Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Status
Claims 1-20 are pending.
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.
Claim(s) 1 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wakankar (US 11,205,155) in view of Mnih (US 2020/0090043).
Examiner Note. Hereafter, above references are combination A.
identifying identifiers corresponding to digital content items stored within a content
management system;
generating, utilizing a first machine learning model, identifier embeddings for the
identifiers corresponding to the digital content items;
Wakankar claim 5, processing the plurality of career transition vectors with the neural network to generate for each identifier an embedding in a common embedding space comprises: processing the career transition vectors to generate, for each identifier, a vector representation of the identifier, with identifiers frequently occurring together as part of a career transition having vector representations that are close in distance to one another.
determining one or more digital connections between the digital content items within the
content management system by generating, utilizing a second machine learning model, digital
similarity predictions based on the identifier embeddings; and
Wakankar discloses elements of the claimed invention as noted but does not disclose above limitation. However, Mnih discloses:
Mnih [0047] FIG. 2 shows a first embedding neural network 201, a second embedding neural network 202 and similarity scorer 203 for selecting a memory address from the memory 101 based upon an input data item 104. The first embedding neural network 201, second embedding neural network 202 and similarity scorer 203 may form part of the neural network system 100 of FIG. 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wakankar to obtain above limitation based on the teachings of Mnih for the purpose of providing a neural network system capable of modelling data such that new data items may be generated from the model that cannot be easily distinguished from real data, see [0037].
providing, for display within a user interface of a client device, one or more suggestions
based on the one or more digital connections.
Mnih [0014] For example, if the inputs to the neural network are images or features that have been extracted from images, the output generated by the neural network for a given image may be an image of an object belonging to a category or classification associated with the input image. Additionally, or alternatively the output may be used to obtain scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category.
Claim(s) 2 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
Combination A discloses wherein providing the one or more suggestions comprises one or more of surfacing a user interface element, displaying a prompt with a recommendation in relation to a digital content item, displaying a suggested action with respect to a digital content item, or requesting information from a user based on the one or more digital connections.
Mnih [0014] For example, if the inputs to the neural network are images or features that have been extracted from images, the output generated by the neural network for a given image may be an image of an object belonging to a category or classification associated with the input image. Additionally, or alternatively the output may be used to obtain scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category.
Claim(s) 3 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
Combination A discloses receiving, via the user interface of the client device, an indication of a user interaction with a folder, a workspace, or a digital content item within the content management system;
identifying, in relation to the user interaction, at least one digital connection of the one or
more digital connections; and
providing, in response to identifying the at least one digital connection, the one or more
suggestions in relation to the folder, the workspace, or the digital content item.
Wakankar abstract detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.
Claim(s) 4 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
Combination A discloses identifying a plurality of related digital content items based on the one or more digital connections between the digital content items within the content management system; and providing, for display within the user interface of the client device, the one or more
suggestions including an indication of the plurality of related digital content items.
Mnih [0014] For example, if the inputs to the neural network are images or features that have been extracted from images, the output generated by the neural network for a given image may be an image of an object belonging to a category or classification associated with the input image. Additionally, or alternatively the output may be used to obtain scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category.
Claim(s) 5 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
Combination A discloses further comprising displaying the plurality of related digital content items within the user interface of the client device in response to receiving a user interaction with at least one digital content item of the plurality of related digital content items.
Wakankar col 2 lines 49-62, One general aspect includes a method that includes an operation for generating career transition vectors for members of an online service, each career transition vector comprising identifiers associated with the career transitions of each member. The method further includes operations for performing a similarity analysis of the career transition vectors to generate an embedding vector for each identifier, detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.
Claim(s) 6 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
Combination A discloses wherein generating the identifier embeddings comprises: generating character-level embeddings based on individual characters within the identifiers; or
generating word-level embeddings based on groups of characters within the identifiers.
Wakankar col 8 lines 10-25, Word2vec takes as input a large corpus of words (e.g., identifiers) and produces a high-dimensional space (typically between a hundred and several hundred dimensions). Each unique word in the corpus is assigned a corresponding embedding 408 in the space. The embeddings 408 are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space. In one example embodiment, each element of the embedding 408 is a real number. Each member is represented by the career transitions vector 304, which is treated as a sentence in the Word2vec algorithm.
Claim(s) 7 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A.
Combination A discloses generating user activity embeddings corresponding to the digital content items, wherein generating the one or more digital connections between the digital content items are further based on the user activity embeddings.
Wakankar abstract detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.
Claim(s) 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wakankar (US 11,205,155) in view of Lian (US 2014/0304297) in view of Mnih (US 2020/0090043).
