, corresponds to the ranking of a user account within a community or the entire topic network. Further, by selecting a particular community (e.g. visual selection using a mouse or pointer of community from the topic network), the community values are then depicted (highlighting the community within the topic network graph, revealing the top influencers within the community, and revealing popular characteristic values for top topics of conversation for the selected community). In Figs.17A-17D, the visualization of the popular characteristic values on the display screen (e.g. screen of computing device 101 in Fig.2) is shown as a word cloud which depicts top conversation topics within the selected community as well as an indication of frequency of use of each topic within all users of the particular community. ¶ [0099] last 3 sentences: In one aspect, the community graph further displays both a visual representation of the users in the community (e.g. as nodes) with the community graph and a textual listing of the users in the community (e.g. as provided to display screen 125 of Fig.1). In yet a further aspect, the display of the listing of users in the community is ranked according to degree of influence within the community and/or within all communities for topic T (e.g as provided to display screen 125 of Fig.1). In accordance with block 308, users UT are then split up into their community graph classifications such as UC1, UC2, … UCn. Similarly, ¶ [0268] last sentence).
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Claims 9,16,20 are rejected under 35 U.S.C. 103 as being unpatentable over:
Kim / Lozano as applied to claims 8,15,7, in view of
Longo et al, US 20170214752 A1 hereinafter Longo. As per,
Claim 9,16,20 Kim / Lozano teaches all the limitations in claim 8,15,7 above. Furthermore,
Kim teaches “further comprising”:
- “retrieving a plurality of posting data of the target user over a network” (Claim 20 only) (Kim ¶ [0109] 1st-2nd sentences: Fig.5 shows obtaining social network data. The data may be received as a stream of data, including messages and meta data, in real time); “and”
- “storing the plurality of posting data of the target user in a posting database” (Claim 20 only) (Kim ¶ [0109] 1st-3rd sentences: Turning to Fig.5, an example obtaining social network data. The data may be received as data stream including messages and meta data in real time. This data is stored in data store 116, using a compressed row format (block 501), wherein the calculating further comprises:
= “retrieving the posting data from the posting database” (Claim 20 only)
(Kim ¶ [0111] 2nd,5th-6th sentences: the indexer process is a separate process from the storage process that includes scanning the messages as they materialize in the data store 116. The indexer process is, a multi-threaded process that materializes a table of indexed data for each day, or for some other given time period. The indexed data is outputted and stored in index store 117 (block 504). ¶ [0112] 1st sentence: Turning to Fig.6, which shows an example index store 117, each row in the table is a unique user account identifier and a corresponding list of all message identifiers that are produced that day, or that given time period. In an example millions of rows of data can be read and written in the index store 117 each day, and this process occur as new data is materialized or added to the data store 116. ¶ [0253] At block 2201, server 100 obtains a topic represented as T. For example, a user may enter in a topic via a GUI displayed at the computing device 101, and computing device 101 sends the topic to server 100. At block 202, the server uses the topic to identify all posts related to the topic. These set of posts are collectively denoted as PT. In an example embodiment, additional search criteria are used, such as a specified time period. In other words, the server may only be examining posts related to the topic within a given period of time. Finding posts related to a certain topic can be implemented in various ways and will be discussed in further detail below), “and”
= “calculating, based the plurality of posting data in the posting database, the influence of the target user in the subset” (Claim 20 only) (Kim ¶ [0253] Turning to Fig.22, an example shown for determining one or more influencers of a given topic. The process in Fig.22 assumes that social network data is available to server 100, and the social network data includes multiple users. At block 2201, server 100 obtains a topic represented as T. For example, a user enter in a topic via GUI displayed at device 101, and device 101 sends the topic to server 100. At block 202, the server uses the topic to identify all posts related to the topic. These set of posts are collectively denoted as PT. In an example embodiment, additional search criteria are used, such as a specified time period. In other words, the server may only be examining posts related to the topic within a given period of time. Finding posts related to a certain topic can be implemented in various ways and will be discussed below. ¶ [0254] 1st-4th sentences: Continuing with Fig.2, the server obtains authors of the posts PT and identifies the top N authors based on rank (block 2203). The set of top ranked authors is represented by AT. In an example embodiment, the top N authors are identified using the Authority Score. Other methods and processes may be used to rank the authors. For example, the server uses PageRank to measure importance of a user within the topic network and to rank the user based on the measure. ¶ [0256] At block 2204, the server characterizes each of the posts PT as a `Reply`, `Mention`, or `Re-Post`, and respectively identifies the user being replied to, the user being mentioned, and the user who originated the content that was re-posted (e.g. grouped as replied to users UR, mentioned users UM, and re-posted content from users URP). The time stamp of each reply, mention, re-post, etc. may also be recorded to determine whether an interaction between users is recent, or to determine a `recent` grading. ¶ [0245] As an example, consider the simplified follower network for a particular topic in Fig.21. Fig. 21 depicts a social network with several kinds of links: a follower-following relationship; a re-post relationship, and another is reply relationship. The mention relationship is applicable, although not shown in Fig.21. It is shown that Ray is fairly influential since he has largest number of followers in the network. However, Rick and Brie also have significant influence as Ray follows them both. Between Rick and Brie, Rick is likely a stronger influencer since Ray has also re-posted and replied to Rick's posts (e.g. tweets or messages). In the given network, the influencers are likely Rick and Ray. ¶ [0268] last sentence: In yet a further aspect, the display of the listing of users in the community is ranked according to degree of influence within the community and/or within all communities for topic T (e.g as provided to display screen 125 of Fig.1). In accordance with block 2212, users UT are then split up into their community graph classifications such as UC1, UC2,… UCn).
