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 Arguments
The present action is responsive to communications that was filed on 05/16/2025. Claims 1, 3, 12, 13, and 20 have been amended. Claims 2, 4, and 14 has been cancelled. Claims 21-23 were added. Claims 1,3, 5-13, 15-23 are currently pending.
Applicant’s arguments, filed 05/16/2025 , with respect to the rejections of claims 1-3, 5-7, 9, 11-13, 19 and 20 under Hebenthal et al. (US PGPub No.20200320250-A1) in view of Vikramaratne et al. (US PGPub No.20170126631-A1) have been fully considered and are persuasive. Therefore, the rejection has been withdrawn. However, upon further consideration, a new grounds of rejection is made in additional view of Goyal et al. (US PGPub No. 20200057843-A1), Larson et al. (US PG Pub No. 20230122684-A1 ), Baek et al. (US PGPub No. 20180189369-A1) and Kamata et al. (US PGPub No. 20170124347-A1 ).
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.
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.
Claims 1, 3 , 5-9, 11-13, 15-17, 19, 20, and 22 are rejected under 35 U.S.C. 103 as being unpatentable over Vikramaratne et al. (US PGPub No.20170126631-A1) in view of Goyal (US PGPub No. US-20200057843-A1).
With respect to claim 1,Vikramaratne teaches a method comprising: generating, by a computing device, different variations of text based on a source document, the different variations to convey the same meaning as the source document while including content different than that of the source document; (¶0005: More specifically, the present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for sure shared documents using dynamic natural language stenography. Embodiments operate within systems in a cloud-based environment, wherein one or more servers are configured to interface with storage devices that store objects accessible by one or more users. A process receives an electronic message comprising a user request to access an object. Before providing user access to the object, the system generates a requestor-specific steganographic message wherein the steganographic message is derived from some portion of requestor identification or user attributes. Various forms of a requestor-specific steganographic message are applied to the object to generate a requestor-specific protected object (generating different variations of text based on a source document) , which is then provided to the requestor.).
generating, by the computing device, copies of the document that include at least one of the different variations of the text, so that individual copies of the document are traceable based on the different variation of the text included within that copy of the document, (¶0025-0026: In one or more embodiments, the techniques described herein further discuss: (7) receiving a disclosed portion of the protected object; (8) identifying the existence and bounds of a protected object from the disclosed portion to recover at least a portion of a steganographic message (e.g., a portion of the steganographic message that was applied to the object when generating the protected object); and (9) determining the request attributes (e.g., user ID) included in the recovered steganographic message (different variations of the text included within that copy of the document) .).
determining, by the computing device, a recipient of a copy of the document based on a different variation of the text included with the copy. (¶0037-0039: A process receives an electronic message comprising a user request to access an object. Before providing user access to the object, the system generates a requestor-specific steganographic message wherein the steganographic message is derived from some portion of requestor identification or user attributes. Various forms of a requestor-specific steganographic message are applied to the object to generate a requestor-specific protected object (copy with different variation) , which is then provided to the requestor. A web crawler can identify posted unauthorized protected object disclosures. Using requestor-specific aspects (different variation of the text) of the embedded steganographic message, the source of the unauthorized disclosure can be identified (recipient of copy can be identified e.g., determined)).
Vikramaratne does not disclose:
wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document;
However, Goyal teaches, wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document; and (¶0070: The natural processing (NLP) may use machine learning to find insights and relationships in text. The text analysis may identify semantics differencing elements, including key phrases, places, people, brands, or events, and may determine to leave certain text unaltered or add consistencies or inconsistencies as to how certain terms are presented in each file. For example, for a text file service can apply NLP technique to generate a clone copy by determining semantic differencing elements for inserting/removing additional punctuation marks, using synonyms, and expanding/shortening phrases like “is not” to “isn't” and vice versa.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a natural language processing (NLP) service to the method of Vikramaratne in order to manage file without compromising security (Goyal ¶0001-0007).
