DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Amendments
This office action responds to the amendments filed on March 9, 2026 for application 18/620,654. Claims 1, 15, 18, and 21 are amended, claim 3 is cancelled, and claim 21 is added as a new claim. Claims 1-2 and 4-21 remain pending in the application.
Response to Arguments
The Examiner has fully considered the Applicant’s arguments filed on March 9, 2026, and the Examiner responds as provided below.
Regarding the Applicant’s response at pages 7-9 of the Remarks that concerns the § 103 rejection, the Applicant suggests that Guo fails to suggest that the analysis of data elements is performed in real-time in response to the request for data. The Examiner notes that “real-time” is a broader term than “automatically,” i.e., the response is performed by the computer nearly instantaneously. Accordingly, an issue exists as to whether Guo and its teaching of “fast” at least suggests the limitation at issue of “real-time.” Rather than argue the point further, the Examiner produces another reference, Chickerur, the puts the issue to rest as detailed below.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, 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 following conventions apply to the mapping of the prior art to the claims:
Italicized text – claim language.
Parenthetical plain text – Examiner’s citation and explanation.
Citation without an explanation – an explanation has been previously provided for the respective limitation(s).
Quotation marks – language quoted from a prior art reference.
Underlining – language quoted from a claim.
Brackets – material altered from either a prior art reference or a claim, which includes the Examiner’s explanation that relates a claim limitation to the quoted material of a reference.
Braces – a limitation taught by another reference, but the limitation is presented with the mapping of the instant reference for context.
Numbered superscript – a first phrase to be moved upwards to the primary reference analysis.
Lettered superscript – a second phrase to be moved after the movement of the first phrase from which it was lifted, or more succinctly, move numbered material first, lettered material last.
A. Claims 1-2, 12, 14-15, 17-18, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Cervantez et al. (US 10,979,461, “Cervantez”) in view of Redberg (US 2015/0154418, “Redberg”), and further in view of Chickerur et al. (US 2020/0233977, “Chickerur”).
Regarding Claim 1
Cervantez discloses
A method (abstract, Figs. 2-4), comprising:
identifying a request for data (Fig. 2, Col. 12:3-23, “At 206, the data security evaluation service 100 may receive data 116 that is, or is requested [i.e., a request for the data to be stored] to be, stored in a storage container [i.e., only the identified data is stored], such as the storage container 108(P), maintained by the network-based storage system 114.”);
identifying one or more data elements that are responsive to the request for data (Col. 8:25-60, “Thus, the trained machine learning model(s) 122 may be configured with text recognition, image recognition, and other functionality to process unknown data [i.e., the data encompassing the request for data] 116 with various types of content. The class labels, in this case, may correspond to a classification of the unknown data 116 as a [identified] type of data among multiple different types of data [data elements] corresponding to different sensitivity levels [i.e., different data elements possess different sensitivity levels] (e.g., medical data, PII or SPI data, publicly accessible data, etc.).”);
analyzing the one or more data elements to classify whether the one or more data elements comprise personally identifiable information (Col. 8:25-60, “Thus, the trained machine learning model(s) 122 may be configured with text recognition, image recognition, and other functionality to process [analyze] unknown data 116 with various types of content. The class labels [for classifying the data elements], in this case, may correspond to a classification of the unknown data 116 as a type of data among multiple different types of data corresponding to different sensitivity levels (e.g., medical data, PII [as a data element] or SPI data, publicly accessible data, etc.).”),
wherein the analyzing is performed using one or more machine learning models (Col. 8:25-60, “Thus, the trained machine learning model(s) 122 may be configured with text recognition, image recognition, and other functionality to process [analyze] unknown data 116 with various types of content.), and
1 …; and
2 …;
wherein the steps of the method are executed by a processing device operatively coupled to a memory (Fig. 10, Col. 21:50-22:7, “The CPUs 1004 perform operations by transitioning from one discrete, physical state to the next…”; and “The chipset 1006 provides an interface between the CPUs 1004 and the remainder of the components and devices on the baseboard 1002. The chipset 1006 can provide an interface to a RAM 1008, used as the main memory in the computer 1000.”).
