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 .
DETAILED ACTION
Claims 1-20 are presented for examination in this application (17/672814) amended 2025-11-10, claiming priority and having an effective filing date of 2020-02-11.
The Examiner cites particular sections in the references as applied to the claims
below for the convenience of the applicant(s). Although the specified citations are
representative of the teachings in the art and are applied to the specific limitations within
the individual claim, other passages and figures may apply as well. It is respectfully
requested that, in preparing responses, the applicant(s) fully consider the references in
their entirety as potentially teaching all or part of the claimed invention, as well as the
context of the passage as taught by the prior art or disclosed by the Examiner.
Response to Arguments
Applicant’s arguments and remarks filed 2025-11-10 have been fully considered. The arguments and remarks regarding the 35 U.S.C 101 rejections were not found to be persuasive. The arguments and remarks regarding the 35 U.S.C 103 rejections were found to be persuasive; however, the amendments have necessitated a change in the references applied. The 35 U.S.C 103 rejections and 35 U.S.C 101 rejections have been maintained.
35 U.S.C 101
Applicant’s response:
Applicant asserts “claim 1 is not directed to a law of nature, a natural
phenomenon, or an abstract idea under the first prong of Step 2A. Without conceding to the appropriateness of the rejection, claim 1 is amended to recite, in part, wherein the one or more social relationships are represented by values of a data structure that is generated by at least obtaining initial weights based on interactions between the at least two users; and performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights. During the Examiner Interview, Applicant's representative understood Examiner Bracero to indicate that claim 1 is not directed to mental processes. Additionally, Applicant respectfully submits that amended claim 1 is not directed to mathematical concepts … “Applicant respectfully submits that "obtaining initial weights based on interactions between the at least two users" does not involve a mathematical concept and that "performing convex transformations on the initial weights that are standardized to generate the values" at most involves a mathematical concept.”
…
“Applicant respectfully submits that the alleged abstract idea is integrated into a practical application under the second prong of Step 2A. The claim as a whole integrates the alleged exception into a practical application, as evidenced by the Applicant's specification as originally filed ("Specification"). One way to demonstrate such integration is when the claimed invention improves the functioning of a computer or improves another technology or technical field."MPEP § 2106.04(d)(1). The Specification describes difficulties of imitation of behavioral features that makes is harder to identify abnormal users. See e.g., paragraphs [0003]-[0004]. Claim 1, when read in light of the Specification, solves the problem and provides certain advantages such as improving the accuracy of identification because the diffusion-abnormal user has a social relationship with the abnormal user despite having the same features as the normal user. See id., at paragraph [0154]. Accordingly, for at least the foregoing reasons, claim 1 is not directed to a judicial exception to patentability and is therefore patent-eligible under 35 USC § 101.”.
Examiner’s response:
The Examiner respectfully disagrees. While the Examiner does agree that performing convex transformations and obtaining weights are not mental processes, the Examiner finds that performing convex transformations are recitations of mathematical calculations. Additionally, the Examiner finds that the stated improvement from the Specification would need to be applied to an additional element to qualify in potentially becoming patent-eligible.
It is important to note, the judicial exception alone cannot provide the improvement. The improvement can be provided by one or more additional elements. See the discussion of Diamond v. Diehr, 450 U.S. 175, 187 and 191-92, 209 USPQ 1, 10 (1981)) in subsection II, below. In addition, the improvement can be provided by the additional element(s) in combination with the recited judicial exception. In the instant case, as well as in the arguments and remarks, the improvement is stemming from the identifying diffusion-abnormal users from to-be-confirmed users based on relationships and this has been deemed to be a mental process by the Examiner.
Thus, applicant has not shown how any alleged technical improvement is reflected by any particular claim limitations, i.e. how the specific components or steps would realize the benefits of the alleged improvement. It would not be clear to a person having ordinary skill in the art how the particular limitations of the claims would bring about the alleged improvement even when considered in light of the specification.
35 U.S.C 103
Applicant’s response:
Applicant asserts “Gangling and Xiukun, individually or in combination do not disclose at least the claimed "wherein the one or more social relationships are represented by values of a data structure that is generated by at least obtaining initial weights based on interactions between the at least two users; and performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights.”.
Examiner’s response:
Applicant’s arguments with respect to claim(s) 1-20 have been considered but were not all found to be persuasive.
Regarding the arguments and remarks made that Ganglin and Xiukun, individually or in combination do not disclose at least the claimed “wherein the one or more social relationships are represented by values of a data structure that is generated by at least obtaining initial weights based on interactions between the at least two users”, the Examiner respectfully disagrees. Ganglin, at least in paragraph [0010], teaches a data structure generated by initial weights, represented by a social network. In regard to the arguments and remarks made that Ganglin and Xiukun do not teach “performing convex transformations that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights”, the Examiner agrees that they do not teach this limitation, however, a new reference, Acemoglu (“Spread of (mis)information in social networks” hereinafter, Acemoglu) does teach this limitation and the Examiner finds that it would be obvious to one of ordinary skill in the art to combine these references to achieve the desired limitations as claimed as a whole.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C 101 because the claimed invention is directed to an abstract idea without significantly more. The analysis of the claims will follow the 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (January 7, 2019) (“2019 PEG”).
Regarding claim 1 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a method.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
a method for data identification, performed by a computing device, the method comprising — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining a target user set from a plurality, the target user set comprising at least two users having one or more social relationships — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights — this limitation is directed to mathematical calculations (see MPEP (a)(2) I. C.)
determining abnormal users in the target user set based on the default abnormal user — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining a status of the target user set based on the abnormal users in the target user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
identifying a diffusion-abnormal user from to-be-confirmed users based on the one or more social relationships between the abnormal users and the to-be-confirmed users in the target user set based on the status of the target user set being abnormal, wherein the to-be-confirmed users comprise users in the target user set other than the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
obtaining initial weights based on interactions between the at least two users — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
acquiring a default abnormal user — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquiring a default abnormal user” and “obtaining initial weights based on interactions between the at least two users” limitations were found to be an insignificant extra-solution activities in claim 1. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 2:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the acquiring the default abnormal user and the determining the abnormal users in the target user set based on the default abnormal user comprises: matching the users in the target user set with the default abnormal user — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining, as the abnormal users in the target user set, users having a matching ratio reaching a matching threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 3:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
determining an anomaly concentration of the target user set according to the quantity of the abnormal users and the total quantity of the users in the target user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining the status of the target user set as a normal state based on the anomaly concentration being less than a concentration threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining the status of the target user set as abnormal based on the anomaly concentration being greater than or equal to the concentration threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the determining the status of the target user set based on the abnormal users comprises: acquiring a quantity of the abnormal users and acquiring a total quantity of the users in the target user set — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 4:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
determining a first feature distribution of the abnormal users according to the social behavior features in the user social behavior feature set, the first feature distribution representing a quantity of types of the social behavior features possessed by the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining a second feature distribution of the users in the target user set according to the social behavior features in the user social behavior feature set, the second feature distribution representing a quantity of types of the social behavior features possessed by the users in the target user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining a feature distribution difference between the abnormal users and the users in the target user set based on the first feature distribution and the second feature distribution — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining the status of the target user set based on the feature distribution difference between the first feature distribution and the second feature distribution — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the determining the status of the target user set based on the abnormal users comprises: acquiring a user social behavior feature set, the user social behavior feature set comprising social behavior features of each user in a user group — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “wherein the determining the status of the target user set based on the abnormal users comprises: acquiring a user social behavior feature set, the user social behavior feature set comprising social behavior features of each user in a user group” limitation was found to be an insignificant extra-solution activity in claim 4. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding 5:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the determining the status of the target user set based on the feature distribution difference between the first feature distribution and the second feature distribution comprises: determining the status of the target user set as a normal state based on the feature distribution difference being less than a difference threshold and the first feature distribution being less than a distribution threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining the status of the target user set as the normal state based on the feature distribution difference being greater than or equal to the difference threshold and the first feature distribution being greater than or equal to the distribution threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining the status of the target user set as abnormal based on the feature distribution difference being greater than or equal to the difference threshold and the first feature distribution being less than the distribution threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 6:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the determining the target user set from the plurality of users comprises: dividing the plurality of users into at least two user sets based on collected social relationships and social behaviors among the plurality of users, such that a closeness of a social relationship among users in each user set is higher than a closeness of a social relationship among users in a different user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
selecting one of a plurality of user sets as the target user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 7 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the dividing the plurality of users into the plurality of user sets comprises: determining a relationship topology graph based on the one or more social relationships and the social behaviors among the plurality of users, wherein, in the relationship topology graph, each node corresponds to one of the plurality of users, and an edge connecting two nodes indicates that the users corresponding to two nodes have a social relationship — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining a closeness of the one or more social relationships between two users based on the one or more social relationships and the social behaviors among the plurality of users, determining a weight of an edge between nodes corresponding to the two users based on the closeness of the one or more social relationships between the two users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
dividing the relationship topology graph into at least two topology sub-graphs by using a clustering algorithm, and selecting a set of users corresponding to nodes in one of the at least two topology sub-graphs as the target user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 8:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the dividing the relationship topology graph into the at least two topology sub-graphs by using the clustering algorithm comprises: acquiring a sampling path corresponding to a first node from the relationship topology graph based on a quantity of sampling paths — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining a jump probability between the first node and an association node in the sampling path based on an edge weight in the relationship topology graph, the association node being a node in the sampling path other than the first node — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
updating the relationship topology graph based on the jump probability to obtain an updated relationship topology graph, and dividing the updated relationship topology graph to obtain the at least two topology sub- graphs — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 9 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the determining the weight of the edge between the nodes corresponding to the users based on the closeness of the social relationship between the two users comprises: setting the closeness of the one or more social relationships between the two users as an initial weight of the edge between the two nodes corresponding to the two users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
performing probability transformation on the initial weight to obtain an edge weight — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 10:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
selecting, as a connection node pair, two nodes in the first node, the intermediate node, and the association node having an edge, acquiring an edge weight corresponding to the connection node pair — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining the jump probability between the first node — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the determining the jump probability between the first node and the association node in the sampling path based on the edge weight in the relationship topology graph comprises: acquiring an intermediate node between the first node and the association node from the sampling path in a case that there is no edge between the first node and the association node, the first node reaching the association node through the intermediate node — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “wherein the determining the jump probability between the first node and the association node in the sampling path based on the edge weight in the relationship topology graph comprises: acquiring an intermediate node between the first node and the association node from the sampling path in a case that there is no edge between the first node and the association node, the first node reaching the association node through the intermediate node” limitation was found to be an insignificant extra-solution activity in claim 10. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 11:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the updating the relationship topology graph based on the jump probability comprises: updating a connected edge in the relationship topology graph based on the first node and the association node to obtain a transition relationship topology graph, the first node and the association node in the transition relationship topology graph being both connected with edges — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
setting the jump probability between the first node and the association node in the transition relationship topology graph as an edge weight between the first node and the association node to obtain the updated relationship topology graph — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 12:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the dividing the updated relationship topology graph to obtain the at least two topology sub-graphs comprises: performing exponential growth on the jump probability, performing probability transformation on the jump probability obtained after the exponential growth to obtain a target probability — this limitation is directed to mathematical calculations, specifically performing exponential growth and probability transformations, which are considered abstract ideas (see MPEP 2106.04(a)(2) I. C.).