With respect to claim 3, the combination of Vikramaratne in view of Goyal teaches the method of claim 1 (see rejection of claim 1 above), wherein the request to an NLP service, the request includes the at least a portion of the source document and an instruction for generating one or more variants of the source document. (Goyal ¶0067-0069: Each clone file may convey the same information with small semantic differences incorporated to make each file unique to each user. While sharing any file via file sharing system, such as SHAREFILE® by Citrix Systems, each unique copy may be generated for each person using natural-language processing (NLP) and machine learning (ML) techniques. After a file is uploaded to the file sharing system, a natural language processing function 332A may begin semantic file analysis in S502 to determine file segments for further processing. The natural language processing may determine a plurality of opportunities to alter a file in ways that not obvious to a user. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a natural language processing (NLP) service to the method of Vikramaratne in order to manage file without compromising security (Goyal ¶0001-0007).
With respect to claim 5, the combination of Vikramaratne in view of Goyal teaches the method of claim 3 (see rejection of claim 3 above), wherein the request further includes one or more examples of variants of the source document. (Vikramaratne: ¶0068: As seen in Figure 4A, for each content partition, modification candidates can be identified (see step 410). In some cases, the request attributes 172 and/or the watermarking rules 174 can be used to determine the modification candidates. The variation possibilities for the modification candidates can then be determined (see step 412). For example, an NLP library 424 can be used to identify variations (e.g., synonyms, syntactic transformations, semantic transformations, etc.) that might be used to carry an encoded a steganographic message.
With respect to claim 6, the combination of Vikramaratne in view of Goyal teaches the method of claim 1 (see rejection of claim 1 above), further comprising: outputting, by the computing device, the copies of the document at one or more times; and (Vikramaratne: ¶0057-0058 & 0069:As seen in Figure 4A, the protected object (copy of the document) comprising dynamically generated natural language steganography is then ready to be published (outputting) for access by the user (see step 422).).
storing information indicating the time at which individual copies of the document were output by the computing device, (Vikramaratne: ¶0071: In one or more embodiments the information recorded in the watermarking logs 186 as described herein can be used to determine and/or predict the source of a disclosed portion of one or more protected objects. ¶0076: As seen in Figure 4B1, The watermarking log entries corresponding to the published protected object having the highest probability matches to the recovered object can then be used to determine various attributes related to the request that generated the published protected object. Specifically, the UserID, DeviceID, location, time, and other request attributes can be extracted from the watermarking logs 186 to assist in identification of the source or sources of unauthorized disclosure of the protected object.).
wherein the determining the recipient of a copy of the document includes determining a time at which the copy of the document was output by the computing device using the stored information. (Vikramaratne: ¶0076: As such, when processing the protected objects, it is possible to use watermarking log entries to facilitate extracting one or more user identities from the watermarking log (see step 450)).
With respect to claim 7, the combination of Vikramaratne in view of Goyal teaches the method of claim 6 (see rejection of claim 6 above), wherein the outputting of the copies of the document is in response to one or more requests for the source document. (Vikramaratne: ¶0037: As seen in Figure 1B, For example, a shared protected movie script having steganographic messages (requests) encoded in a natural language watermark is shown in workspace 122.sub.1. Also, a shared protected product specification having steganographic messages encoded in a natural language watermark is shown in workspace 122.sub.2. As further shown, the collaborators 118 can view and/or download such protected objects. (outputting copies of the document)).
With respect to claim 8, the combination of Vikramaratne in view of Goyal teaches the method of claim 6 (see rejection of claim 6 above), wherein the outputting of the copies of the document is in response to an input of a word processing application. (Goyal: ¶0025: As a general introduction to the subject matter described in more detail below, aspects described herein are directed towards controlling shared documents in an enterprise computing system using managed applications at computing devices. A document manager may perform file analysis for intelligent watermarking and generate clone copies of a file to be shared. )
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a word processing application to the method of Vikramaratne in order to deter leaking of files over a cloud or network (Goyal ¶0025).
