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 . Claims 1-30 are presented in the case.
Priority
Applicant’s claim for the benefit of a prior-filed CIP application 15/728,486 filed on 10/09/2017. From the subject matter in the claims, claims 1, 3, 4, 5, 6, 11, 13 ,14, 15-16, 21, 23-24 and 26 appear in specification filed 01-31-2019. Claims 2, 7-10, 12, 17-20, 22, 27-30 appear in specification filed 03-11-2024.
Claim Objections
Claims 6-10, 16-20 and 26-30 are objected to because of the following informalities:
Claim 6, line 1 recites the phrase “the interaction attributes” which should be “interaction attributes”.
Claim 7, line 1 recites the phrase “the security recommendation” which should be “a security recommendation”.
Claim 8, line 1 recites the phrase “The method of claim 6” which should be “The method of claim 7”.
Claim 9, line 1 recites the phrase “the security recommendation” which should be “a security recommendation”.
Claim 10, line 1 recites the phrase “The method of claim 8” which should be “The method of claim 9”.
For the informalities above and wherever else they may occur appropriate correction is required.
Claims 1-30 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 (“2019 PEG”)
Claims 1, 11 and 21 have the following abstract idea analysis.
Step 1: The claim is directed to “a method, CRM and system" . The claims are directed to the statutory categories accordingly.
Step 2A Prong 1: claims recite the abstract idea limitations of "converting at least some of the pathnames into vectors comprising a plurality of features of hierarchical path components;". These limitations include mental concepts (act of evaluating. Mental processes – concepts performed in the human mind (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2)). "Examples of claims that recite mental processes include: • a claim to “collecting information, analyzing it, and displaying certain results of the collection and analysis,” where the data analysis steps are recited at a high level of generality such that they could practically be performed in the human mind". Also see analysis steps in USPTO example 47 noted as a mental process. The specification also provides example of organizing data into vectors. See USPGPUB ¶50. Other sections of the claims such as "a computer readable medium", "processors" "a predictive model", and "a collaboration recommendation" are advanced processes, too generic or high level to be listed as a mental process judicial exception given the available descriptions and MPEP comparisons.
Step 2A Prong 2: The judicial exceptions recited in these claims are not integrated into a practical application. Merely invoking "a computer readable medium", "processors" "a predictive model", and "a collaboration recommendation" do not yield eligibility. Claims are still in line with mental concepts such as claim 1, 11 and 21 are not specific to a practical application. The additional elements as such are servers and units do not include specialized hardware. See MPEP § 2106.05(f).
Claims 1, 11 and 21 do not include a particular field but even doing so may not be sufficient to overcome the abstract idea rejection. Merely applying an model to a field or data without an advancement in the new field or new hardware is ineligible. MPEP § 2106.05(h).
Step 2B: The claims do not contain significantly more than their judicial exceptions. Servers, units and other hardware are in their standard forms in the field. These additional elements are well-understood, routine, and conventional activity, see MPEP 2106.05(d)(II). Claims lacks any particular "how" or algorithm for a solution in a field in a novel way. Claims require more specificity on processes that would be incapable of simple mathematics, mental processes or use more substantial structure than conventional devices such as non-textbook implementations.
Regarding claims 2-10, 12-20 and 22-30, they merely narrows the previously recited abstract idea limitations with more abstract concepts and/or routine fundamental processes. For the reasons described above with respect to claims 2 and 4 this judicial exception is not meaningfully integrated into a practical application, or significantly more than the abstract idea. Abstract idea steps 1, 2A prong 1 and 2 remain the same as independent analysis above. See specification for more practical application concepts as none are seen in claims 1, 11 and 21.