determining, as a vital association node of the first node, the association node having the updated edge weight greater than a weight threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
dividing a target relationship topology graph into the at least two topology sub-graphs based on the first node and the vital association node — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
updating the edge weight between the first node and the association node based on the target probability — the process of classifying and organizing data amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 13 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the identifying the diffusion-abnormal user from the to-be- confirmed users based on the one or more social relationships between the abnormal users and the to-be- confirmed users in the target user set based on the status of the target user set being abnormal comprises: determining users having the one or more social relationships with the abnormal users from the to-be- confirmed users based on the status of the target user set being abnormal — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining, as the diffusion-abnormal user, the user having a social relationship with an abnormal user — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 14 (currently amended):
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the identifying the diffusion-abnormal user from the to-be-confirmed users based on the social relationships between the abnormal users and the to-be-confirmed users in the target user set based on the status of the target user set being abnormal comprises: determining users having the one or more social relationships with the abnormal users from the to-be- confirmed users based on the status of the target user set being abnormal — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining, as a diffusion-abnormal node, an association user node having an edge weight with one of a number of abnormal user nodes greater than an association threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining, as a diffusion-abnormal node, an association user node having an edge with one of a number of abnormal user nodes greater than an association threshold, and determining a user corresponding to the diffusion-abnormal node as the diffusion-abnormal user — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
acquiring abnormal nodes corresponding to the abnormal users, acquiring association user nodes corresponding to the users having the social relationship with the abnormal users — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquiring abnormal nodes corresponding to the abnormal users, acquiring association user nodes corresponding to the users having the social relationship with the abnormal users” limitation was found to be an insignificant extra-solution activity in claim 14. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 15:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 1 which recited an abstract idea. The claim recites additional abstract ideas:
determining the target user set as abnormal as a to-be-identified user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
acquiring sensitive source data — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
matching the key text data with the sensitive source data — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determining an anomaly category of the to-be-identified user set based on a matching result — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
acquiring user text data of users in the to-be-identified user set, and extracting key text data from the user text data — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquiring user text data of users in the to-be-identified user set, and extracting key text data from the user text data” limitation was found to be an insignificant extra-solution activity in claim 15. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 16 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to an apparatus.
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
first determining code configured to cause the at least one processor to determine a target user set from a plurality of users, the target user set comprising at least two users having one or more social relationships — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights — this limitation is directed to mathematical calculations (see MPEP 2106.04(a)(2) I. C.)
second determining code configured to cause the at least one processor to determine a status of the target user set based on the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
first identifying code configured to cause the at least one processor to identify a diffusion- abnormal user from to-be-confirmed users based on social relationships between the abnormal users and the to-be-confirmed users in the target user set based on the status of the target user set being abnormal, wherein the to-be-confirmed users comprise users in the target user set other than the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a data identification apparatus, comprising: at least one memory configured to store computer program code — the process of classifying and organizing data amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
at least one processor configured to access said computer program code and operate as instructed by said computer program code, said computer program code including — the process of classifying and organizing data amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
wherein the one or more social relationships are represented by vales of a data structure that is generated by at least — the process of classifying and organizing data amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
obtaining initial weights based on interactions between the at least two users — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
first acquiring code configured to cause the at least one processor to acquire a default abnormal user and determine abnormal users in the target user set based on the default abnormal user — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
where the to-be-confirmed users comprise users in the target user set other than the abnormal users — the process of classifying and organizing data amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “first acquiring code configured to cause the at least one processor to acquire a default abnormal user and determine abnormal users in the target user set based on the default abnormal user” and “obtaining initial weights based on interactions between the at least two users” limitations were found to be an insignificant extra-solution activities in claim 16. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Regarding claim 17:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 16 which recited an abstract idea. The claim recites additional abstract ideas:
wherein the first acquiring code is further configured to cause the at least one processor to: match the users in the target user set with the default abnormal user — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determine, as the abnormal users in the target user set, users having a matching ratio reaching a matching threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim does not recite additional elements that integrate the judicial exception into a practical application.
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception.
Regarding claim 18:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 16 which recited an abstract idea. The claim recites additional abstract ideas:
determine an anomaly concentration of the target user set according to the quantity of the abnormal users and the total quantity of the users in the target user set — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determine the status of the target user set as a normal state based on the anomaly concentration being less than a concentration threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determine the status of the target user set as abnormal based on the anomaly concentration being greater than or equal to the concentration threshold — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the second determining code is further configured to cause the at least one processor to: acquire a quantity of the abnormal users and acquiring a total quantity of the users in the target user set — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “wherein the second determining code is further configured to cause the at least one processor to: acquire a quantity of the abnormal users and acquiring a total quantity of the users in the target user set” limitation was found to be an insignificant extra-solution activity in claim 18. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding 19:
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim is dependent on claim 16 which recited an abstract idea. The claim recites additional abstract ideas:
determine a first feature distribution of the abnormal users according to the social behavior features in the user social behavior feature set, the first feature distribution representing a quantity of types of the social behavior features possessed by the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determine a feature distribution difference between the abnormal users and the users in the target user set based on the first feature distribution and the second feature distribution — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determine the status of the target user set based on the feature distribution difference between the first feature distribution and the second feature distribution — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
wherein the second determining code is further configured to cause the at least one processor to: acquire a user social behavior feature set, the user social behavior feature set comprising social behavior features of each user in a user group — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “wherein the second determining code is further configured to cause the at least one processor to: acquire a user social behavior feature set, the user social behavior feature set comprising social behavior features of each user in a user group” limitation was found to be an insignificant extra-solution activity in claim 19. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.). MPEP 2106.05(f) cannot integrate the abstract idea into a practical application.
Regarding claim 20 (currently amended):
Step 1 – Is the claim directed to a process, machine, manufacture, or composition of matter?
Yes, the claim is directed to a non-transitory computer-readable storage medium (manufacture).
Step 2A – Prong 1 – Does the claim recite an abstract idea, law of nature, or natural phenomenon?
Yes, the claim recites abstract ideas:
determine a target user set from a plurality of users, the target user set comprising at least two users having one or more social relationships — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights — this limitation is directed to mathematical calculations (see MPEP 2106.04(a)(2) I. C.)
determine a status of the target user set based on the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
identify a diffusion-abnormal user from to-be-confirmed users based on the one or more social relationships between the abnormal users and the to-be-confirmed users in the target user set based on the status of the target user set being abnormal, wherein the to-be-confirmed users comprise users in the target user set other than the abnormal users — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
determine abnormal users in the target users in the target user set based on the default abnormal user — this limitation is directed to the abstract idea of a mental process (including an observation, evaluation, judgement, opinion) which can be performed by the human mind, or by a human using a pen and paper (see MPEP 2106.4(a)(2) III. C.).
Step 2A – Prong 2 – Does the claim recite additional elements that integrate the judicial exception into a practical application?