Kim / Lozano does not explicitly teach:
- “the posting database includes a plurality of sets of a user name of the target user and a piece of posting data of the target user, and the piece of posting data comprises an identifier of the piece of posting data, a posting date and time” (Claims 9,16, 29).
Longo however in analogues art of providing audience influencer information and identifying at least one influencer for an audience within an identified geographic location teaches or suggests:
- “the posting database includes a plurality of sets of a user name of the target user and a piece of posting data of the target user, and the piece of posting data comprises an identifier of the piece of posting data, a posting date and time” (Claims 9,16, 29) (Longo ¶ [0029] 2nd-3rd sentences: sources of content permit an author to generate individual media posts including text, image content, hyperlinks, audio files, hashtags, likes, dislikes, @mentions, image content, tagged venues/places, and author info including: age, sex, topic interests, and domicile. A media post includes a tweet having an avatar, username, author's name, number of likes, the date of the media post. Similarly, ¶ [0115] 6th sentence: the user interface shows a first card as including an image, volume of posts from the author associated with the identified geographic location, an indication of the time the media post was created, an avatar of the author, a username of the author, and the author's follower count), “a number of impressions in a group” (Claims 9,16, 29) (Longo ¶ [0045] 5th sentence: how many impressions the author has. ¶ [0046] 2nd sentence: how many impressions the influencer has), “a number of interactions of followers in the group” (Claims 9,16,29) (Longo ¶ [0084] 6th-7th, 9th sentences: components of the service 101 compare following lists for each author within the group. Following lists may be received from one or more social media platforms at which the authors maintain a profile, for example through an API. Often, determining which individual, character, group, organization, business, or any other entity that each author of the audience is following includes identifying an author profile associated with that entity. ¶ [0085] At decision block 504, components of service 101, such as influencer component 136 compare those individuals, characters, groups, organizations, businesses, or any other entities, that each author of the audience is following. As mentioned above, this may include comparing one or more following lists. Accordingly, in several embodiments the influencer component determine author profiles commonly followed by authors within the audience (i.e., shared followings). Shared followings refer to an author profile which at least two other authors within the audience are following. While in some embodiments, those shared followings may be associated with an influencer located within the geographic location, more often than not, the identified influencer will not be located within the geographic location. Various aspects and embodiments appreciate that those individuals, characters, groups, organizations, businesses, or any other entity that influences audience, need not necessarily be located within that geographic location. ¶ [0088] 7th sentence: It is appreciated that potential influencers having a follower count between 1000 and 1000000 typically have a dedicated group of followers, without already having been targeted for sponsorships, endorsements, and other targeted advertising), “and a number of interactions of followers outside the group” (Claims 9,16, 29) (Longo ¶ [0120] last sentence: Fig.11 shows one embodiment of the user interface as providing… a followers filter. For example, at ¶ [0085] 5th-6th sentences: While in some embodiments, those one or more shared followings may be associated with an influencer located within the geographic location, more often than not, the identified influencer will not be located within the geographic location. Various aspects appreciate that those individuals, characters, groups, organizations, businesses, or any other entity that influences an audience, need not necessarily be located within that geographic location).
It would have been obvious to one skilled in the art, before the effective filling date of the claimed invention, to have modified Kim / Lozano’ “apparatus” / “method” / “computer-readable non-transitory recording medium” to have included Longo’s teachings in order to have allowed the user to have more efficiently located and navigated content related to social media context (Longo ¶ [0030] in view of MPEP 2143 G and/or F). Longo would have also more effectively allowed the author to have been identified across multiple social media platforms, such that social activity of the author would have been more effectively measured in comparison to measuring activity over a single channel (e.g., Twitter). (Longo ¶ [0068] last sentence in view of MPEP 2143 G and/or F), with a further advantage of having identified each potential influencer as an influencer for the audience in situations when only a few potential influencers have been identified (Longo ¶ [0086] last sentence in view of MPEP 2143 G and/or F). Longo would have also allowed for targeted advertisements to have been generated for those authors, thus improving speed, efficiency, and accuracy of known systems (Longo ¶ [0078] last sentence in view of MPEP 2143 G and/or F). The predictability of such modification would have been corroborated by the broad level of skill of one of ordinary skills in the art as articulated by Kim ¶ [0056], ¶ [0528] in view of Lozano ¶ [0067], ¶ [0105] and in further in view of Longo ¶ [0126]. Further, the claimed invention could have also been viewed as a mere combination of old elements in a similar field of endeavor dealing with identifying at least one influencer and providing audience influencer information. In such combination each element would have merely performed the same analytical and econometric function as it did separately. Thus, one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Kim in view of Longo, the to be combined elements would have fitted together like pieces of a puzzle in a logical, complementary, technologically feasible and/or economically desirable manner. Thus. it would have been reasoned that the results of the combination would have been predictable (MPEP 2143 A).