With respect to claim 9, the combination of Vikramaratne in view of Goyal teaches the method of claim 1 (see rejection of claim 1 above), further comprising: sending, by the computing device, the copies of the document to one or more other computing devices; and (Vikramaratne: ¶0031-0032: As seen in Figure 1A, Any of the users can access shared content from the cloud-based shared content storage system 101 without the additional process of manually downloading and storing a file locally on an instance of the user devices 102 (e.g., smart phone 102.sub.1, tablet 102.sub.2, IP phone 102.sub.3, laptop 102.sub.4, workstation 102.sub.5, laptop 102.sub.6, etc.). Any of the users can access shared content from the cloud-based shared content storage system 101 without the additional process of manually downloading and storing a file locally on an instance of the user devices 102 (e.g., smart phone 102.sub.1, tablet 102.sub.2, IP phone 102.sub.3, laptop 102.sub.4, workstation 102.sub.5, laptop 102.sub.6, etc.).).
storing information for determining which individual copies of the document were provided to which of the one or more other computing devices, (Vikramaratne: ¶0037: As seen in Figure 4A, The resulting protected version of the movie script is uniquely associated with the given request and can be published to the web application 203.sub.2 for access by the user collaborator 123.sub.2. As an example, the steganographic message and watermarking applied can be based, in part, on the request attributes 172 (e.g., UserID, FileID, etc.), storage parameters 17, and the watermarking rules 174, any combinations of which can be stored in the security data 254. A record of the published protected object, and its relationships to the request attributes 172, storage parameters 171, and watermarking rules 174, can be codified in the watermarking logs 186 and possibly stored in the security data 254.).
wherein the determining the recipient of a copy of the document includes determining which of the one or more other computing devices the copy of the document was provided to using the stored information. (Vikramaratne: ¶0046: As seen in Figure 1A, the collaboration server 152 can further interface with the watermarking proxy 252 to assist in secure sharing and tracking of the sensitive movie script object. The creator collaborator 125.sub.2 and the user collaborator 123.sub.2 can interact with the cloud-based shared content storage service using web applications (e.g., web application 203.sub.1, web application 203.sub.2, etc.) that operate on various instances of user devices 102 (e.g., user device 102.sub.8, user device 102.sub.9, etc.), which are in communication with one or more servers (e.g., servers in a cloud-based shared content storage system) ¶0073: In one or more embodiments, the steps and underlying operations shown in Figure 4B1 can be executed by the various environments and systems (e.g., collaboration server 152) described herein. As shown, leak source identification technique 4B100 further references the security data 254 comprising certain watermarking logs 186. ).
With respect to claim 11, the combination of Vikramaratne in view of Goyal teaches the method of claim 1 (see rejection of claim 1 above) wherein the determining the recipient of a copy of the document includes determining the recipient using an excerpt of the copy of the document. (Vikramaratne: ¶0074-0075: As seen in Figure 4B1, the leak source identification technique 4B100 starts with uploading a recovered disclosed protected object (see step 432). For example, an admin 124.sub.3 might invoke the leak source identification technique 4B 100 at the security management interface 256. The FileID of the recovered protected object can be determined (see step 434) using various techniques (e.g., admin 124.sub.3 specifies the FileID, crawler service, etc.). The partitions and/or segments of the recovered protected object are then determined (see step 436). For example, the disclosing party might have removed and/or rearranged various portions of the document to obfuscate the disclosure source. An iterative process comparing the recovered protected object to the set of published protected objects can begin by selecting a first published protected object (see step 440). A document distance (e.g., an edit distance referring to a total number of keystrokes to affect a modification) is calculated so as to provide a quantitative difference between the recovered protected object and the selected published protected object (see step 442)).
With respect to claim 12, Vikramaratne teaches an apparatus comprising: a processor; and a non-volatile memory storing computer program code that when executed on the processor causes the processor to execute a process comprising: ( ¶0093: Figure 7A depicts a block diagram of an instance of a computer system 7A00 suitable for implementing embodiments of the present disclosure. Computer system 7A00 includes a bus 706 or other communication mechanism for communicating information. The bus interconnects subsystems and devices such as a central processing unit (CPU), or a multi-core CPU (e.g., data processor 707), a system memory (e.g., main memory 708, or an area of random access memory (RAM)), a non-volatile storage device or non-volatile storage area (e.g., read-only memory 709), an internal or external storage device 710 (e.g., magnetic or optical), a data interface 733, a communications interface 714 (e.g., PHY, MAC, Ethernet interface, modem, etc.).).
