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
Applicant’s arguments with respect to claim(s) for rejected claims under 35 USC 103(a) have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
Applicant argued in the remark that Ache discloses provide a data set to the electronic platform while masking a second data set of the plurality of data sets to limit visibility of data stored at the token to a subset of data such that the electronic platform only views data relevant for the data request.
Examiner respectfully disagrees, Ache discloses par 0013 encrypts i.e. masking, the unique ID, i.e. a second dataset , if it is stored outside user device, or it can be stored encrypted in the cookie, i.e. a data set, . par 0037 Token 120 can be delivered with the electronic content as it is being delivered to the user device 102, i.e. the electronic platform. Par 0039 discloses user 10 requests access to electronic content. The licensing server 106 determines if the user device 102 has a token 120. Either the token 120 can be passed up to the licensing server 106 at the time of the request or the licensing server can query the user device 102 to find the token 120. If the user device 102 does have a token 120, the user 10 is granted access to the electronic content.
Even if, Ache does not disclose limit visibility of data stored at the token.
However, Shi et al US 2016/0004977 discloses limit visibility of data stored at the token (fig.2, [0035] At step 205, the process 200 calculates the score for each token, sentence, paragraph, and/or section of the content. With the optimized weights, a token's score is calculated as follows: The score has a range of [0, 1]. Based on token scores, the process 200 may calculate scores for sentences, paragraphs, and sections. For example, let t.sub.i.sup.1, . . . t.sub.i.sup.n be scores of n tokens in a sentence i, the score for sentence i can be computed by function: [0036] At step 206, the process 200 redacts the content based on the calculated and normalized scores. Content redaction can be based on tokens, sentences, paragraphs, or sections. The higher the information's score is the more important the information is. Thus, information (e.g., token, sentence, paragraph, section) with the highest score should be redacted first. Then, information with the second highest score should be the next candidate for redaction. In one embodiment, a content provider may specify a threshold value (e.g., 0.8) for purposes of redacting its content. If content redaction is token based, tokens having normalized scores in [0, 1] above the threshold value may be redacted. Similarly, if the redaction is sentence based, sentences having scores above the threshold value may be redacted. 0027] The subjective factor may be computed by using existing algorithms (such as the ones developed by Stanford Natural Language Processing Group) to analyze and extract sentiment of the token. A token having polite, positive sentiment may have a high score between 0 and 1, whereas a token having negative sentiment may have a low score between 0 and 1, or vice versa if the redaction purpose is to hide negative content. And fig.3, has the customer paid for the content? If there is an answer is NO, then show reacted content at 303, it means limited visibility of the content).
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) 1,2,4,9-12 and 17-18 are rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/0272031 and Shi et al US 2016/0004977.
As per claim 1. Ache discloses a system comprising:
processing circuitry (0028 A licensing server 106 contains all or part of a DRM system); and a memory device including instructions stored thereon, wherein the instructions, which when executed by the processing circuitry, configure the processing circuitry to perform operations that (0028 A licensing server 106 contains all or part of a DRM system to manage electronic content for a provider 20):
receive a data request and a token identifier from an electronic platform of a plurality of disparate electronic platforms (0039 user 10 requests access to electronic content and 0009 the user can input the unique ID or initiate a request to make the user device an authorized user device. The request includes a request with the unique ID request, i.e. a token identifier from the different platforms, 102a-102n and 0026, A user 10 has one user device 102 or more than one user device 102a, 102b, 102c . . . 102.sub.N, 102.sub.N+1 for rendering electronic content, i.e. an electronic platform of a plurality of disparate electronic platforms and 0041 receiving a request for access to the electronic content from a user device (step 500), accessing a unique ID of the user device (step 502) );
identify a token that corresponds to the token identifier ( 0010] Once the user requests access to the electronic content either the unique ID is passed to the licensing server with the request or the licensing server queries user device for its unique ID. The unique ID is compared, i.e. identifying a token to the unique IDs of the authorized user devices to determine if the requesting user device is an authorized user device. If the user device is authorized, the user is allowed access to the content. And 0043 the system can determine, i.e. identifying, if the user device has a token 120 (step 518) and provide access to the electronic content if the user device has the token (step 520). ), the token being associated with a user and storing electronic data having a plurality of data sets associated with the user ( 0043 the user device is added to an authorization database (step 524) and a token is provided to the user device ),