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Conclusion
The following art is made of record and considered pertinent to Applicant's disclosure:
Hemann et al, Defining and Measuring Influence, Radian6, radian6 webpages, January 2011
WO 2017034766 A1 teaching Systems and methods for a social networking platform
US 20120158476 A1 teaching at claim 16. The method of claim 14 further comprising: detecting that a first influencer created a positive review associated with said campaign; and decreasing said influence level of said first influencer.
US 20160364379 A1 [0015] Yet another object of the present invention is to reduce the number of influenced entities regarding to a specific issue and to reduce the spreading of the influence by the influenced entities on the public that is not part in the influenced entities circle.
US 20150095152 A1 mid-[0033] The time decay can serve to reduce the influence score of inactive users by discounting their influence over time by some force r so that the influence of the user in the next day is influence divided by the quantity one minus the quotient of r and 365 (r can be an annualized rate compounded daily)
US 20170064033 A1 [0020] For example, a particular user's overall virality quotient may be presented over time, and may, for example, reflect an increasing or decreasing level of influence (or at least effect in terms of causing activity) on the social networking platform
US 20210119785 A1 [0832] The reputation of a contractor can be decreased at the end of the campaign by the difference between full conversion and actual conversion in order to reflect to future influencers how worthy it can be to work for a campaign for that contractor. This decrease can be performed by decreasing the influence of the contractor on each node in the source seeding by (1-conversation ratio)/(number of outgoing edges from contractor in referral graph).
US 9641556 B1 column 24 lines 19-22: The advocate score may be increased when the social graph metrics indicate a relatively large influence and may be decreased when the social graph metrics indicate a relatively low influence.
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 OCTAVIAN ROTARU whose telephone number is (571)270-7950. The examiner can normally be reached on 571.270.7950 from 9AM to 6PM. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, PATRICIA H MUNSON, can be reached at telephone number (571)270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from Patent Center. Status information for published applications may be obtained from Patent Center. Status information for unpublished applications is available through Patent Center for authorized users only. Should you have questions about access to Patent Center, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). 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) Form at https://www.uspto.gov/patents/uspto-automated- interview-request-air-form.
/Octavian Rotaru/
Primary Examiner, Art Unit 3624
September 24rd, 2025
1 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016);
2 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016)
3 MPEP 2106.04(a): “examiners should identify at least one abstract idea grouping, but preferably identify all groupings to the extent possible”.
4 Electric Power Group v. Alstom, S.A., 830 F.3d 1350, 1353-54, 119 USPQ2d 1739, 1741-42 (Fed. Cir. 2016)
5 Alice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
6 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016),
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
7 FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)
8 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
9 lice Corp. Pty. Ltd. V. CLS Bank Int’l, 573 U.S. 208, 223, 110 USPQ2d 1976, 1983 (2014); Gottschalk v. Benson, 409 U.S. 63, 64, 175 USPQ 673, 674 (1972); Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015);
10 Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1370-71, 115 USPQ2d 1636, 1642 (Fed. Cir. 2015);
11 Affinity Labs v. DirecTV, 838 F.3d 1253, 1262, 120 USPQ2d 1201, 1207 (Fed. Cir. 2016);
TLI Communications LLC v. AV Auto, LLC, 823 F.3d 607, 613, 118 USPQ2d 1744, 1748 (Fed. Cir. 2016),
Intellectual Ventures I LLC v. Capital One Bank (USA), 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1639 (Fed. Cir. 2015)
12 Intellectual Ventures I v. Capital One Bank, 792 F.3d 1363, 1367, 115 USPQ2d 1636, 1640 (Fed. Cir. 2015)
13 Electric Power Group, LLC v. Alstom S.A., 830 F.3d 1350, 1354, 119 USPQ2d 1739, 1742 (Fed. Cir. 2016);
14 OIP Techs., 788 F.3d at 1362-63, 115 USPQ2d at 1092-93
15 Alice Corp. Pty. Ltd. v. CLS Bank Int'l, 573 U.S. 208, 225, 110 USPQ2d 1984 (2014)
Ultramercial, 772 F.3d at 716, 112 USPQ2d at 1755
16 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93
17 Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362
18 Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1331, 115 USPQ2d 1681, 1699 (Fed. Cir. 2015)
19 Flook, 437 U.S. at 594, 198 USPQ2d at 199 (recomputing or readjusting alarm limit values); Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012)
Prosecution Insights