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 .
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
Claim(s) 1, 2, 5-7, 9-11, 13-16, 18, 20 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Luo et al. (US 2020/0396233) hereafter Luo.
1. Luo discloses a computing apparatus comprising:
a computer-readable storage media (fig 7 and corresponding text);
a detection engine comprising processor-executable instructions stored on the computer-readable storage media (fig 7 and corresponding text); and
a processor coupled to the computer-readable storage media and configured to execute the processor-executable instructions, wherein the processor-executable instructions, when executed by the processor, direct the computing apparatus (fig 7 and corresponding text), to at least:
determine a plurality of tenants within a cloud-based environment (para 19-23, figs 1 and 2 and corresponding text, resources are URL (universal resource locator));
determine a plurality of resources corresponding to the plurality of tenants, wherein a resource within the plurality of resources corresponds to a respective tenant within the plurality of tenants (para 24-33);
generate one or more clusters from the plurality of resources (para 34-53), wherein a cluster within the one or more clusters comprises a plurality of nodes and one or more lines representing relationships between nodes (fig 1 and corresponding text, subgraph and trace image are clusters; see further para 41-53),
detect that a first cluster of the one or more clusters comprises one or more bot-created tenants, wherein the first cluster comprises a first subset of tenants of the plurality of tenants (section 4.1, clustering creates groups of nodes based on similarities); and
identify a first bot-created tenant within the first cluster using the first subset of tenants (para 15-18, a deep learning model based on a convolutional neural network (CNN), may be used to classify the trace image into one of two categories: bot or human).
2. Luo discloses the computing apparatus of claim 1, wherein the processor-executable instructions to identify the first bot-created tenant within the first cluster using the first subset of tenants, when executed by the processor, further direct the computing apparatus to: generate a plurality of tenant features for the first subset of tenants (para 41-53); input the plurality of tenant features for the first subset of tenants as an input into a neural network model (para 15-18); receive a score for one or more tenants within the first subset of tenants as an output from the neural network model (para 15-18, 54-58); and determine the first bot-created tenant from a respective score received from the neural network model (para 15-18, 54-58).
5. Luo discloses the computing apparatus of claim 1, wherein the processor-executable instructions, when executed by the processor, further direct the computing apparatus to: determine at least one legitimate tenant within the first subset of tenants of the first cluster (para 15-18).
6. Luo discloses the computing apparatus of claim 1, wherein the processor-executable instructions to generate the one or more clusters, when executed by the processor, further direct the computing apparatus to: execute a clustering algorithm on an input comprising the plurality of tenants and the plurality of resources (para 19-23, 34-53); generate the one or more clusters from the input, wherein the plurality of tenants and the plurality of resources are represented as nodes within the one or more clusters (para 19-23, 34-53); and generate a centroid for each of the one or more clusters, wherein a respective centroid is determined using a relational position of the respective nodes within the cluster (para 19-23, 34-53).
Claim 7 is similar in scope to claim 1 and is rejected under similar rationale.
9. Luo discloses the method of claim 7, wherein submitting, by the detection engine, the first subset of tenants as input into the neural network model comprises: determining, by the detection engine, service access information associated with a first tenant within the first subset of tenants (para 34-40); generating, by the detection engine, a plurality of tenant features from service access information for the first tenant (para 41-53); and submitting, by the detection engine, the plurality of tenant features for the first tenant as input into the neural network model (para 15-18).
10. Luo discloses the method of claim 7, wherein determining, by the detection engine, the relational similarity between the first subset of tenants using the plurality of resources comprises: generating, by the detection engine, a plurality of clusters according to the plurality of resources, wherein: the plurality of tenants is grouped into the plurality of clusters (para 34-53); a cluster comprises a plurality of nodes connected by lines, each node representing a tenant or a resource, and each line connecting a tenant to a resource used by the tenant, such that nodes in a cluster have relational similarity to one another through shared ones of the resources (para 34-53); and determining, by the detection engine, a first cluster comprising the plurality of bot-created tenants from the relational similarity between the nodes within the first cluster, wherein the first cluster comprises the first subset of tenants (para 15-18, 34-53).