wherein the token is configured to:
interface with each of the plurality of disparate electronic platforms ( 0014 The token can be linked, i.e. interface, to the user device and controls other tokens on the user device (e.g. a master token, root and leaf tokens) or can be an individual token to each individual piece of electronic content. Tokens can be delivered with the electronic content as it is being delivered to the user device); and
map relationships of the user with ones of the plurality of disparate electronic platforms (0014 The token can be linked to the user device and controls other tokens on the user device (e.g. a master token, root and leaf tokens) wherein the leaf tokens can be seen as the map relationships of the user device of 0026, A user 10 has one user device 102 or more than one user device 102a, 102b, 102c . . . 102.sub.N, 102.sub.N+1 for rendering electronic content, i.e. an electronic platform of a plurality of disparate electronic platforms);
identify a first data set of the plurality of data sets having data that corresponds to the data request ( 0037 can be an individual token 120 to each individual piece of electronic content, i.e. a first data set . 0039 If the unique ID 104 of the user device 102 matches, the unique ID 104, i.e. the unique ID request to identify the electronic content, i.e. a first data set, of an authorized user device 110 in the authorization database 112, the user 10 is granted access to the electronic content ); and
provide a data set to the electronic platform while masking a second data set of the plurality of data sets to limit visibility of data stored at the token to a subset of data such that the electronic platform only views data relevant for the data request.
(Ache discloses par 0013 encrypts i.e. masking, the unique ID, i.e. a second dataset , if it is stored outside user device, or it can be stored encrypted in the cookie, i.e. a data set, . par 0037 Token 120 can be delivered with the electronic content as it is being delivered to the user device 102, i.e. the electronic platform. Par 0039 discloses user 10 requests access to electronic content. The licensing server 106 determines if the user device 102 has a token 120. Either the token 120 can be passed up to the licensing server 106 at the time of the request or the licensing server can query the user device 102 to find the token 120. If the user device 102 does have a token 120, the user 10 is granted access to the electronic content).
Ache does not disclose masking a second data set of the plurality of data sets to limit visibility of data stored at the token to a subset of data,
However, Shi et al US 2016/0004977 discloses provide a data set to the electronic platform while masking a second data set of the plurality of data sets to limit visibility of data stored at the token to a subset of data such that the electronic platform only views data relevant for the data request (fig.2, [0035] At step 205, the process 200 calculates the score for each token, sentence, paragraph, and/or section of the content. With the optimized weights, a token's score is calculated as follows: The score has a range of [0, 1]. Based on token scores, the process 200 may calculate scores for sentences, paragraphs, and sections. For example, let t.sub.i.sup.1, . . . t.sub.i.sup.n be scores of n tokens in a sentence i, the score for sentence i can be computed by function: [0036] At step 206, the process 200 redacts, i.e. masking, the content based on the calculated and normalized scores. Content redaction can be based on tokens, sentences, paragraphs, or sections. The higher the information's score is the more important the information is. Thus, information (e.g., token, sentence, paragraph, section) with the highest score should be redacted first. Then, information with the second highest score should be the next candidate for redaction. In one embodiment, a content provider may specify a threshold value (e.g., 0.8) for purposes of redacting its content. If content redaction is token based, tokens having normalized scores in [0, 1] above the threshold value may be redacted. Similarly, if the redaction is sentence based, sentences having scores above the threshold value may be redacted. 0027] The subjective factor may be computed by using existing algorithms (such as the ones developed by Stanford Natural Language Processing Group) to analyze and extract sentiment of the token. A token having polite, positive sentiment may have a high score between 0 and 1, whereas a token having negative sentiment may have a low score between 0 and 1, or vice versa if the redaction purpose is to hide negative content. And fig.3, has the customer paid for the content? If there is an answer is NO, then show reacted content at 303, it means limited visibility of the content).
Ache and Shi are both considered to be analogous to the claimed invention because they are in the same field of content protection.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache to incorporate the teachings of Shi and provide redacted content.
Doing so would control over the content for unauthorized user, thereby increasing the protection for the sensitive information in the content.