Examiner Note: hereafter, above references are combination B.
at least one processor; and a non-transitory computer-readable medium storing instructions which, when executed by the at least one processor, cause the system to:
Wakankar col 18 lines 1-24
identify one or more identifiers corresponding to one or more digital content items;
generate one or more identifier embeddings corresponding to the one or more
identifiers by utilizing a first machine learning model;
Wakankar claim 5, processing the plurality of career transition vectors with the neural network to generate for each identifier an embedding in a common embedding space comprises: processing the career transition vectors to generate, for each identifier, a vector representation of the identifier, with identifiers frequently occurring together as part of a career transition having vector representations that are close in distance to one another.
generate file relation predictions between the one or more digital content items by
processing the one or more identifier embeddings utilizing a second machine learning
model;
Wakankar discloses elements of the claimed invention as noted but does not disclose above limitation. However, Lian discloses:
Lian [0033] In this embodiment, a server performs processing such as segmenting, backup, and identifier embedding for the media content in advance, stores each segment, into which an identifier is embedded, as an independent file (that is, a media file segment) while performing the processing such as segmenting, backup, and identifier embedding for the media content, and generates a media segment description file (MSDF). The media segment description file indicates an identifier corresponding to each media file segment (that is, the identifier embedded into the media file segment) and a storage address of the media file segment.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wakankar to obtain above limitation based on the teachings of Lian for the purpose of providing the user terminal with a media file segment that is embedded with the identifier of the user terminal, see abstract.
Mnih [0047] FIG. 2 shows a first embedding neural network 201, a second embedding neural network 202 and similarity scorer 203 for selecting a memory address from the memory 101 based upon an input data item 104. The first embedding neural network 201, second embedding neural network 202 and similarity scorer 203 may form part of the neural network system 100 of FIG. 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wakankar to obtain above limitation based on the teachings of Mnih for the purpose of providing a neural network system capable of modelling data such that new data items may be generated from the model that cannot be easily distinguished from real data, see [0037].
identify a file relation prediction associated with a first digital content item; and
provide, for display within a user interface of a client device, a suggested action in
relation to the first digital content item.
Mnih [0014] For example, if the inputs to the neural network are images or features that have been extracted from images, the output generated by the neural network for a given image may be an image of an object belonging to a category or classification associated with the input image. Additionally, or alternatively the output may be used to obtain scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category.
Claim(s) 9 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination B in view of Yantis (US 2021/0326862).
Combination B discloses elements of the claimed invention as noted but does not disclose
further storing instruction which, when executed by the at least one processor, cause the system to provide, for display within the user interface of the client device, the suggested action of assigning or transferring the first digital content item to a suggested workspace. However, Yantis discloses:
Yantis [0003] The system and methods described herein provide for the generation, management, transferring, converting, and sharing of digital items based on distributed ledger or hash technology. In embodiments, the system creates a large set of dynamically generated items that may be unique and vary in rarity among the various items. In some embodiments, the system generates digital items that can be mapped to unique physical or digital items, though such mapping is not required in all embodiments of the system. The system provides an algorithmically secure way to ensure the rarity, immutability and ownership for each item in the item space of the large set of items. Digital items may be traded, gifted, or otherwise transferred between entities, and may be redeemed for a physical representation of the digital item. In some embodiments of the system, a distributed ledger may be utilized to securely track and verify ownership and transfer of an item.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination B to obtain above limitation based on the teachings of Yantis for the purpose of securely tracking and verifying ownership and transferring of an item.
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination B in view of Van Rensburg (US 12,210,588).
Combination B discloses elements of the claimed invention as noted but does not disclose
determine an access privilege prediction for the first digital content item, the access
privilege prediction comprising a defined access level comprising one or more of view, edit, or
share; and provide, for display within the user interface of the client device, the suggested action of assigning or requesting the defined access level to the first digital content item based on the access privilege prediction. However, Van Rensburg discloses:
Van Rensburg claim 8, further comprising determining a recipient's access privileges level and determining which portions of the resource the recipient can view based on the recipient's access privileges level and the recipient's predicted level of understanding of the resource.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination B to obtain above limitation based on the teachings of Van Rensburg for the purpose of displaying a resource preview as a function of the received link and a stored preview-setting profile, see abstract.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination B in view of Chen (US 8,402,545).