11. Luo discloses the method of claim 7, wherein responsive to submitting the first subset of tenants to the neural network model the method further comprises: identifying, by the detection engine, a subset of bot-created tenants within the first subset using the output from the neural network model, wherein the plurality of bot-created tenants comprises the subset of bot-created tenants (para 15-18); and flagging, by the detection engine, the subset of bot-created tenants as fraudulent (para 15-18).
13. Luo discloses the method of claim 7, wherein submitting, by the detection engine, the first subset of tenants as input into the neural network model comprises: extracting, by the detection engine, a plurality of tenant features from the first subset of tenants (para 34-40); generating, by the detection engine, an input comprising the plurality of tenant features from the first subset of tenants (para 41-53); and submitting, by the detection engine, the input as input into the neural network model (para 15-18, 54-58).
14. Luo discloses the method of claim 7, wherein the method further comprises: generating, by the detection engine, a visual representation of the first subset using the relational similarity (para 41-53); identifying, by the detection engine, a subset of bot-created tenants within the first subset from the visual representation (para 15-18, 54-58); and providing, by the detection engine, the visual representation to a client device for display via a user interface (fig 4 and corresponding text).
Claim 15 is similar in scope to claim 1 and is rejected under similar rationale.
16. Luo discloses the computer readable storage media of claim 15, wherein the processor-executable instructions to submit, by the detection engine, the first subset of tenants and the first subset of resources corresponding to the first subset of tenants to the neural network model cause the processor to further execute processor-executable instructions stored in the computer readable storage media to: generate, by the detection engine, a plurality of tenant features for the first subset of tenants from service access information associated with a respective tenant within the first subset of tenants (para 34-40); generate, by the detection engine, an input comprising the plurality of tenant features and the first subset of resources corresponding to the first subset of tenants (para 41-53); and submit, by the detection engine, the input as input into the neural network model (para 15-18, 54-58).
18. Luo discloses the computer readable storage media of claim 15, wherein the output from the neural network model comprises a score and the processor-executable instructions to identify, by the detection engine, a first bot-created tenant within the first subset of tenants using an output from the neural network model (para 15-18, 54-58) cause the processor to further execute processor-executable instructions stored in the computer readable storage media to: receive, by the detection engine, a plurality of scores as output from the neural network model, wherein a score of the plurality of scores corresponds to a respective tenant from the first subset of tenants; and determine, by the detection engine, that a first tenant within the first subset of tenants is bot-created using a respective score received from the neural network model, wherein the first tenant comprises the first bot-created tenant (para 15-18, 54-58; see also fig 4 and corresponding text).
20. Luo discloses the computer readable storage media of claim 15, wherein the processor-executable instructions to flag, by the detection engine, the first bot-created tenant as fraudulent (para 15-18) cause the processor to further execute processor-executable instructions stored in the computer readable storage media to: generate, by the detection engine, a visual representation of a plurality of bot-created tenants within the first subset of tenants, wherein the plurality of bot-created tenants comprises the first bot-created tenant (para 59-63); and transmit, by the detection engine, the visual representation to a client device for display via a user interface (fig 4 and corresponding text).
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 3, 4, 8, 17, 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luo as applied to claims 1, 7, 15 above, and further in view of BotHunter: An Approach to Detect Software Bots in GitHub by Abdellatif et al. hereafter Abdellatif.
3. Luo discloses the computing apparatus of claim 1, wherein: when executed by the processor, further direct the computing apparatus to: identify, via a neural network classifier, a subset of bot-created tenants from the plurality of [[suspected bot-created]] tenants, wherein the subset of bot-created tenants comprises the first bot-created tenant (para 15-18, 54-58). Luo does not explicitly disclose the processor-executable instructions to detect that the first cluster of the one or more clusters comprises one or more bot-created tenants, when executed by the processor, further direct the computing apparatus to: submit the one or more clusters as input into a cluster classifier; and determine, via the cluster classifier, that a first cluster of the one or more clusters comprises a plurality of suspected bot-created tenants; and the processor-executable instructions to identify the first bot-created tenant within the first cluster from the first subset of tenants. However, in an analogous art, Abdellatif discloses detecting bots through feature detection including submit the one or more clusters as input into a cluster classifier (Section 3.1); and determine, via the cluster classifier, that a first cluster of the one or more clusters comprises a plurality of suspected bot-created tenants (Section 3.2). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Luo with the implementation of Abdellatif in order to identify and use the most relevant features (Abstract).