Cervantez doesn’t disclose
1 wherein the analyzing is performed in real-time responsive to the request for data;
2 interfacing with one or more cloud platforms of a plurality of cloud platforms to transfer the one or more data elements that have been classified as comprising personally identifiable information to the one or more cloud platforms;
Redberg, however, discloses
1 interfacing with one or more cloud platforms of a plurality of cloud platforms to transfer the one or more data elements that have been {classified as comprising personally identifiable information (Cervantez Col. 8:25-60)} to the one or more cloud platforms (Fig. 2, ¶ [0051], “According to one embodiment, enterprise users 202 can be operatively coupled [interfacing] with the cloud service provider [cloud platforms of a plurality of cloud platforms] 206 through a gateway 204, which is configured to interface transactions [transfer of the classified data elements] and execute instructions for read/write/search of content between the users 202 and the containers provided by the cloud service providers 206.”; and Fig. 1, ¶ [0046], “Cloud gateway [interfacing] device 108 typically acts as an interface between the clients 102 and stores 114, wherein different file/data read/write requests [for data] received from clients 102 can be handled by the gateway device 108 to identify the appropriate set of stores 114 that need to be accessed for processing the requests [to transfer data elements].”);
Regarding the combination of Cervantez and Redberg, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cloud deployment system of Cervantez to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the cloud deployment system of Cervantez, upon which the claimed invention can be seen as an “improvement” through the use of a cloud interfacing feature;
2) the prior art contained a “comparable” system, namely the cloud platform of Redberg, that has been improved in the same way as the claimed invention through the cloud interfacing feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the cloud interfacing feature to the base cloud deployment system of Cervantez, and the results would have been predictable to one of ordinary skill in the art.
Chickerur, however, discloses
1 wherein the analyzing is performed in real-time responsive to the request for data (Fig. 3, ¶ [0019], “In another example, the personal data oversight machine may be configured to scan data entries in the dataset and automatically [in real-time and not await the delay imposed by an administrator] identify which data entries include PII. For instance, common types of PII that may be stored in a dataset often have distinctive formats (e.g., phone numbers, email addresses, credit card numbers), and the personal data oversight machine may be configured to automatically identify data entries having such formats as including PII, and/or flag such entries for manual review. Such scanning may in some cases use machine learning techniques, such as trained neural networks, to identify PII.”; and “Similarly, as a server receives telemetry or diagnostic information [or similarly data elements responsive to the request for data as disclosed by Cervantes Col. 8:25-60] from a device, any data that is likely PII (e.g., an IP address or geographic location) may automatically [in real-time] be classified as such.”);
Regarding the combination of Cervantes-Redberg and Chickerur, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cloud deployment system of Cervantez-Redberg to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the cloud deployment system of Cervantez-Redberg, upon which the claimed invention can be seen as an “improvement” through the use of a real-time analysis feature;
2) the prior art contained a “comparable” system, namely the cloud platform of Chickerur, that has been improved in the same way as the claimed invention through the real-time analysis feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the real-time analysis feature to the base cloud deployment system of Cervantez-Redberg, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 2
Cervantez in view of Redberg, and further in view of Chickerur (“Cervantez-Redberg-Chickerur”) discloses the method of claim 1, and Cervantez further discloses
wherein the request for data (Fig. 2, Col. 12:3-23) comprises one of…1
Redberg further discloses
1 …a database request, a cache system request, an application programming interface request, a messaging system request and a streaming system request (¶ [0048], “In an instance, a policy stored in the policy database 110 can allow a client 102 to download [based upon a request] a searchable encrypted file from a cloud store [possessing a database that stores the file] such as store 114 a onto a local device, such as client's mobile phone, and then search the downloaded encrypted file on the local device for further processing.”).
Regarding the combination of Cervantez and Redberg, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cloud deployment system of Cervantez to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the cloud deployment system of Cervantez, upon which the claimed invention can be seen as an “improvement” through the use of a database feature;
2) the prior art contained a “comparable” system, namely the cloud platform of Redberg, that has been improved in the same way as the claimed invention through the database feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the database feature to the base cloud deployment system of Cervantez, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 12
Cervantez-Redberg-Chickerur discloses the method of claim 1, and Cervantez further discloses
further comprising predicting a policy to apply to the one or more data elements (Col. 6:1-25, “Additionally, or alternatively, the access policies 118 associated with the storage containers 108 can be access policies 118 that are applied [predicted for] on the level of a data [elements] object 110, or a folder within the storage container 108. Thus, there may be a one-to-many correspondence between the storage containers 108 and the access policies 118, e.g., a single storage container 108 may be associated with multiple access policies 118.”) that have been classified as comprising personally identifiable information (Col. 7:47-8:6, “Because of legal implications of humans obtaining access and viewing sensitive data, such as PII/SPI, medical data, or data of similar sensitivity, it is to be appreciated that the training data 120 may be created in an automated fashion (i.e., without human involvement). In some configurations, unsupervised machine learning may be used on raw customer data, which may be possible due to a large corpus of customer data.”; and Col. 8: 25-60, “The class labels [for classifying] can be directly indicative of the sensitivity [PII], such as by including “sensitive*” in the label itself (e.g., highly sensitive data, sensitive data, moderately sensitive data, not sensitive data, etc.), or the class labels can be indirectly indicative of the sensitivity, such as by labeling the data with the actual type of data (e.g., medical data, financial data, personal data, etc.).”),
wherein the predicting is performed using the one or more machine learning models (Cols. 6:1-25, 7:47-8:6, & 8:25-60).