With respect to step 2B These claims disclose similar limitations described for the independent claims above and do not provide anything significantly more than mathematical or mental concepts. Claims 2-10, 12-20 and 22-30 recite the additional elements of "wherein the collaboration recommendation comprises security recommendation for a file or folder. wherein the collaboration recommendation comprises at least one of, a folder, or a file. further comprising: recording one or more interaction attributes that correspond to user interaction events between users and content objects. wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings. wherein a plurality of features of hierarchical path components or the interaction attributes are codified in one or more feature vectors. wherein a plurality of filters are applied to generate the security recommendation. wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file. wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file. wherein the recommendation to turn sharing off or on for the file corresponds to movement of the file in or out of a shared folder or creating or closing a shared link for the file." These elements are more abstract concepts, generic applications to a field of use or well-understood, routine, conventional activity (see MPEP § 2106.05(d) and can't be simply appended to qualify as significantly more or being a practical application. What type of application, or structure of components beyond generic machine learning is still unknown for these claims. Therefore claims 2-10, 12-20 and 22-30 also recite abstract ideas that do not integrate into a practical application or amount to significantly more than the judicial exception, and are rejected under U.S.C. 101.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis 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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3, 4-6 11, 13, 14-16, 21, 23, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over Kayyoor et al. (US 10997499 B1) hereinafter Kayyoor in view of Green et al. (US 20180081503 A1) hereinafter Green.
As to independent claim 1, Kayyoor teaches a method, comprising:
gathering a set of pathnames; [gathers training data with tokens (col. 7 ln. 13-28) comprising a file path Col. 8 ln. 7-20 "create the first training file where each line is a sequence of tokens from multiple files accessed by the same user."]
converting at least some of the pathnames into vectors comprising a plurality of features of hierarchical path components; [generates vectors from tokens that describe file with path for training dataCol. 1 ln. 40-65 "where each vector within the first set of vectors represents a subset of the set of tokens that describes files that are frequently accessed by a common set of users"…"each token within the set of tokens may include a string derived from a file path "]
generating a predictive model from at least some of the vectors; and [generates a model from training data (has vectors) Col. 1 ln. 40-60 "training data including both a first set of vectors"…"(ii) training, using the set of training data, the machine learning model to define a set of latent features from the set of training data"]
Kayyoor does not specifically teach providing a collaboration recommendation from the predictive model.
However, Green teaches providing a collaboration recommendation from the predictive model. [recommends and predicts files (documents) for a user ¶32, based on interactions from others (collaboration) ¶44"document being related to an upcoming meeting on the user's calendar, another user having commented on, edited, or uploaded the document, or the document being related to other documents associated with the user account"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the file system analytics by Kayyoor by incorporating the providing a collaboration recommendation from the predictive model disclosed by Green because both techniques address the same field of machine learning and by incorporating Green into Kayyoor better assist users in selecting document and increases confidence [Green ¶32]
As to dependent claim 3, the rejection of claim 1 is incorporated, Kayyoor and Green further teach wherein the collaboration recommendation comprises at least one of, a folder, or a file. [Green recommends documents (files) Fig. 10 ¶32 "documents may be presented along with potential motives for the user to open the documents"]
As to dependent claim 4, the rejection of claim 1 is incorporated, Kayyoor and Green further teach recording one or more interaction attributes that correspond to user interaction events between users and content objects. [Green records motives based on comment interactions etc. ¶44"document being related to an upcoming meeting on the user's calendar, another user having commented on, edited, or uploaded the document, or the document being related to other documents associated with the user account"]
As to dependent claim 6, the rejection of claim 1 is incorporated, Kayyoor and Green further teach wherein a plurality of features of hierarchical path components or the interaction attributes are codified in one or more feature vectors. [Green vectors represent documents and contains values for events ¶57 " whether the document includes outstanding comments, whether the user uploaded, commented on, edited, or accessed the document, whether other users shared or suggested the document to the user, commented on the document, edited the document, or opened the document"]
As to independent claim 11, Kayyoor teaches a non-transitory computer readable medium having stored thereon a sequence of instructions which, when stored in memory and executed by one or more processors causes the one or more processors to perform a set of acts comprising: [medium with processor and memory Col. 4 ln. 58-67]
gathering a set of pathnames; [gathers training data with tokens (col. 7 ln. 13-28) comprising a file path Col. 8 ln. 7-20 "create the first training file where each line is a sequence of tokens from multiple files accessed by the same user."]
converting at least some of the pathnames into vectors comprising a plurality of features of hierarchical path components; [generates vectors from tokens that describe file with path for training dataCol. 1 ln. 40-65 "where each vector within the first set of vectors represents a subset of the set of tokens that describes files that are frequently accessed by a common set of users"…"each token within the set of tokens may include a string derived from a file path "]
generating a predictive model from at least some of the vectors; and [generates a model from training data (has vectors) Col. 1 ln. 40-60 "training data including both a first set of vectors"…"(ii) training, using the set of training data, the machine learning model to define a set of latent features from the set of training data"]
Kayyoor does not specifically teach providing a collaboration recommendation from the predictive model.