generating different variations of text based on a source document, the different variations to convey the same meaning as the source document while including content different than that of the source document; (¶0005: More specifically, the present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for sure shared documents using dynamic natural language stenography. Embodiments operate within systems in a cloud-based environment, wherein one or more servers are configured to interface with storage devices that store objects accessible by one or more users. A process receives an electronic message comprising a user request to access an object. Before providing user access to the object, the system generates a requestor-specific steganographic message wherein the steganographic message is derived from some portion of requestor identification or user attributes. Various forms of a requestor-specific steganographic message are applied to the object to generate a requestor-specific protected object (generating different variations of text based on a source document) , which is then provided to the requestor.).
generating copies of the document that include at least one of the different variations of the text, so that individual copies of the document are traceable based on the different variation of the text included within that copy of the document, (¶0025-0026: In one or more embodiments, the techniques described herein further discuss: (7) receiving a disclosed portion of the protected object; (8) identifying the existence and bounds of a protected object from the disclosed portion to recover at least a portion of a steganographic message (e.g., a portion of the steganographic message that was applied to the object when generating the protected object); and (9) determining the request attributes (e.g., user ID) included in the recovered steganographic message (different variations of the text included within that copy of the document) .).
determining a recipient of a copy of the document based on a different variation of the text included with the copy. (¶0037-0039: A process receives an electronic message comprising a user request to access an object. Before providing user access to the object, the system generates a requestor-specific steganographic message wherein the steganographic message is derived from some portion of requestor identification or user attributes. Various forms of a requestor-specific steganographic message are applied to the object to generate a requestor-specific protected object (copy with different variation) , which is then provided to the requestor. A web crawler can identify posted unauthorized protected object disclosures. Using requestor-specific aspects (different variation of the text) of the embedded steganographic message, the source of the unauthorized disclosure can be identified (recipient of copy can be identified e.g., determined)).
Vikramaratne does not disclose:
wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document.
However, Goyal teaches wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document; and (¶0070: The natural processing (NLP) may use machine learning to find insights and relationships in text. The text analysis may identify semantics differencing elements, including key phrases, places, people, brands, or events, and may determine to leave certain text unaltered or add consistencies or inconsistencies as to how certain terms are presented in each file. For example, for a text file service can apply NLP technique to generate a clone copy by determining semantic differencing elements for inserting/removing additional punctuation marks, using synonyms, and expanding/shortening phrases like “is not” to “isn't” and vice versa.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a natural language processing (NLP) service to the method of Vikramaratne in order to manage file without compromising security (Goyal ¶0001-0007).
With respect to claim 13, the combination of Vikramaratne in view of Goyal teaches method of claim 12 (see rejection of claim 12 above), wherein the generating of the different variations of text includes sending a request to a natural language processing (NLP) service, the request includes the at least a portion of the source document and an instruction for generating one or more variants of the source document. (Goyal ¶0067-0069: Each clone file may convey the same information with small semantic differences incorporated to make each file unique to each user. While sharing any file via file sharing system, such as SHAREFILE® by Citrix Systems, each unique copy may be generated for each person using natural-language processing (NLP) and machine learning (ML) techniques. After a file is uploaded to the file sharing system, a natural language processing function 332A may begin semantic file analysis in S502 to determine file segments for further processing. The natural language processing may determine a plurality of opportunities to alter a file in ways that not obvious to a user. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a natural language processing (NLP) service to the method of Vikramaratne in order to manage file without compromising security (Goyal ¶0001-0007).
With respect to claim 15, the combination of Vikramaratne in view of Goyal teaches method of claim 13 (see rejection of claim 13 above), wherein the request further includes one or more examples of variants of the source document. (Vikramaratne: ¶0068: As seen in Figure 4A, for each content partition, modification candidates can be identified (see step 410). In some cases, the request attributes 172 and/or the watermarking rules 174 can be used to determine the modification candidates. The variation possibilities for the modification candidates can then be determined (see step 412). For example, an NLP library 424 can be used to identify variations (e.g., synonyms, syntactic transformations, semantic transformations, etc.) that might be used to carry an encoded a steganographic message.