No, the claim recites additional elements that do not integrate the judicial exception into a practical application:
a non-transitory computer-readable storage medium storing computer instructions that, when executed by at least one processor of a device, cause the at least one processor to — the process of classifying and organizing data amounts to mere instructions to apply an exception, as the use of a computer or other machinery in its ordinary capacity amounts to invoking computer components merely as a tool to perform an existing process (see MPEP 2106.05(f)(2)).
obtaining initial weights based on interactions between the at least two users — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
acquire a default abnormal user — this limitation is directed to mere data gathering and outputting which has been recognized by the courts (as per Ultramercial, 772 F.3d at 715, 112 USPQ2d at 1754) as insignificant extra-solution activity (see MPEP 2106.05(g)).
Step 2B – Does the claim recite additional elements that amount to significantly more than the abstract idea itself?
No, there are no additional elements that amount to significantly more than the judicial exception. Any additional elements that were determined to be insignificant extra-solution activity in step 2A prong 2 are further evaluated in step 2B on whether they are well-understood, routine, and conventional activities. The “acquire a default abnormal user ” and “obtaining initial weights based on interactions between the at least two users” limitations were found to be an insignificant extra-solution activities in claim 20. This limitation is recited at a high level of generality and amounts to transmitting data over a network, which is well-understood, routine, and conventional activity (see MPEP 2106.05(d) II.).
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 4-6, 13, 15, 16-17, and 19-20 are rejected under 35 U.S.C 103 as being unpatentable over Ganglin (CN108615119A, hereinafter Ganglin) in view of Xiukun et al. (TWI804575B, hereinafter Xiukun) in further view of Acemoglu et al. (“Spread of (mis)information in social networks”, hereinafter Acemoglu).
Regarding claim 1:
Ganglin teaches a method for data identification, performed by a computing device, the method comprising (see [0016]: “The implementation of the abnormal user identification method and terminal device provided by the embodiment of the present invention has the following beneficial effects:”),
determining a target user set from a plurality of users, the target user set comprising at least two users having one or more social relationships (see [0037]: “In this embodiment, after determining the user information of each user, the identification device will combine the users in pairs, calculate the matching degree between the two users, compare the matching degree with a preset matching degree threshold, and determine whether the two users are associated users.”),
wherein the one or more social relationships are represented by values of a data structure that is generated by at least (see [0010]: “Based on the associated users of each of the users, a relationship network of the user database is created.”)
obtaining initial weights based on interactions between the at least two users (see [0048]: “Based on the associated users of each of the users, a user relationship network of the user database is created; the user relationship network includes a user node recording an initial value of the credit coefficient of each of the users; the initial value of the credit coefficient of the user is determined by the credit coefficient of the associated users of the user;”. Also, see [0048]: “Where Cdit<sub>0</sub>(user<sub>N</sub>) is the initial value of the credit coefficient of the n-th user; Trade(x<sub>j</sub>) is the contribution of the j-th transaction record of the n-thuser to the credit coefficient; Cdit<sub>0</sub>(user<sub>i</sub>) is the credit coefficient of the i-th associated user of the n-th user, and Cofft<sub>i</sub> is the preset weight of the i-th associated user; M is the number of transactions of the n-th user, and N is the number of users associated with the n-th user.”)
…
acquiring a default abnormal user and determining abnormal users in the target user set based on the default abnormal user (see [0017]: “The embodiment of the present invention obtains user information of each user in a user database”. Also see [0013]: “Users whose adjusted credit coefficient is less than a preset credit threshold are selected as abnormal users.”)
determining a status of the target user set based on the abnormal users in the target user set (see [0017]: “Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”)
Ganglin does not explicitly teach performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights, identifying a diffusion-abnormal user from to-be-confirmed users based on social relationships between the abnormal users or the to-be-confirmed users in the target user set based on the status of the target user set being abnormal, wherein the to-be-confirmed users comprise users in the target user set other than the abnormal users.
Xiukun, however, teaches in analogous identifying a diffusion-abnormal user from (see pg. 3 lines 1-3: “One or more embodiments of this specification describe a method and apparatus that can diffuse identified high-risk users based on a crowd relationship graph, thereby more efficiently and accurately identifying high-risk users.”. Also see pg. 7 lines 3-13 “In response to this incident, the computing platform first determined whether the user was a high-risk user. If so, it "diffused" the risk based on the user's relationship network and discovered other high-risk users. Correspondingly, the computing platform includes an anomaly rule engine and a diffusion engine. The anomaly rule engine makes a preliminary judgment on the user's business application event based on pre-set anomaly rules. In some cases, this can be supplemented by human review. Once the current business application event is determined to be a high-risk event, the current user will also be identified as a high-risk user. Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks.”. Also see )
Xiukun does not explicitly teach to-be-confirmed users. Ganglin, however, does as recited (see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin and Xiukun before him or her, to modify the method of claim 1 to include attributes of using diffusion techniques to identify abnormal users in order to greatly improve identification efficiency (see pg. 7 lines 12-20: “Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks. This is based on observations and statistics: many high-risk events exhibit "group" characteristics, that is, they require the cooperation of multiple people to complete. In this way, if a user is identified as a high-risk user, the user's relationship network can be further analyzed to discover groups with "gang" characteristics, thereby identifying other related high-risk users. In this way, multiple high-risk users can be identified from a single business request event, greatly improving identification efficiency.”.).
Ganglin in view of Xiukun does not teach performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights.
Acemoglu, however, analogously teaches performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights (see pg. 200 section 3: “In this section, we provide our main convergence result. In particular, we show that despite the presence of forceful agents, with potentially very different opinions at the beginning, the society will ultimately converge to a consensus, in which all individuals share the same belief. … Our analysis essentially relies on showing that iterates of Eq. (4), x(k), converge to a consensus with probability one, i.e., x(k) → ¯xe, where ¯x is a scalar random variable that depends on the initial beliefs and the random sequence of matrices {W(k)}, and e is the vector of all ones. … Moreover, the random variable ¯x is a convex combination of initial agent beliefs, i.e.,”. Also see pg. 202 theorem 2: “(b) The expected value of ¯x is given by a convex combination of the initial agent values xi(0), where the weights are given by the components of the probability vector ¯π, i.e.,” )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, and Acemoglu before him or her, to modify the method of claim 1 to include attributes of performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights in order to highlight similar characteristics (see pg. 200 section 3: “In particular, we show that despite the presence of forceful agents, with potentially very different opinions at the beginning, the society will ultimately converge to a consensus, in which all individuals share the same belief.”)
Regarding claim 2:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 1.
Ganglin further teaches wherein the acquiring the default abnormal user and the determining the abnormal users in the target user set based on the default abnormal user comprises: matching the users in the target user set with the default abnormal user (see [0009]: “determining a matching degree between the users based on the user information, and marking two users whose matching degrees are greater than a matching threshold as mutually associated users;”. Also see [00017]: “When financial fraud is carried out in a group manner, the members of the same group are associated with each other. Therefore, when one member of the group implements an illegal transaction, the other members of the group can be immediately identified and identified as abnormal users, which effectively protects the funds of financial institutions and reduces the investment risks of financial institutions.”. Also see [0013]: “Users whose adjusted credit coefficient is less than a preset credit threshold are selected as abnormal users.”.)
determining, as the abnormal users in the target user set, users having a matching ratio reaching a matching threshold (see [0059]: “ When financial fraud is carried out in a group manner, the members of the same group are associated with each other. Therefore, when one member of the group implements an illegal transaction, the other members of the group can be immediately identified and identified as abnormal users, which effectively protects the funds of financial institutions and reduces the investment risks of financial institutions.”. Also see [0039]: “For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified.”.).
Regarding claim 17:
Claim 17 recites analogous limitations to claim 2 and therefore is rejected on the same grounds as claim 2.
Regarding claim 18:
Claim 18 recites analogous limitations to claim 3 and therefore is rejected on the same grounds as claim 3.
Regarding claim 4:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 1.
Ganglin further teaches the determining the status of the target user set based on the abnormal users comprises: acquiring a user social behavior feature set, the user social behavior feature set comprising social behavior features of each user in a user group (see [0092]: “In this embodiment, before determining the credit adjustment coefficient of each transaction behavior record, the identification device will first determine the user's behavioral habits, and therefore extract the user's historical transaction records from the historical transaction information database, and determine the historical behavior feature value of each historical transaction record, and calculate the historical transaction feature value corresponding to the user. Specifically, the historical transaction feature value may be an average of historical behavior feature values based on various historical transaction records.”. Also see [0138]: “Therefore, in the abnormal user identification device provided by the embodiment of the present invention, the credit coefficient of each user is not only related to the transaction behavior record of the user, but also to the credit coefficients of the user's associated users. Financial fraud is carried out in a group manner, and the members of the same group are associated with each other. Therefore, when one member of the group implements an illegal transaction, the other members of the group can be immediately identified and identified as abnormal users, which effectively protects the funds of financial institutions and reduces the investment risks of financial institutions.”.);
determining a first feature distribution of the abnormal users according to the social behavior features in the user social behavior feature set, the first feature distribution representing a quantity of types of the social behavior features possessed by the abnormal users (see [0091]: “In S1041 , the historical transaction records of the user are extracted from a historical transaction information database, and the historical transaction feature value is determined based on the historical transaction records.”. Also see [0096]: “the credit adjustment coefficient is determined according to the behavior characteristic value of the transaction behavior record and the historical transaction characteristic value.”. Also see [0017]: “The initial value of the credit coefficient of each user is determined through the user relationship network. The initial value of the credit coefficient will be related to the credit coefficient of the associated users of the user, that is, the credit coefficients between associated users are mutually influential and not independent. Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”.)