Regarding Claim 14
Cervantez-Redberg-Chickerur discloses the method of claim 1, and Cervantez further discloses
wherein {interfacing with one or more cloud platforms of the plurality of cloud platforms (Redberg Figs. 1 & 2, ¶¶ [0046], [0051])} comprises:
uploading the one or more data elements that have been classified as comprising personally identifiable information (Col. 8:25-60) to…1 (Col. 15:39-57, “This authorization request may be provided at a time when the customer 106 has requested to upload data [elements] 116 into the storage container 108,…”); and
2 ….
Redberg further discloses
1 … {uploading data elements to} cloud object storage for the one or more cloud platforms (Fig. 2, ¶ [0071], “Referring back to FIG. 2, according to one embodiment, storage module [cloud object storage] 212 is configured to store the [uploaded] files and/or other content [data elements], including but not limited to searchable encrypted files, within the one or more cloud platforms and/or containers of the cloud service providers 206 by one or more users 202 based on their respective policies.”; and ¶ [0045], “…clients 102 may send a copy of some data objects to a secondary storage computing device by utilizing one or more data agents.”);
2 downloading the one or more data elements that have been {classified as comprising personally identifiable information (Cervantez Col. 8:25-60)} from the cloud object storage to a database of the one or more cloud platforms (¶ [0045], “Clients 102 may also upload files, search for files or content therein, or even download files as and when desired, wherein during a copy, backup, archive or other storage operation, clients 102 may send a copy of some data objects to a secondary storage computing device [or cloud object storage/“storage module 212” of the “gateway 204”] by utilizing one or more data agents.”; and Fig. 2, ¶¶ [0049]-[0051], “Cloud [platform] service providers 206 may offer free, personal and/or business accounts providing hundreds or more of gigabytes of online [database] storage [to receive the downloaded data elements from the “gateway 204”/cloud object storage].”).
Regarding the combination of Cervantez and Redberg, the rationale to combine is the same as provided for claim 1 due to the overlapping subject matter of claims 1 and 14.
Regarding Independent Claims 15 and 18 and Dependent Claims 17 and 20
With respect to claims 15, 17-18 and 20, a corresponding reasoning as given earlier for independent claim 1 and dependent claim 14 applies, mutatis mutandis, to the subject matter of claims 15, 17-18 and 20. Therefore, claims 15, 17-18 and 20 are rejected, for similar reasons, under the grounds set forth for claims and 14.
B. Claims 4-7 and 21 are rejected under 35 U.S.C. 103 as being unpatentable over Cervantez in view of Redberg and Chickerur, and further in view of Mohanty (US 2023/0085559 “Mohanty”).
Regarding Claim 4
Cervantez-Redberg-Chickerur discloses the method of claim 1, and Cervantez further discloses
wherein the one or more machine learning models (Col. 8:25-60) comprise…1 to classify whether the one or more data elements comprise personally identifiable information (Col. 8:25-60, “Thus, the trained machine learning model(s) 122 may be configured with text recognition, image recognition, and other functionality to process [classify] unknown data 116 with various types of content. The class labels, in this case, may correspond to a classification of the unknown data 116 as a type of data among multiple different types of data corresponding to different sensitivity levels (e.g., medical data, PII [as a data element] or SPI data, publicly accessible data, etc.).”).
Cervantez-Redberg-Chickerur doesn’t disclose
1 … a neural network-based binary classification algorithm ...
Mohanty, however, discloses
1 … a neural network-based binary classification algorithm … (¶ [0068], “To this end, in some embodiments, survey request prediction module 106 includes a learning model (e.g., a dense neural network (DNN)) that is trained using machine learning techniques with a training dataset generated using historical survey request event-survey data. The DNN may be a binary classification model (e.g., a classifier).”)