Combination B discloses elements of the claimed invention as noted but does not disclose wherein generating the file relation predictions between the one or more digital content items comprises generating a parent-child relationship prediction or a sibling relationship prediction between the first digital content item and a second content item from the one or more digital content items. However, Chen discloses:
Chen col 2 lines 4-20, In some examples, the server or backend may only convict a child object (i.e., classify the child object as a security risk) if an instance of the child object and/or its parent object has never been encountered before within the user community (i.e., if the child object and/or the parent object represents a new or unique file or singleton). In other examples, the server or backend may convict a child object if less than a predetermined number of instances of the child object and/or its parent object appear within the user community or if instances of the child object and/or its parent object appear on less than a predetermined percentage of user devices within the user community. These predetermined percentages and numbers may be determined based on a variety of predictive rules and heuristics.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination B to obtain above limitation based on the teachings Chen for the purpose of identifying unique malware variants that include identifying the creation of a child object by a parent object on a client device, see abstract.
Claim(s) 12 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination B in view of Garg (US 2022/0141653).
Combination B discloses elements of the claimed invention as noted but does not disclose further storing instructions which, when executed by the at least one processor, cause the system to:
determine a storage location within a file structure for a user account based at least in part
on the parent-child relationship prediction or the sibling relationship prediction between the first
digital content item and the second content item; and provide, for display within the user interface of the client device, the suggested action of storing the first digital content item at the storage location within the file structure for the user account.
Garg [0074] To resolve conflicts between usage settings, the process identifies a role associated with each of the active relationships. Various roles may have assigned a respective priority that is used to resolve such conflicts. For example, in the illustration, the relationship between user account 1 and user account 2 may be a parent/child relationship, such that user account 1 has a “child” role and user account 2 has a “parent” role.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination B to obtain above limitation based on the teachings of Garg for the purpose of identifying, with respect to a first user account, a set of relationships associated with a plurality of additional user accounts, see [0003]
Claim(s) 13 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination B in view of Shah (US 2022/0086172)
Combination B discloses elements of the claimed invention as noted but does not disclose
wherein generating the one or more identifier embeddings comprises: generating one or more extension-level embeddings based on file extensions within the one or more identifiers;
However, Shah discloses:
Shah [0084] At 336, process 300 can determine the number of “.bin” files present in the “/word/embeddings/” directory. Process 300 can determine the number of “.bin” files present in the “/word/embeddings/” directory in any suitable manner in some embodiments. For example, in some embodiments, this determination can be made by checking all the files present in the “/word/embeddings/” directory and counting the number of files with the “.bin” extension.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination B to obtain above limitation based on the teachings of Shah for the purpose of protecting network devices from malicious rich text format (RTF) files.
generating one or more character-level embeddings based on individual characters within
the one or more identifiers; or generating one or more word-level embeddings based on groups of characters within the one or more identifiers.
Wakankar col 8 lines 10-25, Word2vec takes as input a large corpus of words (e.g., identifiers) and produces a high-dimensional space (typically between a hundred and several hundred dimensions). Each unique word in the corpus is assigned a corresponding embedding 408 in the space. The embeddings 408 are positioned in the vector space such that words that share common contexts in the corpus are located in close proximity to one another in the space. In one example embodiment, each element of the embedding 408 is a real number. Each member is represented by the career transitions vector 304, which is treated as a sentence in the Word2vec algorithm.
Claim(s) 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination B
Combination B discloses further storing instructions which, when executed by the at
least one processor, cause the system to generate one or more user activity embeddings
corresponding to the one or more digital content items, wherein generating the file relation
predictions between the one or more digital content items is further based on the one or more user activity embeddings.
Wakankar abstract detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.
Claim(s) 15 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wakankar (US 11,205,155) in view of Mnih (US 2020/0090043).
Examiner Note. Hereafter, above references are combination A.
identify identifiers corresponding to digital content items stored within a content
management system;
generate, utilizing a first machine learning model, identifier embeddings for the
identifiers corresponding to the digital content items;
Wakankar claim 5, processing the plurality of career transition vectors with the neural network to generate for each identifier an embedding in a common embedding space comprises: processing the career transition vectors to generate, for each identifier, a vector representation of the identifier, with identifiers frequently occurring together as part of a career transition having vector representations that are close in distance to one another.
determine one or more digital connections between the digital content items within the
content management system by generating, utilizing a second machine learning model, digital
similarity predictions based on the identifier embeddings; and
Wakankar discloses elements of the claimed invention as noted but does not disclose above limitation. However, Mnih discloses:
Mnih [0047] FIG. 2 shows a first embedding neural network 201, a second embedding neural network 202 and similarity scorer 203 for selecting a memory address from the memory 101 based upon an input data item 104. The first embedding neural network 201, second embedding neural network 202 and similarity scorer 203 may form part of the neural network system 100 of FIG. 1.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Wakankar to obtain above limitation based on the teachings of Mnih for the purpose of providing a neural network system capable of modelling data such that new data items may be generated from the model that cannot be easily distinguished from real data, see [0037].
provide, for display within a user interface of a client device, one or more suggestions
based on the one or more digital connections.