determining a second feature distribution of the users in the target user set according to the social behavior features in the user social behavior feature set, the second feature distribution representing a quantity of types of the social behavior features possessed by the users in the target user set (see [0091]: “In S1041 , the historical transaction records of the user are extracted from a historical transaction information database, and the historical transaction feature value is determined based on the historical transaction records.”. Also see [0096]: “the credit adjustment coefficient is determined according to the behavior characteristic value of the transaction behavior record and the historical transaction characteristic value.”. Also see [0017]: “The initial value of the credit coefficient of each user is determined through the user relationship network. The initial value of the credit coefficient will be related to the credit coefficient of the associated users of the user, that is, the credit coefficients between associated users are mutually influential and not independent. Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”.)
determining a feature distribution difference between the abnormal users and the users in the target user set based on the first feature distribution and the second feature distribution (see [0091]: “In S1041 , the historical transaction records of the user are extracted from a historical transaction information database, and the historical transaction feature value is determined based on the historical transaction records.”. Also see [0096]: “the credit adjustment coefficient is determined according to the behavior characteristic value of the transaction behavior record and the historical transaction characteristic value.”. Also see [0017]: “The initial value of the credit coefficient of each user is determined through the user relationship network. The initial value of the credit coefficient will be related to the credit coefficient of the associated users of the user, that is, the credit coefficients between associated users are mutually influential and not independent. Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”.); and
determining the status of the target user set based on the feature distribution difference between the first feature distribution and the second feature distribution (see [0091]: “In S1041 , the historical transaction records of the user are extracted from a historical transaction information database, and the historical transaction feature value is determined based on the historical transaction records.”. Also see [0096]: “the credit adjustment coefficient is determined according to the behavior characteristic value of the transaction behavior record and the historical transaction characteristic value.”. Also see [0017]: “The initial value of the credit coefficient of each user is determined through the user relationship network. The initial value of the credit coefficient will be related to the credit coefficient of the associated users of the user, that is, the credit coefficients between associated users are mutually influential and not independent. Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”.).
Regarding claim 19:
Claim 19 recites analogous limitations to claim 4 and therefore is rejected on the same grounds as claim 4.
Regarding claim 5:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 4.
Ganglin further teaches wherein the determining the status of the target user set based on the feature distribution difference between the first feature distribution and the second feature distribution comprises: determining the status of the target user set as a normal state based on the feature distribution difference being less than a difference threshold and the first feature distribution being less than a distribution threshold (see [0075]: “In this embodiment, if the value of the risk timer is less than or equal to the risk threshold, it means that the transaction frequency between the risky user and the abnormal user is low, and there is no strong correlation between the risky user and the abnormal user. In this case, the user can be re-identified as a normal user, and the risky user is deleted from the risk database.”.);
determining the status of the target user set as the normal state based on the feature distribution difference being greater than or equal to the difference threshold and the first feature distribution being greater than or equal to the distribution threshold (see [0058]: “In this embodiment, after adjusting the credit coefficients of all users, the identification device will compare each adjusted credit coefficient with the credit threshold to determine whether the user is an abnormal user. If the credit coefficient is less than the credit threshold, the user is identified as an abnormal user. Conversely, if the user's credit coefficient is greater than or equal to the credit threshold, the user is identified as a normal user.”.);
determining the status of the target user set as abnormal based on the feature distribution difference being greater than or equal to the difference threshold and the first feature distribution being less than the distribution threshold (see [0069]: “In particular, if the valid timer of the identification device is less than the valid duration and the value of the risk counter is greater than the preset risk threshold, the risk user can be identified as an abnormal user, so that abnormal users can be discovered in time without waiting for the valid timer to reach the preset valid duration.”.).
Regarding claim 6:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 4.
Ganglin further teaches wherein the determining the target user set from the plurality of users comprises: dividing the plurality of users into at least two user sets based on collected social relationships and social behaviors among the plurality of users such that a closeness of a social relationship among users in each user set is higher than a closeness of a social relationship among users in a different user set, and selecting one of a plurality of user sets as the target user set (see [0017]: “When financial fraud is carried out in a group manner, the members of the same group are associated with each other. Therefore, when one member of the group implements an illegal transaction, the other members of the group can be immediately identified and identified as abnormal users, which effectively protects the funds of financial institutions and reduces the investment risks of financial institutions.”. Also see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified.”.).
Regarding claim 13:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 1.
Ganglin does not explicitly teach wherein the identifying the diffusion-abnormal user from the
Xiukun, however, analogously teaches wherein the identifying the diffusion-abnormal user from the (see pg. 3 lines 1-3: “One or more embodiments of this specification describe a method and apparatus that can diffuse identified high-risk users based on a crowd relationship graph, thereby more efficiently and accurately identifying high-risk users.”. Also see pg. 7 lines 3-13 “In response to this incident, the computing platform first determined whether the user was a high-risk user. If so, it "diffused" the risk based on the user's relationship network and discovered other high-risk users. Correspondingly, the computing platform includes an anomaly rule engine and a diffusion engine. The anomaly rule engine makes a preliminary judgment on the user's business application event based on pre-set anomaly rules. In some cases, this can be supplemented by human review. Once the current business application event is determined to be a high-risk event, the current user will also be identified as a high-risk user. Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks.”.)
determining as the diffusion-abnormal user, the user having a social relationship with an abnormal user (see pg. 22 lines 6-14: “That is, in one embodiment, after the second user is determined, a third user who has a specific relationship with the second user is determined based on the above-mentioned population relationship graph, and the third user is also added to the high-risk user set. The process of determining a third user with a specific association relationship based on the second user is consistent with the process of determining the second user based on the first user described above, and the details are not repeated here. It should be understood that the above diffusion process can be repeated continuously, that is, after determining the third user, the fourth user associated with the third user is found based on the third user, and so on, until no new high-risk users appear.”).
Xiukun does not explicitly teach to-be-confirmed users. Ganglin, however, does as recited (see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, and Acemoglu before him or her, to modify the method of claim 13 to include attributes of using diffusion techniques to identify abnormal users in order to greatly improve identification efficiency (see pg. 7 lines 12-20: “Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks. This is based on observations and statistics: many high-risk events exhibit "group" characteristics, that is, they require the cooperation of multiple people to complete. In this way, if a user is identified as a high-risk user, the user's relationship network can be further analyzed to discover groups with "gang" characteristics, thereby identifying other related high-risk users. In this way, multiple high-risk users can be identified from a single business request event, greatly improving identification efficiency.”.).
Regarding claim 15:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 1.
Ganglin further teaches determining the target user set as abnormal as a to-be-identified user set (see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified.”. Also see [0035]: “For example, when a new user is detected to be entered into the user database, the relevant operations of S101 may be performed to identify whether the user is an abnormal user.”.);
acquiring user text data of users in the to-be-identified user set, and extracting key text data from the user text data (see [0038]: “Optionally, the method for determining the matching degree between two user information may be: importing the user information into a preset keyword dictionary, generating a keyword vector corresponding to the user information, specifically, if the user information contains a keyword in the keyword dictionary, then the value of the element corresponding to the keyword number in the keyword vector is marked as 1; if the keyword is not contained, then the value of the element corresponding to the keyword number is marked as 0. After determining the keyword vectors corresponding to the two user information, the number of identical elements is counted, that is, the number of elements with the same value at the same position in the two vectors. The number of identical elements is used as the matching degree between the above two user information, and the matching degree is compared with the matching degree threshold to determine whether the two users are related users.”.);
acquiring sensitive source data (see [0030]: “The embodiment of the present invention obtains user information of each user in a user database, determines the associated users of each user based on the user information, and constructs a user relationship network. The initial value of the credit coefficient of each user is determined through the user relationship network. The initial value of the credit coefficient will be related to the credit coefficient of the associated users of the user, that is, the credit coefficients of the associated users influence each other and are not independent. Then, each transaction behavior record is identified, a credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users with credit coefficients lower than a preset credit threshold are selected as abnormal users.”.);
matching the key text data with the sensitive source data (see [0038]: “Optionally, the method for determining the matching degree between two user information may be: importing the user information into a preset keyword dictionary, generating a keyword vector corresponding to the user information, specifically, if the user information contains a keyword in the keyword dictionary, then the value of the element corresponding to the keyword number in the keyword vector is marked as 1; if the keyword is not contained, then the value of the element corresponding to the keyword number is marked as 0.
After determining the keyword vectors corresponding to the two user information, the number of identical elements is counted, that is, the number of elements with the same value at the same position in the two vectors. The number of identical elements is used as the matching degree between the above two user information, and the matching degree is compared with the matching degree threshold to determine whether the two users are related users.
”.) and
determining an anomaly category of the to-be-identified user set based on a matching result (see [0052]: “Optionally, the identification device may determine the credit adjustment coefficient corresponding to each transaction behavior record by extracting transaction characteristic values from the transaction behavior record. The transaction characteristic values may be information such as transaction frequency, transaction amount, transaction attributes, and transaction address. It should be noted that the transaction attributes are specifically used to specify whether the transaction is a positive transaction or a negative transaction. A positive transaction is a transaction operation that complies with transaction behavior specifications, such as repayment on time, payment of interest on time, and other transaction operations; while a negative transaction behavior is a transaction operation that violates the transaction behavior rules, such as overdue repayment, user loss of contact, and the like.”. Also see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified.”).