Regarding the combination of Cervantez-Redberg-Chickerur and Mohanty, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cloud deployment system of Cervantez-Redberg-Chickerur to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the cloud deployment system of Cervantez-Redberg-Chickerur, upon which the claimed invention can be seen as an “improvement” through the use of a neural-network, binary classification feature;
2) the prior art contained a “comparable” system, namely the machine learning method of Mohanty, that has been improved in the same way as the claimed invention through the neural-network, binary classification feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the neural-network, binary classification feature to the base cloud deployment system of Cervantez-Redberg-Chickerur, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 5
Cervantez in view of Redberg, and further in view of Mohanty (“Cervantez-Redberg-Chickerur-Mohanty”) discloses the method of claim 4, and Cervantez further discloses
further comprising…1,
wherein respective ones of the plurality of data elements correspond to respective dependent variables indicating whether the respective ones of the plurality of data elements comprise personally identifiable information (8:25-9:17, “In some embodiments, the trained machine learning model(s) 122 may be configured to partition the storage containers [that store data elements] 108 into sub-containers based on the types of data [elements] stored therein, such as after classifying the data into different types of data at varying levels of sensitivity [involving PII]. For example, an individual storage container 108 may include data 116 of various different types [dependent variables], such as a first type [dependent variable] of data (e.g., marriage certificates), a second type [dependent variable] of data (e.g., birth certificates), a third type of [dependent variable] data (e.g., death certificates), and so on.”).
Mohanty further discloses
training a neural network of the neural network-based binary classification algorithm with training data comprising a plurality of data elements as independent variables (¶ [0068], “In some embodiments, a randomly selected portion of the training dataset [with the data elements as disclosed by Cervantez Col. 8:25-60] can be used for training the DNN [with the binary classification], and the remaining portion of the training dataset can be used as a testing dataset.”; and ¶ [0070], “Input layer 502 may be comprised of a number of neurons to match (i.e., equal to) the number of input variables [data elements] (independent variables). Taking as an example the independent variables illustrated in data structure 200 (FIG. 2 ), input layer 502 may include 8 neurons to match the 8 independent variables (e.g., event source 202, event type 204, event status 206, internal/external 208, destination 210, location 212, language 214, and mode 216), where each neuron in input layer 502 receives a respective independent variable.”),
Regarding the combination of Cervantez-Redberg-Chickerur and Mohanty, the rationale to combine is the same as provided for claim 4 due to the overlapping subject matter of claims 4 and 5.
Regarding Claim 6
Cervantez-Redberg-Chickerur-Mohanty discloses the method of claim 4, and Cervantez further discloses
wherein a neural network of the neural network-based binary classification algorithm (¶¶ [0068]-[0070]) comprises at least two hidden layers utilizing a rectified linear unit activation function (¶ [0072], “Each neuron in hidden layers [at least two] 504 and the neuron in output layer 506 may be associated with an activation function. For example, according to one embodiment, the activation function for the neurons in hidden layers 504 may be a rectified linear unit (ReLU) activation function.”).
Regarding the combination of Cervantez-Redberg-Chickerur and Mohanty, the rationale to combine is the same as provided for claim 4 due to the overlapping subject matter of claims 4 and 6.
Regarding Claim 7
Cervantez-Redberg-Chickerur-Mohanty discloses the method of claim 4, and Cervantez further discloses
wherein a neural network of the neural network-based binary classification algorithm comprises a plurality of nodes connected with each other (¶ [0069], “In brief, the DNN [dense neural network] includes an input layer for all input variables such as event source, event type, event status, internal/external, etc., multiple hidden layers for feature extraction, and an output layer. Each layer may be comprised of a number of nodes or units embodying an artificial neuron (or more simply a ‘neuron’).”; and ¶¶ [0073]-[0074], “Since this is a dense network, as can be seen in FIG. 5 , each neuron in the different layers may be coupled [connected] to one another. Each coupling (i.e., each interconnection) between two neurons...”), and
wherein respective ones of the connections comprise a weight factor and respective ones of the plurality of nodes comprise a bias factor (¶¶ [0071]-[0074], “Each neuron may then perform a linear calculation by combining the multiplication of each input variables (x1, x2, . . . ) with their weight factors and then adding the bias of the neuron.”).
Regarding the combination of Cervantez-Redberg-Chickerur and Mohanty, the rationale to combine is the same as provided for claim 4 due to the overlapping subject matter of claims 4 and 6.