As per claims 2, Ache and Shi disclose the system of claim 1, Ache disclsoes wherein each of the plurality of disparate electronic platforms have different communication protocols (fig.1, numeral 102 0026 . A user 10 has one user device 102 or more than one user device 102a, 102b, 102c . . . 102.sub.N, 102.sub.N+1 for rendering electronic content. Electronic content can be audio, video, still images, text, data, and software or any combination thereof. User devices 102 can be any device than can render the electronic content, including computers, laptops, PDAs, cellular telephones, pagers, Blackberries.RTM., handheld players, including MP3 and video players, stereos, DVD players, DVRs, and portable entertainment systems. The devices can be Janus& enabled).
As per claim 4. Ache and Shi disclose the system of claim 1, Ache wherein the token is configured to provide the electronic data from the plurality of data sets in response to multiple data requests (fig.3, tokens 1- n+1 data sets are providing in response of the request from the multiple electronic platforms, [0042] The unique ID is used to determine if the user device is an authorized user device 110 (step 506) and if the user device 102 is the authorized user device 110, access is provided to the electronic content (step 508). If the user device 102 is not one of the authorized user devices 110, determining if a maximum number N of the authorized user devices is reached (step 510). The user device 102 is converted to an authorized user device 110, if the maximum number N is not reached (step 512) and access to the electronic content can be provided (step 508). If the maximum number N of authorized user devices 110 is reached, optionally denying the user device 102 access to the electronic content (step 514) or requesting de-authorization of one of the authorized user devices (step 516), converting the user device to an authorized user device 110 (step 512), and provide access to the electronic content (step 508)).
As per claim 9. Ache and Shi discloses The system of claim 1, Ache discloses wherein the token is stored offsite from the plurality of disparate electronic platforms ( 0041 the unique ID 104 can be stored on the user device 102 or the portal device 103. The list of authorized user devices can be authorization database 112 or any other method known in the art to compile and store data to be accessed.).
As per claim 10. Ache and Shi disclose the system of claim 9, Ache discloses wherein the token is encrypted and stored on one of a blockchain, a distributed ledger, or a graphical database (0037 Token 120 can be delivered with the electronic content i.e. a first data set as it is being delivered to the user device 102. And 0013 encrypts the unique ID if it is stored outside user device, or it can be stored encrypted in the cookie. And 0014 The token can be any identifier, thus the token 120 is encrypted as it is masked and0041 the unique ID 104 can be stored on the user device 102 or the portal device 103).
As per claim 11, this claim is rejected based on the same rational set forth in the claim 1.
As per claim 12, this claim is rejected based on the same rational set forth in the claim 2.
As per claim 17. Ache and Shi disclose the non-transitory, machine-readable medium of claim 11, Ache discloses wherein the token is: configured to provide the electronic data from the plurality of data sets in response to multiple data requests (([0042] The unique ID is used to determine if the user device is an authorized user device 110 (step 506) and if the user device 102 is the authorized user device 110, access is provided to the electronic content (step 508). If the user device 102 is not one of the authorized user devices 110, determining if a maximum number N of the authorized user devices is reached (step 510). The user device 102 is converted to an authorized user device 110, if the maximum number N is not reached (step 512) and access to the electronic content can be provided (step 508). If the maximum number N of authorized user devices 110 is reached, optionally denying the user device 102 access to the electronic content (step 514) or requesting de-authorization of one of the authorized user devices (step 516), converting the user device to an authorized user device 110 (step 512), and provide access to the electronic content (step 508). ); stored offsite from the plurality of disparate electronic platforms (0041 the unique ID 104 can be stored on the user device 102 or the portal device 103. The list of authorized user devices can be authorization database 112 or any other method known in the art to compile and store data to be accessed ) ; and encrypted and stored on one of a blockchain, a distributed ledger, or a graphical database ( [0042] The unique ID is used to determine if the user device is an authorized user device 110 (step 506) and if the user device 102 is the authorized user device 110, access is provided to the electronic content (step 508). If the user device 102 is not one of the authorized user devices 110, determining if a maximum number N of the authorized user devices is reached (step 510). The user device 102 is converted to an authorized user device 110, if the maximum number N is not reached (step 512) and access to the electronic content can be provided (step 508). If the maximum number N of authorized user devices 110 is reached, optionally denying the user device 102 access to the electronic content (step 514) or requesting de-authorization of one of the authorized user devices (step 516), converting the user device to an authorized user device 110 (step 512), and provide access to the electronic content (step 508)).