Regarding claim 16:
Ganglin teaches a data identification apparatus, comprising: at least one memory configured to store computer program code; and at least one processor configured to access said computer program code and operate as instructed by said computer program code, said program code including (see [0014]: “A second aspect of an embodiment of the present invention provides an abnormal user identification device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the first aspect when executing the computer program.”):
first determining code configured to cause the at least one processor to determine a target user set from a plurality of users, the target user set comprising at least two users having one or more social relationships (see [0037]: “In this embodiment, after determining the user information of each user, the identification device will combine the users in pairs, calculate the matching degree between the two users, compare the matching degree with a preset matching degree threshold, and determine whether the two users are associated users.”),
wherein the one or more social relationships are represented by values of a data structure that is generated by at least (see [0010]: “Based on the associated users of each of the users, a relationship network of the user database is created.”)
obtaining initial weights based on interactions between the at least two users (see [0048]: “Based on the associated users of each of the users, a user relationship network of the user database is created; the user relationship network includes a user node recording an initial value of the credit coefficient of each of the users; the initial value of the credit coefficient of the user is determined by the credit coefficient of the associated users of the user;”. Also, see [0048]: “Where Cdit<sub>0</sub>(user<sub>N</sub>) is the initial value of the credit coefficient of the n-th user; Trade(x<sub>j</sub>) is the contribution of the j-th transaction record of the n-thuser to the credit coefficient; Cdit<sub>0</sub>(user<sub>i</sub>) is the credit coefficient of the i-th associated user of the n-th user, and Cofft<sub>i</sub> is the preset weight of the i-th associated user; M is the number of transactions of the n-th user, and N is the number of users associated with the n-th user.”)
…
first acquiring code configured to cause the at least one processor to acquire a default abnormal user and determining abnormal users in the target user set based on the default abnormal user (see [0017]: “The embodiment of the present invention obtains user information of each user in a user database”. Also see [0013]: “Users whose adjusted credit coefficient is less than a preset credit threshold are selected as abnormal users.”)
second determining code configured to cause the at least one processor to determine a status of the target user set based on the abnormal users in the target user set (see [0017]: “Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”)
Ganglin does not explicitly teach performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights, identify a diffusion-abnormal user from to-be-confirmed users based on social relationships between the abnormal users or the to-be-confirmed users in the target user set based on the status of the target user set being abnormal, wherein the to-be-confirmed users comprise users in the target user set other than the abnormal users.
Xiukun, however, teaches in analogous identifying a diffusion-abnormal user from (see pg. 3 lines 1-3: “One or more embodiments of this specification describe a method and apparatus that can diffuse identified high-risk users based on a crowd relationship graph, thereby more efficiently and accurately identifying high-risk users.”. Also see pg. 7 lines 3-13 “In response to this incident, the computing platform first determined whether the user was a high-risk user. If so, it "diffused" the risk based on the user's relationship network and discovered other high-risk users. Correspondingly, the computing platform includes an anomaly rule engine and a diffusion engine. The anomaly rule engine makes a preliminary judgment on the user's business application event based on pre-set anomaly rules. In some cases, this can be supplemented by human review. Once the current business application event is determined to be a high-risk event, the current user will also be identified as a high-risk user. Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks.”. Also see )
Xiukun does not explicitly teach to-be-confirmed users. Ganglin, however, does as recited (see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin and Xiukun before him or her, to modify the method of claim 16 to include attributes of using diffusion techniques to identify abnormal users in order to greatly improve identification efficiency (see pg. 7 lines 12-20: “Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks. This is based on observations and statistics: many high-risk events exhibit "group" characteristics, that is, they require the cooperation of multiple people to complete. In this way, if a user is identified as a high-risk user, the user's relationship network can be further analyzed to discover groups with "gang" characteristics, thereby identifying other related high-risk users. In this way, multiple high-risk users can be identified from a single business request event, greatly improving identification efficiency.”.).
Ganglin in view of Xiukun does not teach performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights.
Acemoglu, however, analogously teaches performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights (see pg. 200 section 3: “In this section, we provide our main convergence result. In particular, we show that despite the presence of forceful agents, with potentially very different opinions at the beginning, the society will ultimately converge to a consensus, in which all individuals share the same belief. … Our analysis essentially relies on showing that iterates of Eq. (4), x(k), converge to a consensus with probability one, i.e., x(k) → ¯xe, where ¯x is a scalar random variable that depends on the initial beliefs and the random sequence of matrices {W(k)}, and e is the vector of all ones. … Moreover, the random variable ¯x is a convex combination of initial agent beliefs, i.e.,”. Also see pg. 202 theorem 2: “(b) The expected value of ¯x is given by a convex combination of the initial agent values xi(0), where the weights are given by the components of the probability vector ¯π, i.e.,” )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, and Acemoglu before him or her, to modify the apparatus of claim 16 to include attributes of performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights in order to highlight similar characteristics (see pg. 200 section 3: “In particular, we show that despite the presence of forceful agents, with potentially very different opinions at the beginning, the society will ultimately converge to a consensus, in which all individuals share the same belief.”)
Regarding claim 20:
Ganglin teaches a data identification apparatus, comprising: at least one memory configured to store computer program code; and at least one processor configured to access said computer program code and operate as instructed by said computer program code, said program code including (see [0014]: “A second aspect of an embodiment of the present invention provides an abnormal user identification device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps of the first aspect when executing the computer program.”):
first determining code configured to cause the at least one processor to determine a target user set from a plurality of users, the target user set comprising at least two users having one or more social relationships (see [0037]: “In this embodiment, after determining the user information of each user, the identification device will combine the users in pairs, calculate the matching degree between the two users, compare the matching degree with a preset matching degree threshold, and determine whether the two users are associated users.”),
wherein the one or more social relationships are represented by values of a data structure that is generated by at least (see [0010]: “Based on the associated users of each of the users, a relationship network of the user database is created.”)
obtaining initial weights based on interactions between the at least two users (see [0048]: “Based on the associated users of each of the users, a user relationship network of the user database is created; the user relationship network includes a user node recording an initial value of the credit coefficient of each of the users; the initial value of the credit coefficient of the user is determined by the credit coefficient of the associated users of the user;”. Also, see [0048]: “Where Cdit<sub>0</sub>(user<sub>N</sub>) is the initial value of the credit coefficient of the n-th user; Trade(x<sub>j</sub>) is the contribution of the j-th transaction record of the n-thuser to the credit coefficient; Cdit<sub>0</sub>(user<sub>i</sub>) is the credit coefficient of the i-th associated user of the n-th user, and Cofft<sub>i</sub> is the preset weight of the i-th associated user; M is the number of transactions of the n-th user, and N is the number of users associated with the n-th user.”)
…
first acquiring code configured to cause the at least one processor to acquire a default abnormal user and determining abnormal users in the target user set based on the default abnormal user (see [0017]: “The embodiment of the present invention obtains user information of each user in a user database”. Also see [0013]: “Users whose adjusted credit coefficient is less than a preset credit threshold are selected as abnormal users.”)
second determining code configured to cause the at least one processor to determine a status of the target user set based on the abnormal users in the target user set (see [0017]: “Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”)
Ganglin does not explicitly teach performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights, identify a diffusion-abnormal user from to-be-confirmed users based on social relationships between the abnormal users or the to-be-confirmed users in the target user set based on the status of the target user set being abnormal, wherein the to-be-confirmed users comprise users in the target user set other than the abnormal users.
Xiukun, however, teaches in analogous identifying a diffusion-abnormal user from (see pg. 3 lines 1-3: “One or more embodiments of this specification describe a method and apparatus that can diffuse identified high-risk users based on a crowd relationship graph, thereby more efficiently and accurately identifying high-risk users.”. Also see pg. 7 lines 3-13 “In response to this incident, the computing platform first determined whether the user was a high-risk user. If so, it "diffused" the risk based on the user's relationship network and discovered other high-risk users. Correspondingly, the computing platform includes an anomaly rule engine and a diffusion engine. The anomaly rule engine makes a preliminary judgment on the user's business application event based on pre-set anomaly rules. In some cases, this can be supplemented by human review. Once the current business application event is determined to be a high-risk event, the current user will also be identified as a high-risk user. Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks.”. Also see )
Xiukun does not explicitly teach to-be-confirmed users. Ganglin, however, does as recited (see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin and Xiukun before him or her, to modify the method of claim 16 to include attributes of using diffusion techniques to identify abnormal users in order to greatly improve identification efficiency (see pg. 7 lines 12-20: “Furthermore, the diffusion engine is used to diffuse high-risk users based on the current users' relationship networks. This is based on observations and statistics: many high-risk events exhibit "group" characteristics, that is, they require the cooperation of multiple people to complete. In this way, if a user is identified as a high-risk user, the user's relationship network can be further analyzed to discover groups with "gang" characteristics, thereby identifying other related high-risk users. In this way, multiple high-risk users can be identified from a single business request event, greatly improving identification efficiency.”.).
Ganglin in view of Xiukun does not teach performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights.