Regarding Claim 21
With respect to dependent claim 21, a corresponding reasoning as given earlier for dependent claim 4 applies, mutatis mutandis, to the subject matter of claim 21. Therefore, claim 21 is rejected, for similar reasons, under the grounds set forth for claim 4.
C. Claims 8-11 are rejected under 35 U.S.C. 103 as being unpatentable over Cervantez in view of Redberg and Chickerur, and further in view of Engelberg et al. (US 2023/0328096, “Engelberg”).
Regarding Claim 8
Cervantez-Redberg-Chickerur discloses the method of claim 1, and Cervantez further discloses
further comprising storing,…1 , the one or more data elements that have been classified as comprising personally identifiable information,
2 …,
3 ….
Cervantez doesn’t disclose
1 …, in one or more relationship graphs,…
2 wherein the one or more relationship graphs comprise a plurality of relationships between a plurality of nodes,
3 wherein the plurality of relationships comprise edges of the one or more relationship graphs.
Engelberg, however, discloses
1 …, in one or more relationship graphs,… (¶ [0090], “Once the Risk-Process ontology is generated, the analytics component 204 is used for a data extraction step that consists of querying the ontology through the M2 constructs. The data extraction step returns a labeled property graph structure where each node represents an instance of an Element at Risk and each edge represents an instance of a Relation [to create a relationship graph]. The Risk and Importance values are then represented as vectorized properties of nodes and relationships respectively.”)
2 wherein the one or more relationship graphs comprise a plurality of relationships between a plurality of nodes (¶ [0090], “Once the Risk-Process ontology is generated, the analytics component 204 is used for a data extraction step that consists of querying the ontology through the M2 constructs. The data extraction step returns a labeled property graph structure where each node [of a plurality of nodes] represents an instance of an Element at Risk and each edge represents an instance of a Relation. The Risk and Importance values are then represented as vectorized properties of nodes and relationships respectively.”),
3 wherein the plurality of relationships comprise edges of the one or more relationship graphs (¶ [0090], “Once the Risk-Process ontology is generated, the analytics component 204 is used for a data extraction step that consists of querying the ontology through the M2 constructs. The data extraction step returns a labeled property graph structure where each node represents an instance of an Element at Risk and each edge [edges] represents an instance of a Relation [relationships]. The Risk and Importance values are then represented as vectorized properties of nodes and relationships respectively.”).
Regarding the combination of Cervantez-Redberg-Chickerur and Engelberg, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cloud deployment system of Cervantez-Redberg-Chickerur to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the cloud deployment system of Cervantez-Redberg-Chickerur, upon which the claimed invention can be seen as an “improvement” through the use of a graphing feature;
2) the prior art contained a “comparable” system, namely the cloud storage system of Engelberg, that has been improved in the same way as the claimed invention through the graphing feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the graphing feature to the base cloud deployment system of Cervantez-Redberg-Chickerur, and the results would have been predictable to one of ordinary skill in the art.
Regarding Claim 9
Cervantez in view of Redberg, and further in view of Engelberg (“Cervantez-Redberg-Chickerur-Engelberg”) discloses the method of claim 8, and Cervantez further discloses
wherein the {plurality of nodes (Engelberg ¶ [0090])} comprise the one or more data elements that have been classified as comprising personally identifiable information and one or more other data elements (Col. 8:25-60, “Thus, the trained machine learning model(s) 122 may be configured with text recognition, image recognition, and other functionality to process unknown data 116 with various types of content. The class labels [for classifying the data elements], in this case, may correspond to a classification of the unknown data 116 as a type of data among multiple different types of data corresponding to different sensitivity levels (e.g., medical data, PII [as a data element] or SPI data, publicly accessible data, etc.).”).
Regarding the combination of Cervantez-Redberg-Chickerur and Engelberg, the rationale to combine is the same as provided for claim 8 due to the overlapping subject matter of claims 8 and 9.
Regarding Claim 10
Cervantez-Redberg-Chickerur-Engelberg discloses the method of claim 8, and Engelberg further discloses
wherein the plurality of relationships (¶ [0090]) comprise interactions between respective pairs of the plurality of nodes (¶ [0090], “Once the Risk-Process ontology is generated, the analytics component 204 is used for a data extraction step that consists of querying the ontology through the M2 constructs. The data extraction step returns a labeled property graph structure where each node [plurality of nodes] represents an instance of an Element at Risk and each edge represents an instance of a Relation [relationships]. The Risk and Importance values are then represented as vectorized properties of [respective] nodes and relationships [interactions] respectively.”).