As per claims 11 and 18, claims are rejected based on the same rational set forth in the claim 1.
As per claim 12, this claim is rejected based on the same rational set forth in the claim 2.
Claim(s) 3, 13 and 19 are rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/027203 and Shi in view of Schmidt US 2013/0185325.
As per claim 3. Ache and Shi discloses the system of claim 1, but fails to disclose wherein the relationships further include: relationships between the user and other users; relationships between the user and accounts associated with the user at the plurality of disparate electronic platforms; relationships between a first electronic platform of the plurality of disparate electronic platforms and a second electronic platform of the plurality of disparate electronic platforms; and relationships between the user and assets stored at the plurality of disparate electronic platforms.
However, Schmidt discloses relationships between the user and other users (0025 the system can determine whether users associated with the user ); relationships between the user and accounts associated with the user at the plurality of disparate electronic platforms (0041] Social network 400 may include a plurality of nodes 402 that may each correspond to a social network user ID. For example, a node 402 may correspond to a social network user ID `A1` of a user A and another node may correspond to a social network ID `C1` of a user C. User A and user C may interact on social network 400 and may, for example, become "friends". This may result in a relationship link 406 being established between the node 402 corresponding to the social network user ID `A1` and the node 402 corresponding to the social network user ID `B1`. It should be appreciated that relationship link 406 may only be one of many different types of relationship links that may exist in a social network. In addition, the personal association between user A and user C may result in a respective relationship value 404 being assigned to their relationship link 406. The relationship value may indicate the "strength" of the relationship between user A and user C, for example. Relationship value 404 corresponding to any relationship 406 may be determined and/or assigned by social network 400, and may be included in relationship data for social network 400. ); relationships between a first electronic platform of the plurality of disparate electronic platforms and a second electronic platform of the plurality of disparate electronic platforms ( [0045] In some embodiments, a server (e.g., server 200 or 302) may store data structure 500 to maintain an inventory of all the assets (e.g., which may or may not each be stored on the server itself) available for access. Data structure 500 may also be stored in an electronic device (e.g., device 100 or 304) to maintain a list of all the assets that are associated with the electronic device. The list of assets associated with the electronic device may be all of the assets that have been or once was installed on, used, or accessed by the electronic device. In some embodiments, the electronic device and the server may store the same or different information in data structure 500. For example server may store more detailed information regarding each asset in data structure 500 (e.g., purchase price, date of creation, etc.) that the electronic device may not need to store. In some embodiments, data structure 500 may be updated whenever an asset is added, deleted, or any information regarding the asset); and relationships between the user and assets stored at the plurality of disparate electronic platforms (0004 The user account database may include a plurality of user accounts. Each user account may maintain a list of assets stored on one or more devices associated with that user account. Each user account may be associated with one or more social network user IDs. Each user may have associated relationship metrics that define a personal association probability value between that user account and each other user account based on access to at least one social network associated with the one or more social network user IDs of that user account. The method may include receiving an asset search query from a first user having a first user account in the user account database, and accessing an asset database to obtain a set of prioritized assets that correspond to the asset search query. The set of prioritized assets may be based on the relationship metrics associated with the first user account. The method may also include displaying at least a subset of the prioritized set of assets. And [0067] At step 1006, the server may access the asset database (e.g., data structure 500) to obtain a set of prioritized assets that correspond to the received asset search query. For example, the first user may enter an asset search query `Game` in the asset search screen. The server may access the asset database (e.g., data structure 500) to search for assets that corresponds to `Game`, and may produce asset search results (e.g., asset search results 900) based on what the server finds in the asset database. The server may then refer to the user account database (e.g., data structure 600) and may determine that the first user (e.g., user ID `0001` stored in element 602a of data structure 600) is associated with another user account (e.g., user ID `0003` stored in element 602b of data structure 600) and that a corresponding relationship metric or overall related user relationship value of this another user account, for the first user account, exceeds a threshold (e.g., `1` stored in element 622b of data structure 600). In some embodiments, the threshold may be set according to any suitable criteria. In this example, the threshold may be 0.5. The server may also determine that the asset search results (e.g., asset search results 900) include a particular asset (e.g., `Game Z`) and that the another user account is associated with a device that is associated the same particular asset (e.g., `Game Z` stored in element 608g of data structure 600). As a result, the server may prioritize the asset search results (e.g., asset search results 900) to produce at prioritized asset search results (e.g., prioritized asset search results 920). Thus, the set of prioritized assets may be based on the relationship metrics associated with the first user account. In some embodiments, the particular asset that is included in the asset search results and that is associated with the another user account may already be associated with the first user (e.g., in data structure 600). In this scenario, the server may remove the particular asset from the asset search results (e.g., asset search results 900) to avoid presenting assets that the first user is already associated with).