Acemoglu, however, analogously teaches performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights (see pg. 200 section 3: “In this section, we provide our main convergence result. In particular, we show that despite the presence of forceful agents, with potentially very different opinions at the beginning, the society will ultimately converge to a consensus, in which all individuals share the same belief. … Our analysis essentially relies on showing that iterates of Eq. (4), x(k), converge to a consensus with probability one, i.e., x(k) → ¯xe, where ¯x is a scalar random variable that depends on the initial beliefs and the random sequence of matrices {W(k)}, and e is the vector of all ones. … Moreover, the random variable ¯x is a convex combination of initial agent beliefs, i.e.,”. Also see pg. 202 theorem 2: “(b) The expected value of ¯x is given by a convex combination of the initial agent values xi(0), where the weights are given by the components of the probability vector ¯π, i.e.,” )
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, and Acemoglu before him or her, to modify the non-transitory computer-readable medium of claim 16 to include attributes of performing convex transformations on the initial weights that are standardized to generate the values, the convex transformations magnifying the difference between the standardized weights in order to highlight similar characteristics (see pg. 200 section 3: “In particular, we show that despite the presence of forceful agents, with potentially very different opinions at the beginning, the society will ultimately converge to a consensus, in which all individuals share the same belief.”)
Claims 3 and 18 are rejected under 35 U.S.C 103 as being unpatentable over Ganglin (CN108615119A, hereinafter Ganglin) in view of Xiukun et al. (TWI804575B, hereinafter Xiukun) in further view of Mengyu (CN107093090A, hereinafter Mengyu).
Regarding claim 3:
Ganglin in view of Xiukun in view of Acemoglu teaches the method of claim 1.
Ganglin further teaches wherein the determining the status of the target user set based on the abnormal users comprises: acquiring a quantity of the abnormal users and acquiring a total quantity of the users in the target user set (see [0017]: “The embodiment of the present invention obtains user information of each user in a user database, determines the associated users of each user based on the user information, and constructs a user relationship network … Then, each transaction behavior record is identified, the credit adjustment coefficient is determined, the credit coefficient of each user is adjusted, and users whose credit coefficients are lower than a preset credit threshold are selected as abnormal users.”.);
determining an anomaly concentration of the target user set according to the quantity of the abnormal users and the total quantity of the users in the target user set (see [0058]: “In this embodiment, after adjusting the credit coefficients of all users, the identification device will compare each adjusted credit coefficient with the credit threshold to determine whether the user is an abnormal user. If the credit coefficient is less than the credit threshold, the user is identified as an abnormal user. Conversely, if the user's credit coefficient is greater than or equal to the credit threshold, the user is identified as a normal user.”).
Ganglin does not explicitly teach determining the status of the target user set as a normal state based on the anomaly concentration being less than a concentration threshold or determining the status of the target user set as abnormal based on the anomaly concentration being greater than or equal to the concentration threshold.
Mengyu, however, teaches in analogous determining the status of the target user set as a normal state based on the anomaly concentration being less than a concentration threshold (see [0062]: “After the server generates the user relationship network, it determines ordering users whose association relationships are lower than a preset threshold from the user relationship network, and deletes the ordering users whose association relationships are lower than the preset threshold from the relationship network to form a strong user relationship network.”),
determining the status of the target user set as abnormal based on the anomaly concentration being greater than or equal to the concentration threshold (see [0070]: “S309: When the number of ordering users belonging to the same community whose user characteristic information meets the preset order-brushing information is greater than a set threshold, all ordering users in the community are determined to be abnormal users.”.).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, Acemoglu, and Mengyu before him or her, to modify the method of claim 3 to include attributes of determining status of a the target user set as abnormal or normal based on their difference from the concentration threshold in order to improve accuracy and completeness of identification of users (see Mengyu at [0032]: “In order to solve the problem in the prior art of inaccurate and incomplete identification of users with abnormal behaviors such as group order-padding, an embodiment of the present invention provides an abnormal user identification scheme, in which a user relationship network is established based on the association relationships between each ordering user; and the user relationship network is divided into multiple communities using the community division principle. When some ordering users in a community are determined to be abnormal users, all ordering users in the community may also be determined to be abnormal users, thereby more comprehensively and accurately identifying users with abnormal behaviors.”. Also see [0071]: “After the server divides the graph network into multiple communities, it determines whether the user characteristic information of each ordering user in each community meets the preset order-brushing information. If the number of ordering users in a community whose user characteristic information meets the preset order-brushing information is greater than the set threshold, since other ordering users in the community are directly or indirectly related, it can be determined that all ordering users in the community are abnormal users.”.)
Claims 7-11 and 14 are rejected under 35 U.S.C 103 as being unpatentable over Ganglin (CN108615119A. hereinafter Ganglin) in view of Xiukun (TWI804575B, hereinafter Xiukun) in further view of Acemoglu et al. (“Spread of (mis)information in social networks” hereinafter, Acemoglu) and further in view of Hongjun et al. (WO2018219223A1, hereinafter Hongjun).
Regarding claim 7:
Ganglin in view of Xiukun in further view of Acemoglu teaches the method of claim 6.
Ganglin in view of Xiukun in further view of Acemoglu does not explicitly teach wherein the dividing the plurality of users into the plurality of user sets comprises: determining a relationship topology graph based on the social relationships and the social behaviors among the plurality of users, wherein, in the relationship topology graph, each node corresponds to one of the plurality of users, and an edge connecting two nodes indicates that the users corresponding to two nodes have a social relationship, determining a closeness of the social relationship between two users based on the social relationships and the social behaviors among the plurality of users, determining a weight of an edge between nodes corresponding to the two users based on the closeness of the social relationship between the two users, or dividing the relationship topology graph into at least two topology sub-graphs by using a clustering algorithm, and selecting a set of users corresponding to nodes in one of the at least two topology sub-graphs as the target user set.
Hongjun, however, analogously teaches wherein the dividing the plurality of users into the plurality of user sets comprises: determining a relationship topology graph based on the social relationships and the social behaviors among the plurality of users, wherein, in the relationship topology graph, each node corresponds to one of the plurality of users, and an edge connecting two nodes indicates that the users corresponding to two nodes have a social relationship (see [0017]: “ Acquire a user relationship network, and create a user relationship topology graph based on the user relationship network with each user in the user relationship network as a node, and generate a user relationship vector corresponding to each user based on the user relationship topology graph.”.),
determining a closeness of the social relationship between two users based on the social relationships and the social behaviors among the plurality of users, determining a weight of an edge between nodes corresponding to the two users based on the closeness of the social relationship between the two users (see [0038]: “Among them, (X1 and X2), (X1 and X3), (X2 and X3), (X3 and X4), and (X3 and X5) are all direct friend relationships and can be called first-degree friends. For example, X1 is a close friend of X3. Among them, (X1 and X4), (X1 and X5), (X2 and X4), (X2 and X5), and (X4 and X5) are all indirect friend relationships and can be called second-degree friends. For example, X4 is a second-degree friend of X1 (i.e., X4 is a first-degree friend of X1's first-degree friend X3). In other words, a node may correspond to a zero-degree friend (i.e., the node itself), a first-degree friend, and a second-degree friend.”.); and
dividing the relationship topology graph into at least two topology sub-graphs by using a clustering algorithm, and selecting a set of users corresponding to nodes in one of the at least two topology sub-graphs as the target user set (see [0074]: “S302: Divide the user relationship topology graph into multiple sub-topology graphs, and create a modular topology graph with the multiple sub-topology graphs as nodes.”/ Also see [0087]: “S307: Cluster the user relationship network according to the target vector corresponding to each user, so as to divide the user relationship network into multiple user sets.”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, Acemoglu, and Hongjun before him or her, to modify the method of claim 7 to include attributes of determining a relationship topology graph based on the social relationships and the social behaviors among the plurality of users, wherein, in the relationship topology graph, each node corresponds to one of the plurality of users, and an edge connecting two nodes indicates that the users corresponding to two nodes have a social relationship, determining a closeness of the social relationship between two users based on the social relationships and the social behaviors among the plurality of users, determining a weight of an edge between nodes corresponding to the two users based on the closeness of the social relationship between the two users, and dividing the relationship topology graph into at least two topology sub-graphs by using a clustering algorithm, and selecting a set of users corresponding to nodes in one of the at least two topology sub-graphs as the target user set in order to represent users with social relationships and improve accuracy of community division (see Hongjun [0051]: “The embodiment of the present invention obtains a user relationship network, creates a user relationship topology graph based on the user relationship network with each user in the user relationship network as a node, generates a user relationship vector corresponding to each user based on the user relationship topology graph, obtains a user attribute vector corresponding to each user, merges the user relationship vector and the user attribute vector corresponding to each user, obtains a target vector corresponding to each user, and clusters the user relationship network based on the target vector corresponding to each user to divide the user relationship network into multiple user sets. Since both user attributes and social relationships are converted into vectors for calculation, the computational complexity can be effectively reduced. Moreover, by considering both user attributes and social relationships at the same time, the division dimensions can be enriched, thereby improving the accuracy of community division.”.)
Regarding claim 8:
Ganglin in view of Xiukun in further view of Acemoglu and further in view of Hongjun teaches the method of claim 7.