Mnih [0014] For example, if the inputs to the neural network are images or features that have been extracted from images, the output generated by the neural network for a given image may be an image of an object belonging to a category or classification associated with the input image. Additionally, or alternatively the output may be used to obtain scores for each of a set of object categories, with each score representing an estimated likelihood that the image contains an image of an object belonging to the category.
Claim(s) 16 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Wang (US 2016/0179329)
Combination A discloses elements of the claimed invention as noted but does not disclose further storing instructions which, when executed by the at least one processor, cause the at least one processor to provide, for display within the user interface of the client device, the suggested action of accessing the first digital content item in response to a user interaction with the second digital content item within the user interface on the client device. However, Wang discloses:
Wang [0069] The third graphical item 70 may provide the user with access to the first and second graphical items 50, 60. FIG. 7C indicates an example in which selection of the third graphical item 70 (as indicated by the arrow 78) causes the processor 12 to control the display 22 to display the first and second graphical items 50, 60, which are then individually selectable. The third graphical item 70 may be removed from display after selection.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings
Wang for the purpose of controlling a touch sensitive display to display a first graphical item at a first position and a second graphical item at a second position, the second graphical item being separated from the first graphical item by a first distance, see [0004].
Claim(s) 17 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Sutardja (US 10,893,104).
Combination A discloses elements of the claimed invention as noted but does not disclose
further storing instructions which, when executed by the at least one processor, cause the at least one processor to provide, for display within the user interface of the client device, the suggested action of storing or transferring the first digital content item to a shared storage location with the second digital content item. However, Sutardja discloses:
Sutardja col 6 lines 15-26, One or more embodiments include storing a set of one or more content items (Operation 202). A personal user device receives an instruction to store the set of content items from a user interface and/or another digital device. In response to receiving the instruction, the personal user device stores the content items. In an embodiment, the content items may be stored across one or more storage devices that are included within a private cloud infrastructure.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings
Wang for the purpose of enabling a personal user device to store a particular content item and transmit the particular content item to a data distribution device for storage, see abstract.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Codrington (US 2017/0220546).
Combination A discloses elements of the claimed invention as noted but does not disclose further storing instructions which, when executed by the at least one processor, cause the at least one processor to provide, for display within the user interface of the client device, the suggested action of assigning a defined access level to the first digital content item based on a previously assigned access level of the second digital content item. However, Codrington discloses:
Codrington [0178] Only a document Owner may have full access to a document. Other users may only access content of the document if invited by the Owner as one of an Owner, Editor, Review, or Reader. In the document, Owners may control an access level of the document content as a whole document or through varying access levels with respect to specific document content, assigning access levels to such content. The access levels may be of Editor, Reviewer, Reader, or a Redacted access level such that certain content may be redacted for an assigned user.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify combination A to obtain above limitation based on the teachings of Codrington for the purpose of specifying access by others to a user document.
Claim(s) 19 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A in view of Official Notice.
Combination A discloses elements of the claimed invention as noted but does not disclose wherein the one or more identifiers corresponding to the one or more digital content items are file names of the one or more digital content items.
Official Notice is taken that file names is well-known in the technological field of the present invention. One of ordinary skill in the art would have been motivated to designate file name for the purpose of uniquely identifying the file.
Claim(s) 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over combination A
Combination A discloses further storing instructions which, when executed by the at least one processor, cause the at least one processor to generate one or more user activity embeddings corresponding to the one or more digital content items, wherein determining the file relation predictions between the one or more digital content items is further based on the one or more user activity embeddings.
Wakankar col 2 lines 49-62, One general aspect includes a method that includes an operation for generating career transition vectors for members of an online service, each career transition vector comprising identifiers associated with the career transitions of each member. The method further includes operations for performing a similarity analysis of the career transition vectors to generate an embedding vector for each identifier, detecting access of a first member to a job search user interface, and selecting one or more top embedding vectors based on one or more embedding vectors of the first member. One or more search starters associated with the one or more top embedding vectors are generated, and the one or more search starters are presented on the job search user interface.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to ETIENNE PIERRE LEROUX whose telephone number is (571)272-4022. The examiner can normally be reached M-F 8:00 am to 4:30 pm.
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, Apu Mofiz can be reached at 571 272 4080. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ETIENNE P LEROUX/Primary Examiner of Art Unit 2161
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