However, Green teaches providing a collaboration recommendation from the predictive model. [recommends and predicts files (documents) for a user ¶32, based on interactions from others (collaboration) ¶44"document being related to an upcoming meeting on the user's calendar, another user having commented on, edited, or uploaded the document, or the document being related to other documents associated with the user account"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the file system analytics by Kayyoor by incorporating the providing a collaboration recommendation from the predictive model disclosed by Green because both techniques address the same field of machine learning and by incorporating Green into Kayyoor better assist users in selecting document and increases confidence [Green ¶32]
As to dependent claim 13, the rejection of claim 11 is incorporated, Kayyoor and Green further teach wherein the collaboration recommendation comprises at least one of, a folder, or a file. [Green recommends documents (files) Fig. 10 ¶32 "documents may be presented along with potential motives for the user to open the documents"]
As to dependent claim 14, the rejection of claim 11 is incorporated, Kayyoor and Green further teach recording one or more interaction attributes that correspond to user interaction events between users and content objects. [Green records motives based on comment interactions etc. ¶44"document being related to an upcoming meeting on the user's calendar, another user having commented on, edited, or uploaded the document, or the document being related to other documents associated with the user account"]
As to dependent claim 16, the rejection of claim 11 is incorporated, Kayyoor and Green further teach wherein a plurality of features of hierarchical path components or the interaction attributes are codified in one or more feature vectors. [Green vectors represent documents and contains values for events ¶57 " whether the document includes outstanding comments, whether the user uploaded, commented on, edited, or accessed the document, whether other users shared or suggested the document to the user, commented on the document, edited the document, or opened the document"]
As to independent claim 21, Kayyoor teaches a system, comprising: [system Col. 4 ln. 58-67]
a storage medium having stored thereon a sequence of instructions; and [medium with instruct Col. 4 ln. 58-67]
gathering a set of pathnames; [gathers training data with tokens (col. 7 ln. 13-28) comprising a file path Col. 8 ln. 7-20 "create the first training file where each line is a sequence of tokens from multiple files accessed by the same user."]
converting at least some of the pathnames into vectors comprising a plurality of features of hierarchical path components; [generates vectors from tokens that describe file with path for training dataCol. 1 ln. 40-65 "where each vector within the first set of vectors represents a subset of the set of tokens that describes files that are frequently accessed by a common set of users"…"each token within the set of tokens may include a string derived from a file path "]
generating a predictive model from at least some of the vectors; and [generates a model from training data (has vectors) Col. 1 ln. 40-60 "training data including both a first set of vectors"…"(ii) training, using the set of training data, the machine learning model to define a set of latent features from the set of training data"]
Kayyoor does not specifically teach providing a collaboration recommendation from the predictive model.
However, Green teaches providing a collaboration recommendation from the predictive model. [recommends and predicts files (documents) for a user ¶32, based on interactions from others (collaboration) ¶44"document being related to an upcoming meeting on the user's calendar, another user having commented on, edited, or uploaded the document, or the document being related to other documents associated with the user account"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the file system analytics by Kayyoor by incorporating the providing a collaboration recommendation from the predictive model disclosed by Green because both techniques address the same field of machine learning and by incorporating Green into Kayyoor better assist users in selecting document and increases confidence [Green ¶32]
As to dependent claim 23, the rejection of claim 21 is incorporated, Kayyoor and Green further teach wherein the collaboration recommendation comprises at least one of, a folder, or a file. [Green recommends documents (files) Fig. 10 ¶32 "documents may be presented along with potential motives for the user to open the documents"]
As to dependent claim 24, the rejection of claim 21 is incorporated, Kayyoor and Green further teach recording one or more interaction attributes that correspond to user interaction events between users and content objects. [Green records motives based on comment interactions etc. ¶44"document being related to an upcoming meeting on the user's calendar, another user having commented on, edited, or uploaded the document, or the document being related to other documents associated with the user account"]
As to dependent claim 26, the rejection of claim 21 is incorporated, Kayyoor and Green further teach wherein a plurality of features of hierarchical path components or the interaction attributes are codified in one or more feature vectors. [Green vectors represent documents and contains values for events ¶57 " whether the document includes outstanding comments, whether the user uploaded, commented on, edited, or accessed the document, whether other users shared or suggested the document to the user, commented on the document, edited the document, or opened the document"]
Claims 2, 12 and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Kayyoor in view of Green as applied in the rejection of claims 1, 11 and 21 above, and further in view of Larson et al. (US 20180189517 A1) hereinafter Larson.