Ganglin does not teach wherein the dividing the relationship topology graph into the at least two topology sub-graphs by using the clustering algorithm comprises: acquiring a sampling path corresponding to a first node from the relationship topology graph based on a quantity of sampling paths, determining a jump probability between the first node and an association node in the sampling path based on an edge weight in the relationship topology graph, the association node being a node in the sampling path other than the first node, or updating the relationship topology graph based on the jump probability to obtain an updated relationship topology graph, and dividing the updated relationship topology graph to obtain the at least two topology sub- graphs.
Hongjun, however, analogously teaches wherein the dividing the relationship topology graph into the at least two topology sub-graphs by using the clustering algorithm comprises: acquiring a sampling path corresponding to a first node from the relationship topology graph based on a quantity of sampling paths (see [0040]: “Furthermore, please refer to Figure 1c, which is a partial schematic diagram of another user relationship topology diagram provided by an embodiment of the present invention. As shown in Figure 1c, let t be the starting node of the node sequence, where X1 and X2 are both first-degree friends of t, and X3 is a second-degree friend of t.”. Also see [0041]: “word2vec uses the distributed representation of word vectors. The basic idea of word2vec is to map each node into a user relationship vector (such as a real number vector) by training multiple node sequences. The multiple node sequences are used as samples”.);
determining a jump probability between the first node and an association node in the sampling path based on an edge weight in the relationship topology graph, the association node being a node in the sampling path other than the first node (see [0056]: “Specifically, the server may select a target node as a starting node in the user relationship topology graph, and calculate a transition probability for node jumping based on a preset random walk parameter and the relationship degree between nodes in the user relationship topology graph.”); and
updating the relationship topology graph based on the jump probability to obtain an updated relationship topology graph, and dividing the updated relationship topology graph to obtain the at least two topology sub-graphs (see [0056]: “A plurality of node sequences including the starting node are generated according to the transition probability and a preset sequence length. Continue to select the next node in the user relationship topology graph as the starting node, and generate multiple node sequences including the starting node until all nodes in the user relationship topology graph are selected as the starting nodes. Among them, the random walk parameters can be the p and q parameters in the Random Walk algorithm in the embodiment corresponding to Figure 1c above, the relationship degree is used to characterize the degree of association between nodes, and the relationship degree can refer to zero-degree friends, first-degree friends, and second-degree friends in the embodiment corresponding to Figure 1c above.”. Also see [0084]: “The server can generate multiple module node sequences based on the modular topology graph (such as the module node sequence can be a-b-c), and generate module relationship vectors corresponding to node a, node b, and node c respectively based on the multiple module node sequences, that is, the module relationship vectors corresponding to sub-topology graph A, sub-topology graph B, and sub-topology graph C respectively.”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, Acemoglu, and Hongjun before him or her, to modify the method of claim 8 to include attributes of dividing the relationship topology graph into the at least two topology sub-graphs by using the clustering algorithm comprises: acquiring a sampling path corresponding to a first node from the relationship topology graph based on a quantity of sampling paths, determining a jump probability between the first node and an association node in the sampling path based on an edge weight in the relationship topology graph, the association node being a node in the sampling path other than the first node, and updating the relationship topology graph based on the jump probability to obtain an updated relationship topology graph, and dividing the updated relationship topology graph to obtain the at least two topology sub- graphs in order to represent users with social relationships and improve accuracy of community division (see Hongjun [0051]: “The embodiment of the present invention obtains a user relationship network, creates a user relationship topology graph based on the user relationship network with each user in the user relationship network as a node, generates a user relationship vector corresponding to each user based on the user relationship topology graph, obtains a user attribute vector corresponding to each user, merges the user relationship vector and the user attribute vector corresponding to each user, obtains a target vector corresponding to each user, and clusters the user relationship network based on the target vector corresponding to each user to divide the user relationship network into multiple user sets. Since both user attributes and social relationships are converted into vectors for calculation, the computational complexity can be effectively reduced. Moreover, by considering both user attributes and social relationships at the same time, the division dimensions can be enriched, thereby improving the accuracy of community division.”.)
Regarding claim 9:
Ganglin in view of Xiukun in further view of Acemoglu and further in view of Hongjun teaches the method of claim 7.
Ganglin does not explicitly teach wherein the determining the weight of the edge between the nodes corresponding to the two users based on the closeness of the social relationship between the two users comprises: setting the closeness of the social relationship between the two users as an initial weight of the edge between the two nodes corresponding to the two users performing probability transformation on the initial weight to obtain an edge weight.
Hongjun, however, analogously teaches wherein the determining the weight of the edge between the nodes corresponding to the two users based on the closeness of the social relationship between the two users comprises: setting the closeness of the social relationship between the two users as an initial weight of the edge between the two nodes corresponding to the two users performing probability transformation on the initial weight to obtain an edge weight (see [0056]: “Continue to select the next node in the user relationship topology graph as the starting node, and generate multiple node sequences including the starting node until all nodes in the user relationship topology graph are selected as the starting nodes. Among them, the random walk parameters can be the p and q parameters in the Random Walk algorithm in the embodiment corresponding to Figure 1c above, the relationship degree is used to characterize the degree of association between nodes, and the relationship degree can refer to zero-degree friends, first-degree friends, and second-degree friends in the embodiment corresponding to Figure 1c above.”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, Acemoglu, and Hongjun before him or her, to modify the method of claim 9 to include attributes of setting the closeness of the social relationship between the two users as an initial weight of the edge between the two nodes corresponding to the two users performing probability transformation on the initial weight to obtain an edge weight in order to represent users with social relationships and improve accuracy of community division (see Hongjun [0051]: “The embodiment of the present invention obtains a user relationship network, creates a user relationship topology graph based on the user relationship network with each user in the user relationship network as a node, generates a user relationship vector corresponding to each user based on the user relationship topology graph, obtains a user attribute vector corresponding to each user, merges the user relationship vector and the user attribute vector corresponding to each user, obtains a target vector corresponding to each user, and clusters the user relationship network based on the target vector corresponding to each user to divide the user relationship network into multiple user sets. Since both user attributes and social relationships are converted into vectors for calculation, the computational complexity can be effectively reduced. Moreover, by considering both user attributes and social relationships at the same time, the division dimensions can be enriched, thereby improving the accuracy of community division.”.)
Regarding claim 10:
Ganglin in view of Xiukun in further view of Acemoglu and further in view of Hongjun teaches the method of claim 8.
Ganglin does not explicitly teach wherein the determining the jump probability between the first node and the association node in the sampling path based on the edge weight in the relationship topology graph comprises: acquiring an intermediate node between the first node and the association node from the sampling path in a case that there is no edge between the first node and the association node, the first node reaching the association node through the intermediate node, selecting, as a connection node pair, two nodes in the first node, the intermediate node, and the association node having an edge, acquiring an edge weight corresponding to the connection node pair, or determining the jump probability between the first node and the association node based on the edge weight corresponding to the connection node pair.
Hongjun, however, analogously teaches wherein the determining the jump probability between the first node and the association node in the sampling path based on the edge weight in the relationship topology graph comprises: acquiring an intermediate node between the first node and the association node from the sampling path in a case that there is no edge between the first node and the association node, the first node reaching the association node through the intermediate node (see [0040]: “Therefore, if t has jumped to X2, then the transition probability of X2 jumping to t is a=1/p, the transition probability of X2 jumping to X1 is a=1, and the transition probability of X2 jumping to X3 is a=1/q. Then X2 can jump to t or X1 or X3 according to the corresponding transition probability.”.);
selecting, as a connection node pair, two nodes in the first node, the intermediate node, and the association node having an edge, acquiring an edge weight corresponding to the connection node pair (see [0040]: “Among them, p and q are two important parameters in the Random Walk algorithm, which can affect the node sequence generated by the Random Walk algorithm. When q>1, X2 tends to transfer to t's first-degree friends, and Random Walk at this time tends to be a breadth-first search; when q<1, X2 tends to transfer to t's second-degree friends, and Random Walk at this time tends to be a depth-first search.
”.) and
determining the jump probability between the first node and the association node based on the edge weight corresponding to the connection node pair (see [0040]: “The preset transition probability of jumping to t itself is a=1/p, the transition probability of jumping to t's first-degree friend is a=1, and the transition probability of jumping to t's second-degree friend is a=1/q. Therefore, if t has jumped to X2, then the transition probability of X2 jumping to t is a=1/p, the transition probability of X2 jumping to X1 is a=1, and the transition probability of X2 jumping to X3 is a=1/q. Then X2 can jump to t or X1 or X3 according to the corresponding transition probability. If X2 further jumps to X1, it can further jump based on the corresponding transition probabilities of X1 jumping back to X2, X1 jumping to X2's first-degree friend, and X1 jumping to X2's second-degree friend.
That is, each node can jump to the previous node or the first-degree friend of the previous node or the second-degree friend of the previous node, so in the process of generating the node sequence, the transition probability of the corresponding node can be re-determined according to the node jumped to each time.”).