With respect to claim 16, the combination of Vikramaratne in view of Goyal teaches method of claim 15 (see rejection of claim 15 above), wherein the process further includes: outputting the copies of the document at one or more times; (Vikramaratne: ¶0057-0058 & 0069:As seen in Figure 4A, the protected object (copy of the document) comprising dynamically generated natural language steganography is then ready to be published (outputting) for access by the user (see step 422).).
and storing information indicating the time at which individual copies of the document were output, (Vikramaratne: ¶0071: In one or more embodiments the information recorded in the watermarking logs 186 as described herein can be used to determine and/or predict the source of a disclosed portion of one or more protected objects. ¶0076: As seen in Figure 4B1, The watermarking log entries corresponding to the published protected object having the highest probability matches to the recovered object can then be used to determine various attributes related to the request that generated the published protected object. Specifically, the UserID, DeviceID, location, time, and other request attributes can be extracted from the watermarking logs 186 to assist in identification of the source or sources of unauthorized disclosure of the protected object.).
wherein the determining the recipient of a copy of the document includes determining a time at which the copy of the document was output using the stored information. (Vikramaratne: ¶0076: As such, when processing the protected objects, it is possible to use watermarking log entries to facilitate extracting one or more user identities from the watermarking log (see step 450).
With respect to claim 17, the combination of Vikramaratne in view of Goyal teaches method of claim 12 (see rejection of claim 12 above) wherein the process further includes: sending the copies of the document to one or more computing devices; and (Vikramaratne: ¶0031-0032: As seen in Figure 1A, Any of the users can access shared content from the cloud-based shared content storage system 101 without the additional process of manually downloading and storing a file locally on an instance of the user devices 102 (e.g., smart phone 102.sub.1, tablet 102.sub.2, IP phone 102.sub.3, laptop 102.sub.4, workstation 102.sub.5, laptop 102.sub.6, etc.). Any of the users can access shared content from the cloud-based shared content storage system 101 without the additional process of manually downloading and storing a file locally on an instance of the user devices 102 (e.g., smart phone 102.sub.1, tablet 102.sub.2, IP phone 102.sub.3, laptop 102.sub.4, workstation 102.sub.5, laptop 102.sub.6, etc.).).
storing information for determining which individual copies of the document were provided to which of the one or more computing devices, , (Vikramaratne: ¶0037: As seen in Figure 4A, The resulting protected version of the movie script is uniquely associated with the given request and can be published to the web application 203.sub.2 for access by the user collaborator 123.sub.2. As an example, the steganographic message and watermarking applied can be based, in part, on the request attributes 172 (e.g., UserID, FileID, etc.), storage parameters 17, and the watermarking rules 174, any combinations of which can be stored in the security data 254. A record of the published protected object, and its relationships to the request attributes 172, storage parameters 171, and watermarking rules 174, can be codified in the watermarking logs 186 and possibly stored in the security data 254.).
wherein the determining the recipient of a copy of the document includes determining which of the one or more computing devices the copy of the document was provided to using the stored information. (Vikramaratne: ¶0046: As seen in Figure 1A, the collaboration server 152 can further interface with the watermarking proxy 252 to assist in secure sharing and tracking of the sensitive movie script object. The creator collaborator 125.sub.2 and the user collaborator 123.sub.2 can interact with the cloud-based shared content storage service using web applications (e.g., web application 203.sub.1, web application 203.sub.2, etc.) that operate on various instances of user devices 102 (e.g., user device 102.sub.8, user device 102.sub.9, etc.), which are in communication with one or more servers (e.g., servers in a cloud-based shared content storage system) ¶0073: In one or more embodiments, the steps and underlying operations shown in Figure 4B1 can be executed by the various environments and systems (e.g., collaboration server 152) described herein. As shown, leak source identification technique 4B100 further references the security data 254 comprising certain watermarking logs 186. ).
With respect to claim 19, the combination of Vikramaratne in view of Goyal teaches method of claim 12 (see rejection of claim 12 above) wherein the determining the recipient of a copy of the document includes determining the recipient using an excerpt of the copy of the document. (Vikramaratne: ¶0074-0075: As seen in Figure 4B1, the leak source identification technique 4B100 starts with uploading a recovered disclosed protected object (see step 432). For example, an admin 124.sub.3 might invoke the leak source identification technique 4B 100 at the security management interface 256. The FileID of the recovered protected object can be determined (see step 434) using various techniques (e.g., admin 124.sub.3 specifies the FileID, crawler service, etc.). The partitions and/or segments of the recovered protected object are then determined (see step 436). For example, the disclosing party might have removed and/or rearranged various portions of the document to obfuscate the disclosure source. An iterative process comparing the recovered protected object to the set of published protected objects can begin by selecting a first published protected object (see step 440). A document distance (e.g., an edit distance referring to a total number of keystrokes to affect a modification) is calculated so as to provide a quantitative difference between the recovered protected object and the selected published protected object (see step 442)).