As to dependent claim 2, Kayyoor and Green teach the method of claim 1 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the collaboration recommendation comprises security recommendation for a file or folder.
However, Larson teaches wherein the collaboration recommendation comprises security recommendation for a file or folder. [suggests a policy (security recommendation) ¶5 ". Based on the analysis, the security and compliance module may suggest a policy or a configuration change to be implemented based on the analysis"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendations disclosed by Kayyoor and Green by incorporating the wherein the collaboration recommendation comprises security recommendation for a file or folder disclosed by Larson because all techniques address the same field of analytics and by incorporating Larson into Kayyoor and Green better personalizes recommendations to users with more effective performance monitoring [Larson ¶18]
As to dependent claim 12, Kayyoor and Green teach the method of claim 11 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the collaboration recommendation comprises security recommendation for a file or folder.
However, Larson teaches wherein the collaboration recommendation comprises security recommendation for a file or folder. [suggests a policy (security recommendation) ¶5 ". Based on the analysis, the security and compliance module may suggest a policy or a configuration change to be implemented based on the analysis"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendations disclosed by Kayyoor and Green by incorporating the wherein the collaboration recommendation comprises security recommendation for a file or folder disclosed by Larson because all techniques address the same field of analytics and by incorporating Larson into Kayyoor and Green better personalizes recommendations to users with more effective performance monitoring [Larson ¶18]
As to dependent claim 22, Kayyoor and Green teach the method of claim 21 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the collaboration recommendation comprises security recommendation for a file or folder.
However, Larson teaches wherein the collaboration recommendation comprises security recommendation for a file or folder. [suggests a policy (security recommendation) ¶5 ". Based on the analysis, the security and compliance module may suggest a policy or a configuration change to be implemented based on the analysis"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendations disclosed by Kayyoor and Green by incorporating the wherein the collaboration recommendation comprises security recommendation for a file or folder disclosed by Larson because all techniques address the same field of analytics and by incorporating Larson into Kayyoor and Green better personalizes recommendations to users with more effective performance monitoring [Larson ¶18]
Claims 5, 15 and 25 are rejected under 35 U.S.C. 103 as being unpatentable over Kayyoor in view of Green as applied in the rejection of claims 1, 11 and 21 above, and further in view of Corrado et al. (US 9141916 B1) hereinafter Corrado.
As to dependent claim 5, Kayyoor and Green teach the method of claim 1 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings.
However, Corrado teaches wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings. [trains a classifier with training data and uses embedding functions (Col. 7 ln. 26-38) "training process to train the logistic regression classifier (step 404) using the specified initial values for the parameters and a set of training data"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Kayyoor and Green by incorporating wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings disclosed by Corrado because all techniques address the same field of machine learning and by incorporating Corrado into Kayyoor and Green improve the performance of existing models when predicting labels [Corrado Col. 2-3 ln. 59-3]
As to dependent claim 15, Kayyoor and Green teach the method of claim 11 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings.
However, Corrado teaches wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings. [trains a classifier with training data and uses embedding functions (Col. 7 ln. 26-38) "training process to train the logistic regression classifier (step 404) using the specified initial values for the parameters and a set of training data"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Kayyoor and Green by incorporating wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings disclosed by Corrado because all techniques address the same field of machine learning and by incorporating Corrado into Kayyoor and Green improve the performance of existing models when predicting labels [Corrado Col. 2-3 ln. 59-3]
As to dependent claim 25, Kayyoor and Green teach the method of claim 21 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings.