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, Acemoglu, and Hongjun before him or her, to modify the method of claim 10 to include attributes of acquiring an intermediate node between the first node and the association node from the sampling path in a case that there is no edge between the first node and the association node, the first node reaching the association node through the intermediate node, selecting, as a connection node pair, two nodes in the first node, the intermediate node, and the association node having an edge, acquiring an edge weight corresponding to the connection node pair, and determining the jump probability between the first node and the association node based on the edge weight corresponding to the connection node pair in order to represent users with social relationships and improve accuracy of community division (see Hongjun [0051]: “The embodiment of the present invention obtains a user relationship network, creates a user relationship topology graph based on the user relationship network with each user in the user relationship network as a node, generates a user relationship vector corresponding to each user based on the user relationship topology graph, obtains a user attribute vector corresponding to each user, merges the user relationship vector and the user attribute vector corresponding to each user, obtains a target vector corresponding to each user, and clusters the user relationship network based on the target vector corresponding to each user to divide the user relationship network into multiple user sets. Since both user attributes and social relationships are converted into vectors for calculation, the computational complexity can be effectively reduced. Moreover, by considering both user attributes and social relationships at the same time, the division dimensions can be enriched, thereby improving the accuracy of community division.”.)
Regarding claim 11:
Ganglin in view of Xiukun in further view Acemoglu and further in view of Hongjun teaches the method of claim 8.
Ganglin does not explicitly teach wherein the updating the relationship topology graph based on the jump probability comprises: updating a connected edge in the relationship topology graph based on the first node and the association node to obtain a transition relationship topology graph, the first node and the association node in the transition relationship topology graph being both connected with edges, or setting the jump probability between the first node and the association node in the transition relationship topology graph as an edge weight between the first node and the association node to obtain the updated relationship topology graph.
Hongjun further teaches wherein the updating the relationship topology graph based on the jump probability comprises: updating a connected edge in the relationship topology graph based on the first node and the association node to obtain a transition relationship topology graph, the first node and the association node in the transition relationship topology graph being both connected with edges (see [0039]: “In particular, the server can specifically generate multiple node sequences in the user relationship topology graph through a Random Walk algorithm. … The specific process of the Random Walk algorithm is: select a node as the starting node in the user relationship topology graph, mark the starting node as the current position, randomly or according to a preset probability select a neighbor node of the current position, and move the current position to the selected neighbor node position (that is, mark the selected neighbor node as the current position). Repeat this step n times, and finally get a node sequence with a length of n from the start node to the end node. By selecting different nodes as the starting nodes, more node sequences of length n can be generated.”) and
setting the jump probability between the first node and the association node in the transition relationship topology graph as an edge weight between the first node and the association node to obtain the updated relationship topology graph (see [0040]: “That is, each node can jump to the previous node or the first-degree friend of the previous node or the second-degree friend of the previous node, so in the process of generating the node sequence, the transition probability of the corresponding node can be re-determined according to the node jumped to each time.”.)
Before the effective filing date of the claimed invention, it would have been obvious to one of ordinary skill in the art, having the teachings of Ganglin, Xiukun, Acemoglu, and Hongjun before him or her, to modify the method of claim 11 to include attributes of updating a connected edge in the relationship topology graph based on the first node and the association node to obtain a transition relationship topology graph, the first node and the association node in the transition relationship topology graph being both connected with edges and setting the jump probability between the first node and the association node in the transition relationship topology graph as an edge weight between the first node and the association node to obtain the updated relationship topology graph in order to represent users with social relationships and improve accuracy of community division (see Hongjun [0051]: “The embodiment of the present invention obtains a user relationship network, creates a user relationship topology graph based on the user relationship network with each user in the user relationship network as a node, generates a user relationship vector corresponding to each user based on the user relationship topology graph, obtains a user attribute vector corresponding to each user, merges the user relationship vector and the user attribute vector corresponding to each user, obtains a target vector corresponding to each user, and clusters the user relationship network based on the target vector corresponding to each user to divide the user relationship network into multiple user sets. Since both user attributes and social relationships are converted into vectors for calculation, the computational complexity can be effectively reduced. Moreover, by considering both user attributes and social relationships at the same time, the division dimensions can be enriched, thereby improving the accuracy of community division.”.)
Regarding claim 14:
Ganglin in view of Xiukun in further view of Hongjun teaches the method of claim 7.
Ganglin further teaches wherein the identifying the diffusion-abnormal user from the to-be- confirmed users based on the social relationships between the abnormal users and the to-be- confirmed users in the target user set based on the status of the target user set being abnormal comprises: determining users having the social relationships with the abnormal users from the to-be- confirmed users based on the status of the target user set being abnormal; acquiring abnormal user nodes corresponding to the abnormal users (see [0039]: “Preferably, in this embodiment, if the identification device has established a user relationship network for some users, each user will be divided into an identified user group and a user group to be identified before S102. For users in the identified user group, there is no need to identify the matching degree between the users in the group in S102, while users in the user group to be identified need to calculate the matching degree with all users in the identified user group and the user group to be identified to determine the associated users with the user to be identified.”. Also see [0035]: “For example, when a new user is detected to be entered into the user database, the relevant operations of S101 may be performed to identify whether the user is an abnormal user.”.),
acquiring association user nodes corresponding to the users having the social relationship with the abnormal users (see [0063]: “Therefore, after a user is identified as an abnormal user, the user node corresponding to the abnormal user will be marked in the user relationship network, and other user nodes associated with the user node will be obtained to identify the user as a risky user.”.).
Ganglin does not explicitly teach determining, as a diffusion-abnormal node an association user node having an edge weight with one of a number of abnormal user nodes greater than an association threshold or determining a user corresponding to the diffusion-abnormal node as the diffusion-abnormal user.
Xiukun, however, teaches in analogous, determining, as a diffusion-abnormal node an association user node having an edge weight with one of a number of abnormal user nodes greater than an association threshold (pg. 24 lines 1-4: “If the accuracy of sampling detection is lower than a certain threshold, the algorithm process for diffusion based on the population relationship graph is adjusted, such as adjusting the proportion determination threshold of high-risk groups, so that the diffusion process of high-risk users is more accurate.”.), and
determining a user corresponding to the diffusion-abnormal node as the diffusion-abnormal user (see pg. 22 lines 1-14: ““In one example, the first user and the second user are colored in a crowd relationship graph to facilitate subsequent diffusion analysis. In one embodiment, after a first user is identified based on a high-risk business request event and then diffused to a second user based on the first user, further diffusion is performed based on the second user.
That is, in one embodiment, after the second user is determined, a third user who has a specific relationship with the second user is determined based on the above-mentioned population relationship graph, and the third user is also added to the high-risk user set. The process of determining a third user with a specific association relationship based on the second user is consistent with the process of determining the second user based on the first user described above, and the details are not repeated here. It should be understood that the above diffusion process can be repeated continuously, that is, after determining the third user, the fourth user associated with the third user is found based on the third user, and so on, until no new high-risk users appear.”.)
Examiner’s Note
Claim 12 is objected to as being dependent upon a rejected base claim but would be allowable if rewritten in independent form including all of the limitations of the base claim and any intervening claims provided 101 rejections are overcome. Regarding claim 12, the closest prior arts of record to the limitations of the aforementioned claims are Hongjun and Horvitz et al (US20140279737A1 hereinafter referred to as Horvitz). Hongjun mentions choosing a node within a relationship topology graph and calculating a jump probability which is then used to generate node sequences (see [0056]: “Specifically, the server may select a target node as a starting node in the user relationship topology graph, and calculate a transition probability for node jumping based on a preset random walk parameter and the relationship degree between nodes in the user relationship topology graph.
A plurality of node sequences including the starting node are generated according to the transition probability and a preset sequence length.
Continue to select the next node in the user relationship topology graph as the starting node, and generate multiple node sequences including the starting node until all nodes in the user relationship topology graph are selected as the starting nodes. Among them, the random walk parameters can be the p and q parameters in the Random Walk algorithm in the embodiment corresponding to Figure 1c above, the relationship degree is used to characterize the degree of association between nodes, and the relationship degree can refer to zero-degree friends, first-degree friends, and second-degree friends in the embodiment corresponding to Figure 1c above. The specific process of generating the multiple node sequences based on the p and q parameters of the Random Walk algorithm can be found in the description of the embodiment corresponding to Figure 1c above, and will not be repeated here.”.) Horvitz mentions how transition probabilities can be related to tasks that grow exponentially (see [0056]: “One adaptive control methodology is referred to as CrowdExplorer. CrowdExplorer is based on an online learning module for learning a set of probabilistic models representing the dynamics of the world (i.e. state transitions), and a decision-making module that optimizes hiring decisions by simultaneously reasoning about its uncertainty about its models and the way a task may stochastically progress in the world. One of the challenges is that the number of state transitions that define the dynamics of consensus tasks grows exponentially in the horizon. However, the next state of the system is completely determined by the vote of a next worker. Thus, the transition probabilities may be captured with a set of models that predict the vote of a next worker based on the current state of the task.”.) The examiner has found that the distinct features of the applicant’s claimed invention over the prior art is the explicit claiming of the aforementioned limitations specified in claim 12. When viewed individually or in combination with other prior art of record, the limitations in claim 12 are distinct.
Pertinent Prior Art
The prior art made of record and not relied upon is considered pertinent to applicant’s disclosure:
US20120197834A1 — Panigraphy et al. — discloses relatedness of nodes in a graph and transition probabilities among them
US20140279737A1 — Horvitz et al. — discloses exponential growth and jump probabilities
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
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/ANDREW BRACERO/Examiner, Art Unit 2126 /VAN C MANG/Primary Examiner, Art Unit 2126