4. Luo discloses the computing apparatus of claim 1, but does not explicitly disclose wherein the processor-executable instructions to determine the plurality of resources corresponding to the plurality of tenants, when executed by the processor, further direct the computing apparatus to: determine one or more identity and access credentials for a tenant within the plurality of tenants for accessing a service within the cloud-based environment; and determine service access information for the tenant using the one or more identity and access credentials. However, in an analogous art, Abdellatif discloses detecting bots through feature detection including determine one or more identity and access credentials for a tenant within the plurality of tenants for accessing a service within the cloud-based environment (Section 3.1); and determine service access information for the tenant using the one or more identity and access credentials (Section 3.1). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Luo with the implementation of Abdellatif in order to identify and use the most relevant features (Abstract).
8. Luo discloses the method of claim 7, but does not explicitly disclose wherein the plurality of resources comprise identity and access credentials used by a respective tenant to access a service within the cloud-based or hybrid environment. However, in an analogous art, Abdellatif discloses detecting bots through feature detection including wherein the plurality of resources comprise identity and access credentials used by a respective tenant to access a service within the cloud-based or hybrid environment (Section 3.1). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Luo with the implementation of Abdellatif in order to identify and use the most relevant features (Abstract).
17. Luo discloses the computer readable storage media of claim 15, but does note explicitly disclose wherein the processor-executable instructions cause the processor to further execute processor-executable instructions stored in the computer readable storage media to: classify, by the detection engine, the plurality of tenants into one or more tenant classifications, wherein the one or more tenant classifications indicate a relational similarity between tenants and resources within a respective tenant classification; and determine, by the detection engine, the first subset of tenants using the tenant classifications. However, in an analogous art, Abdellatif discloses detecting bots through feature detection including classify, by the detection engine, the plurality of tenants into one or more tenant classifications (Section 3), wherein the one or more tenant classifications indicate a relational similarity between tenants and resources within a respective tenant classification (Section 3); and determine, by the detection engine, the first subset of tenants using the tenant classifications (Section 3). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Luo with the implementation of Abdellatif in order to identify and use the most relevant features (Abstract).
19. Luo discloses the computer readable storage media of claim 15, but does not explicitly disclose wherein the processor-executable instructions to determine, by the detection engine, the plurality of resources corresponding to the plurality of tenants cause the processor to further execute processor-executable instructions stored in the computer readable storage media to: determine, by the detection engine, one or more identity and access credentials for a tenant within the plurality of tenants for accessing a service within the cloud-based or hybrid environment. However, in an analogous art, Abdellatif discloses detecting bots through feature detection including determine, by the detection engine, one or more identity and access credentials for a tenant within the plurality of tenants for accessing a service within the cloud-based or hybrid environment (Section 3.1). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Luo with the implementation of Abdellatif in order to identify and use the most relevant features (Abstract).
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over Luo as applied to claim 7 above, and further in view of CGNN: A Compatibility-Aware Graph Neural Network for Social Media Bot Detection by Huang et al. hereafter Huang.
12. Luo discloses the method of claim 7, but does not explicitly disclose wherein the neural network model comprises a heterogenous graph neural network comprising a two-tier architecture having a precision of greater than 98%. However, in an analogous art, Huang discloses detecting bots in online social networks including wherein the neural network model comprises a heterogenous graph neural network comprising a two-tier architecture having a precision of greater than 98% (figure 1, shows heterogenous graph neural network with two tier; Table III, accuracy 99.13%). It would have been obvious to a person of ordinary skill in the art before the effective filing date to modify the implementation of Luo with the implementation of Huang in order to achieve greater performance benefits (Section II.A).
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JAMES R TURCHEN whose telephone number is (571)270-1378. The examiner can normally be reached Monday-Friday: 7-3.
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/JAMES R TURCHEN/Primary Examiner, Art Unit 2439
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