Regarding the combination of Cervantez-Redberg-Chickerur and Engelberg, the rationale to combine is the same as provided for claim 8 due to the overlapping subject matter of claims 8 and 10.
Regarding Claim 11
Cervantez-Redberg-Chickerur-Engelberg discloses the method of claim 8, and Engelberg further discloses
wherein the one or more relationship graphs (¶ [0090]) are in one of a resource description framework (RDF) format and a labeled property graph (LPG) format (¶ [0090], “Once the Risk-Process ontology is generated, the analytics component 204 is used for a data extraction step that consists of querying the ontology through the M2 constructs. The data extraction step returns a labeled property graph structure where each node [plurality of nodes] represents an instance of an Element at Risk and each edge represents an instance of a Relation [relationships]. The Risk and Importance values are then represented as vectorized properties of [respective] nodes and relationships [interactions] respectively.”).
Regarding the combination of Cervantez-Redberg-Chickerur and Engelberg, the rationale to combine is the same as provided for claim 8 due to the overlapping subject matter of claims 8 and 11.
D. Claims 13, 16, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Cervantez in view of Redberg and Chickerur, and further in view of Tan et al. (US 2018/0077219, “Tan”).
Regarding Claim 13
Cervantez-Redberg-Chickerur discloses the method of claim 1, and Cervantez further discloses
wherein {interfacing with one or more cloud platforms of the plurality of cloud platforms (Redberg Figs. 1 & 2, ¶¶ [0046], [0051])} comprises:
1 … upload the one or more data elements that have been classified as comprising personally identifiable information (Col. 8:25-60) to a cloud platform bucket (Col. 5:4-41, “That is, the network-based storage system [or cloud platform as disclosed by Redberg Fig. 2] 114 may provide and maintain logical storage units in the form of storage containers 108 (sometimes referred to as “buckets”) that are accessible to authorized computing devices 104 and/or authorized customers 106,…”; and Col. 15:39-57, “This authorization request may be provided at a time when the customer 106 has requested to upload data [elements] 116 into the storage container [bucket] 108,…”); and
2 ….
Redberg further discloses
2 using at least one of a designated application programming interface and a designated database adapter corresponding to a database of the one or more cloud platforms to download {the one or more data elements that have been classified as comprising personally identifiable information (Cervantez Col. 8:25-60)} from the cloud platform bucket to the database (Fig. 2, ¶¶ [0073]-[0074], “In sum, the generalized API module 218 allows [via using] a single standard thread to multiple users to connect any of their applications with the gateway 204 [possessing the cloud platform bucket as disclosed by Cervantez Col. 5:4-41] to perform any of storage, upload, retrieval, download, modify, search, and other allied functions at the cloud stores [database of a cloud platform].”).
Regarding the combination of Cervantez and Redberg, the rationale to combine is the same as provided for claim 1 due to the overlapping subject matter of claims 1 and 13.
Cervantez-Redberg-Chickerur doesn’t disclose
1 using a designated client library to upload…
Tan, however, discloses
1 using a designated client library to upload… (¶ [0024], “The uploader application 110 may use a protocol or application programming interface (API) provided by the cloud storage service provider, or a client or library software provided by the cloud storage service provider.”)
Regarding the combination of Cervantez-Redberg-Chickerur and Tan, it would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify the cloud deployment system of Cervantez-Redberg-Chickerur to arrive at the claimed invention. KSR establishes that a rationale for obviousness is proven by showing a “use of [a] known technique to improve similar devices in the same way.” See MPEP § 2143(I)(C).
To substantiate the conclusion of obviousness under this KSR rationale, the Examiner finds pursuant to MPEP § 2143(I)(C):
1) the prior art contained a base system, namely the cloud deployment system of Cervantez-Redberg-Chickerur, upon which the claimed invention can be seen as an “improvement” through the use of a library feature;
2) the prior art contained a “comparable” system, namely the cloud storage system of Tan, that has been improved in the same way as the claimed invention through the library feature; and
3) one of ordinary skill in the art could have applied the known improvement technique of applying the library feature to the base cloud deployment system of Cervantez-Redberg-Chickerur, and the results would have been predictable to one of ordinary skill in the art.
Regarding Dependent Claims 16 and 19
With respect to dependent claims 16 and 19, a corresponding reasoning as given earlier for dependent claim 13 applies, mutatis mutandis, to the subject matter of claims 16 and 19. Therefore, claims 16 and 19 are rejected, for similar reasons, under the grounds set forth for claim 13.
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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/D'Arcy Winston Straub/Primary Examiner, Art Unit 2491
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