With respect to claim 20, Vikramaratne teaches a non-transitory machine-readable medium encoding instructions that when executed by one or more processors cause a process to be carried out, the process comprising: ( ¶0093-0099: As seen in Figure 7A depicts a block diagram of an instance of an instance of a computer system 7A00 suitable for implementing embodiments of the present disclosure. According to an embodiment of the disclosure, computer system 7A00 performs specific operations by data processor 707 executing one or more sequences of one or more program code instructions contained in a memory. The term “computer readable medium” or “computer usable medium” as used herein refers to any medium that participates in providing instructions to data processor 707 for execution. Common forms of computer readable media includes, for example, floppy disk, flexible disk, hard disk, magnetic tape, or any other magnetic medium; CD-ROM or any other optical medium; punch cards, paper tape, or any other physical medium with patterns of holes; RAM, PROM, EPROM, FLASH-EPROM, or any other memory chip or cartridge, or any other non-transitory computer readable medium.).
generating, by a computing device, different variations of text based on a source document, the different variations to convey the same meaning as the source document while including content different than that of the source document; (¶0005: More specifically, the present disclosure provides a detailed description of techniques used in systems, methods, and in computer program products for sure shared documents using dynamic natural language stenography. Embodiments operate within systems in a cloud-based environment, wherein one or more servers are configured to interface with storage devices that store objects accessible by one or more users. A process receives an electronic message comprising a user request to access an object. Before providing user access to the object, the system generates a requestor-specific steganographic message wherein the steganographic message is derived from some portion of requestor identification or user attributes. Various forms of a requestor-specific steganographic message are applied to the object to generate a requestor-specific protected object (generating different variations of text based on a source document) , which is then provided to the requestor.).
generating, by the computing device, copies of the document that include at least one of the different variations of the text, so that individual copies of the document are traceable based on the different variation of the text included within that copy of the document, (¶0025-0026: In one or more embodiments, the techniques described herein further discuss: (7) receiving a disclosed portion of the protected object; (8) identifying the existence and bounds of a protected object from the disclosed portion to recover at least a portion of a steganographic message (e.g., a portion of the steganographic message that was applied to the object when generating the protected object); and (9) determining the request attributes (e.g., user ID) included in the recovered steganographic message (different variations of the text included within that copy of the document) .).
wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document; and
determining, by the computing device, a recipient of a copy of the document based on a different variation of the text included with the copy. (¶0037-0039: A process receives an electronic message comprising a user request to access an object. Before providing user access to the object, the system generates a requestor-specific steganographic message wherein the steganographic message is derived from some portion of requestor identification or user attributes. Various forms of a requestor-specific steganographic message are applied to the object to generate a requestor-specific protected object (copy with different variation) , which is then provided to the requestor. A web crawler can identify posted unauthorized protected object disclosures. Using requestor-specific aspects (different variation of the text) of the embedded steganographic message, the source of the unauthorized disclosure can be identified (recipient of copy can be identified e.g., determined)).
Vikramaratne does not disclose:
wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document;
However, Goyal teaches wherein generating the copies comprises sending a request to a natural language processing (NLP) service, the request including an instruction to remove and add punctuation from at least a portion of the source document; (¶0070: The natural processing (NLP) may use machine learning to find insights and relationships in text. The text analysis may identify semantics differencing elements, including key phrases, places, people, brands, or events, and may determine to leave certain text unaltered or add consistencies or inconsistencies as to how certain terms are presented in each file. For example, for a text file service can apply NLP technique to generate a clone copy by determining semantic differencing elements for inserting/removing additional punctuation marks, using synonyms, and expanding/shortening phrases like “is not” to “isn't” and vice versa.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a natural language processing (NLP) service to the method of Vikramaratne in order to manage file without compromising security (Goyal ¶0001-0007).