However, Corrado teaches wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings. [trains a classifier with training data and uses embedding functions (Col. 7 ln. 26-38) "training process to train the logistic regression classifier (step 404) using the specified initial values for the parameters and a set of training data"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the models disclosed by Kayyoor and Green by incorporating wherein the predictive model is generated based at least in part on generating a classifier from both training data and embeddings disclosed by Corrado because all techniques address the same field of machine learning and by incorporating Corrado into Kayyoor and Green improve the performance of existing models when predicting labels [Corrado Col. 2-3 ln. 59-3]
Claims 7, 17 and 27 are rejected under 35 U.S.C. 103 as being unpatentable over Kayyoor in view of Green as applied in the rejection of claims 1, 11 and 21 above, and further in view of Redlich et al. (US 9734169 B2) hereinafter Redlich.
As to dependent claim 7, Kayyoor and Green teach the method of claim 1 above that is incorporated,
Kayyoor and Green do not specifically teach wherein a plurality of filters is applied to generate the security recommendation.
However, Redlich teaches wherein a plurality of filters is applied to generate the security recommendation. [filters for content selection including secure and sensitive data Col. 3 ln. 37-41, Col. 4 ln. 27-37 "select content data stores for respective ones of a plurality of enterprise designated categorical filters"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein a plurality of filters is applied to generate the security recommendation disclosed by Redlich because all techniques address the same field of semantic analysis and by incorporating Redlich into Kayyoor and Green better ensures information is properly handled for improved classification [Redlich Col. 2-3 ln. 56-3].
As to dependent claim 17, Kayyoor and Green teach the method of claim 11 above that is incorporated,
Kayyoor and Green do not specifically teach wherein a plurality of filters is applied to generate the security recommendation.
However, Redlich teaches wherein a plurality of filters is applied to generate the security recommendation. [filters for content selection including secure and sensitive data Col. 3 ln. 37-41, Col. 4 ln. 27-37 "select content data stores for respective ones of a plurality of enterprise designated categorical filters"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein a plurality of filters is applied to generate the security recommendation disclosed by Redlich because all techniques address the same field of semantic analysis and by incorporating Redlich into Kayyoor and Green better ensures information is properly handled for improved classification [Redlich Col. 2-3 ln. 56-3].
As to dependent claim 27, Kayyoor and Green teach the method of claim 21 above that is incorporated,
Kayyoor and Green do not specifically teach wherein a plurality of filters is applied to generate the security recommendation.
However, Redlich teaches wherein a plurality of filters is applied to generate the security recommendation. [filters for content selection including secure and sensitive data Col. 3 ln. 37-41, Col. 4 ln. 27-37 "select content data stores for respective ones of a plurality of enterprise designated categorical filters"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein a plurality of filters is applied to generate the security recommendation disclosed by Redlich because all techniques address the same field of semantic analysis and by incorporating Redlich into Kayyoor and Green better ensures information is properly handled for improved classification [Redlich Col. 2-3 ln. 56-3].
Claims 8, 18 and 28 are rejected under 35 U.S.C. 103 as being unpatentable over Kayyoor in view of Green as applied in the rejection of claims 6, 17 and 27 above, and further in view of Turbin et al. (US 20120192273 A1) hereinafter Turbin.
As to dependent claim 8, Kayyoor and Green teach the method of claim 6 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file.
However, Turbin teaches wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file. [staged step checks (filters) for paths ¶33 filename, size, timestamps ¶37 and content (header) ¶42, Fig. 3]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file by Turbin because all techniques address the same field of semantic analysis and by incorporating Turbin into Kayyoor and Green provides a more efficient analysis avoiding data storage problems [Turbin ¶6-7].
As to dependent claim 18, Kayyoor and Green teach the method of claim 17 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file.
However, Turbin teaches wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file. [staged step checks (filters) for paths ¶33 filename, size, timestamps ¶37 and content (header) ¶42, Fig. 3]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file by Turbin because all techniques address the same field of semantic analysis and by incorporating Turbin into Kayyoor and Green provides a more efficient analysis avoiding data storage problems [Turbin ¶6-7].
As to dependent claim 28, Kayyoor and Green teach the method of claim 276 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file.