With respect to claim 22, the combination of Vikramaratne in view of Goyal teaches method of claim 1 (see rejection of claim 1 above) wherein generating the copies further comprises: generating, using the NLP service, an initial copy of the document, wherein the initial copy is provided as further input to the NLP service to generate the copies. (Goyal ¶0067-0069: Each clone file may convey the same information with small semantic differences incorporated to make each file unique to each user. While sharing any file via file sharing system, such as SHAREFILE® by Citrix Systems, each unique copy may be generated for each person using natural-language processing (NLP) and machine learning (ML) techniques. After a file is uploaded to the file sharing system, a natural language processing function 332A may begin semantic file analysis in S502 to determine file segments for further processing. The natural language processing may determine a plurality of opportunities to alter a file in ways that not obvious to a user. ).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Goyal of using a natural language processing (NLP) service to the method of Vikramaratne in order to manage file without compromising security (Goyal ¶0001-0007).
Claims 10 and 18 are rejected under 35 U.S.C. 103 as being unpatentable over Vikramaratne et al. (US PGPub No.20170126631-A1) in view of Goyal (US PGPub No. US-20200057843-A1) and Wu et al. (US PGPub No. 20170134344-A1).
With respect to claim 10, the combination of Vikramaratne in view of Goyal teaches the method of claim 1 (see rejection of claim 1 above) but does not disclose, wherein the determining the recipient of a copy of the document includes determining the recipient based on an using of a hash of the copy of the document.
However, Wu teaches wherein the determining the recipient of a copy of the document includes determining the recipient based on an using of a hash of the copy of the document. (¶0046: When all tiles have been processed, a protected object can be built from the modified tiles (see step 316). Certain attributes associated with the protected object can then be stored (see step 318). For example, the role profile associated with the requesting user can be updated with attributes such as “File ID”, “Steganography Method”, “Steganography Hash”, and other attributes describing the protected object (hash of the copy of the document) as seen see Figure. 1E2 . Such attributes can, in part, enable identification of the requesting user should the requesting user capture and disclose the rendered view of the protected object (see step 320) (determining the recipient)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching Wu of using a hash of the copy of the document to the method of Vikramaratne in view of Goyal in order to identify the user that is related to the unauthorized access to the protected object (Wu ¶0008).
With respect to claim 18, the combination of Vikramaratne in view of Goyal teaches method of claim 12 (see rejection of claim 12 above) but does not disclose, wherein the determining the recipient of a copy of the document includes determining the recipient based on an using of a hash of the copy of the document.
However, Wu teaches wherein the determining the recipient of a copy of the document includes determining the recipient based on an using of a hash of the copy of the document. (¶0046: When all tiles have been processed, a protected object can be built from the modified tiles (see step 316). Certain attributes associated with the protected object can then be stored (see step 318). For example, the role profile associated with the requesting user can be updated with attributes such as “File ID”, “Steganography Method”, “Steganography Hash”, and other attributes describing the protected object (hash of the copy of the document) as seen see Figure. 1E2 . Such attributes can, in part, enable identification of the requesting user should the requesting user capture and disclose the rendered view of the protected object (see step 320) (determining the recipient)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching Wu of using a hash of the copy of the document to the method of Vikramaratne in view of Goyal in order to identify the user that is related to the unauthorized access to the protected object (Wu ¶0008).
Claim 21 is rejected under 35 U.S.C. 103 as being unpatentable over Vikramaratne et al. (US PGPub No.20170126631-A1) in view of Goyal (US PGPub No. US-20200057843-A1) and Larson et al. (US PGPub No. 20230122684-A1).
With respect to claim 21, the combination of Vikramaratne in view of Goyal teaches method of claim 1 (see rejection of claim 1 above) but does not disclose wherein the NLP service includes a plurality of different natural language processing models, and wherein the copies are generated using a particular model, of the plurality of different natural language processing models , based on a number of examples provided in the request, a type of text in the document, and a format in which the copies will be generated.
However, Larson teaches wherein the NLP service includes a plurality of different natural language processing models, (¶0043: Additionally, or alternatively, the classification subsystem 100 may implement The one or more ensembles of machine learning models may employ any suitable machine learning including one or more of: supervised learning (e.g., using logistic regression, using back propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using an Apriori algorithm, using K-means clustering), semi-supervised learning, reinforcement learning (e.g., using a Q-learning algorithm, using temporal difference learning), adversarial learning, and any other suitable learning style. Each module of the plurality can implement any one or more of: … including ULMFiT, XLM UDify, MT-DNN, SpanBERT, RoBERTa, XLNet, ERNIE, KnowBERT, VideoBERT, ERNIE BERT-wwm, MobileBERT, TinyBERT, GPT, GPT-2, GPT-3, GPT-4 (and all subsequent iterations), ELMo, content2Vec, and the like (implying the use of plurality of different language processing module)) and wherein the copies are generated using a particular model, of the plurality of different natural language processing models , based on a number of examples provided in the request, (¶0063: This is such embodiments, the document-image generator may have programmatic, web, and/or Internet-based access to each of the plurality of distinct external sources of document data and may selectively ) a type of text in the document, and a format in which the copies will be generated. (¶0070: As seen in Figure 2, in fourth implementation, S210 may function to implement a document-image generator that interface with one or more generative deep learning models, such as generative adversarial networks or a text-to-image model (e.g., DALL-E and/or DALL-E 2, a transformer language model derived from GPT-3) for producing one or more corpora of document samples or image samples . In this fourth implementation, document sourcing parameters may include one or more document seed samples or one or more image seed samples (based on a number of examples) from a generative deep learning model may use as model input for learning and subsequently, generating new document samples or new image samples based on learning derived from the document or image seed samples.).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Larson of using a plurality of natural language processing models to the method of Vikramaratne in view of Goyal in order to successfully govern/manage digital items throughout any type of storage system (Larson ¶0003).
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Vikramaratne et al. (US PGPub No.20170126631-A1) in view of Goyal (US PGPub No. US-20200057843-A1), Baek et al. (US PGPub No. 20180189369-A1 ), and Kamata et al. (US PGPub No. 20170124347-A1).
With respect to claim 23, the combination of Vikramaratne in view of Goyal teaches method of claim 1 (see rejection of claim 1 above) but does not disclose wherein the request further includes an instruction to reword text of the document to intentionally convey a different meaning as the document, wherein the reworded text conceals sensitive information of the document.
However, Baek teaches wherein the request further includes an instruction to reword text of the document to intentionally convey a different meaning as the document, ( ¶0121: Other qualitative edit or update categorizations are also possible, such as determining that edits or updates are primarily proofreading edits (e.g., corrections for misspellings, grammar, or other typographic errors that do not alter the meaning of the original text), copy edits (e.g., corrections to conform to a style guide in addition to proofreading edits), substantive edits (e.g., additions or deletions that alter the meaning of the original text), etc.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Baek of rewording text to change meaning to the method of Vikramaratne in view of Goyal in order to allow flexibility while being efficient with time (Baek ¶0006).
Vikramaratne in view of Goyal and Baek does not disclose:
wherein the reworded text conceals sensitive information of the document
However, Kamata teaches wherein the reworded text conceals sensitive information of the document. ( ¶0059: Further, the intermediate-data edit unit 310 edits the intermediate data such that predetermined information contained in the text in the intermediate data is redacted. More specifically, the intermediate-data edit unit 310 according to this embodiment queries the document management system 110 for the confidential information relevant to the document designated by the print request. Further, the intermediate-data edit unit 310 deletes the confidential information contained in the intermediate data based on the query result. A detailed description is given later of the intermediate-data edit unit 310. Further, the intermediate-data edit unit 310 retrieves the confidential information based on the extraction result and deletes the confidential information by redaction (concealing sensitive information of the document) or replacement. With this configuration, the confidential information can be detected more precisely and effectively compared with a typical text search.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to utilize the teaching of Kamata of concealing sensitive information of the document to the method of Vikramaratne in view of Goyal and Baek in order to prevent confidential information to be accessed unintentionally (Kamata ¶0006).
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.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to TAYLOR P VU whose telephone number is (703)756-1218. The examiner can normally be reached MON - FRI (7:30 - 5:00).
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Alexander Lagor can be reached at (571) 270-5143. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/T.P.V./Examiner, Art Unit 2437
/ALEXANDER LAGOR/Supervisory Patent Examiner, Art Unit 2437
Prosecution Insights