However, Turbin teaches wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file. [staged step checks (filters) for paths ¶33 filename, size, timestamps ¶37 and content (header) ¶42, Fig. 3]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein the plurality of filters comprises a first level of filtering that analyze pathnames, a second level of filtering that analyzes filename metadata from within a file, and a third level of filtering that analyze content from within the file by Turbin because all techniques address the same field of semantic analysis and by incorporating Turbin into Kayyoor and Green provides a more efficient analysis avoiding data storage problems [Turbin ¶6-7].
Claims 9-10, 19-20 and 29-30 are rejected under 35 U.S.C. 103 as being unpatentable over Kayyoor in view of Green as applied in the rejection of claim 1, 11 and 21 above, and further in view of Rajput et al. (US 10803188 B1) hereinafter Rajput.
As to dependent claim 9, Kayyoor and Green teach the method of claim 1 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file.
However, Rajput teaches wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file. [security actions including blocking sharing or not (sharing off) Col. 2 ln. 5-10 "The security action may include at least one of blocking sharing the sensitive information with the application, permitting sharing the sensitive information with the application,"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file by Rajput because all techniques address the same field of sharing data and by incorporating Rajput into Kayyoor and Green improves security and avoids unintentional sharing of content [Rajput Col. 1 ln. 28-44].
As to dependent claim 10, the rejection of claim 9 is incorporated, Kayyoor, Green and Rajput further teach wherein the recommendation to turn sharing off or on for the file corresponds to movement of the file in or out of a shared folder or creating or closing a shared link for the file. [Rajput provides website lines (shared links) Col. 1 ln. 65]
As to dependent claim 19, Kayyoor and Green teach the method of claim 11 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file.
However, Rajput teaches wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file. [security actions including blocking sharing or not (sharing off) Col. 2 ln. 5-10 "The security action may include at least one of blocking sharing the sensitive information with the application, permitting sharing the sensitive information with the application,"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file by Rajput because all techniques address the same field of sharing data and by incorporating Rajput into Kayyoor and Green improves security and avoids unintentional sharing of content [Rajput Col. 1 ln. 28-44].
As to dependent claim 20, the rejection of claim 19 is incorporated, Kayyoor, Green and Rajput further teach wherein the recommendation to turn sharing off or on for the file corresponds to movement of the file in or out of a shared folder or creating or closing a shared link for the file. [Rajput provides website lines (shared links) Col. 1 ln. 65]
As to dependent claim 29, Kayyoor and Green teach the method of claim 21 above that is incorporated,
Kayyoor and Green do not specifically teach wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file.
However, Rajput teaches wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file. [security actions including blocking sharing or not (sharing off) Col. 2 ln. 5-10 "The security action may include at least one of blocking sharing the sensitive information with the application, permitting sharing the sensitive information with the application,"]
Accordingly, it would have been obvious to a person of ordinary skill in the art before the effective filling date of the claimed invention to modify the recommendation disclosed by Kayyoor and Green by incorporating wherein the security recommendation corresponds to a recommendation to turn sharing off or on for a file by Rajput because all techniques address the same field of sharing data and by incorporating Rajput into Kayyoor and Green improves security and avoids unintentional sharing of content [Rajput Col. 1 ln. 28-44].
As to dependent claim 30, the rejection of claim 29 is incorporated, Kayyoor, Green and Rajput further teach wherein the recommendation to turn sharing off or on for the file corresponds to movement of the file in or out of a shared folder or creating or closing a shared link for the file. [Rajput provides website lines (shared links) Col. 1 ln. 65]
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Applicant is required under 37 C.F.R. § 1.111(c) to consider these references fully when responding to this action.
Dong et al. (US 20200142973 A1) teaches recommending a target location when relocating a file. In the method, a source file path of a file is obtained. A target starting directory in a target directory system to which the file is to be relocated is determined based on the source file path. (see ¶3)
It is noted that any citation to specific pages, columns, lines, or figures in the prior art references and any interpretation of the references should not be considered to be limiting in any way. A reference is relevant for all it contains and may be relied upon for all that it would have reasonably suggested to one having ordinary skill in the art. In re Heck, 699 F.2d 1331, 1332-33, 216 U.S.P.Q. 1038, 1039 (Fed. Cir. 1983) (quoting In re Lemelson, 397 F.2d 1006, 1009, 158 U.S.P.Q. 275, 277 (C.C.P.A. 1968)).
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/BEAU D SPRATT/Primary Examiner, Art Unit 2143
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