Ache and Shi and Schmidt are both considered to be analogous to the claimed invention because they are in the same field of control of the content.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache, including the teaching of Shi, to incorporate the teachings of Schmidt and provide correlation of one or more of the identified other social network user IDs to user accounts maintained in a user account database.
Doing so would determine that the one or more other social network user IDs are associated with other user accounts in the user account database, thereby increasing prioritize the determined set of assets(par 0074).
As per claim 13, this claim is rejected based on the same rational set forth in the claim 3.
As per claim 19, claim is rejected based on the same rational set forth in the claim 3.
Claim(s) 5, 14 and 20 are rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/027203 and Shi in view of Kayyoor et al US 10,997,499.
As per claim 5. Ache and Shi disclose the system of claim 1, fails to disclose wherein the token includes a machine learning model configured to create the plurality of data sets and the processing circuitry is further configured to perform operations that:
provide a first set of training data to the machine learning model, the first set of training data training the machine learning model to create the plurality of data sets at a first time interval; and
provide a second set of training data to the machine learning model, the second set of training data training the machine learning model to update the plurality of data sets at a second time interval in response to data requirement changes for the token,
where the second set of training data causes the machine learning model to change over time between the first time interval and the second time interval such that the token changes over time based on the machine learning model changing over time between the first time interval and the second time interval.
However, Kayyoor discloses wherein the token includes a machine learning model configured to create the plurality of data sets and the processing circuitry is further configured to perform operations that (col 1, lines 40-45 creating a set of training data to train a machine learning model to analyze tokens within a set of tokens that describe files within a file system, the set of training data including both a first set of vectors):
provide a first set of training data to the machine learning model, the first set of training data training the machine learning model to create the plurality of data sets at a first time interval (col 1, lines 45-60 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, and a second set of vectors, where each vector within the second set of vectors represents a subset of the set of tokens that describes files with a predetermined number of common file path ancestors, (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, (iii) determining, by providing at least one input token from the set of tokens as input to the trained machine learning model, that the at least one input token is related to at least one additional token within the set of tokens, and (iv) performing an action responsive to observing the input token and involving the additional token and the file system in response to determining that the input token is related to the additional token ); and
provide a second set of training data to the machine learning model, the second set of training data training the machine learning model to update the plurality of data sets at a second time interval in response to data requirement changes for the token ( col 2, lines 30-50 creates a set of training data to train a machine learning model to analyze tokens within a set of tokens that describe files within a file system, the set of training data including both a first set of vectors, 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, and a second set of vectors, where each vector within the second set of vectors represents a subset of the set of tokens that describes files with a predetermined number of common file path ancestors, (ii) a training module, stored in memory, that trains, using the set of training data, the machine learning model to define a set of latent features from the set of training data, (iii) a determination module, stored in memory, that determines, by providing at least one input token from the set of tokens as input to the trained machine learning model, that the at least one input token is related to at least one additional token within the set of tokens, (iv) a performing module, stored in memory, that performs an action responsive to observing the input token and involving the additional token and the file system in response to determining that the input token is related to the additional token, and (v) at least one physical processor configured to execute the creation module, the training module, the determination module, and the performing module),
where the second set of training data causes the machine learning model to change over time between the first time interval and the second time interval such that the token changes over time based on the machine learning model changing over time between the first time interval and the second time interval (col 2, lines 60-67 create a set of training data to train a machine learning model to analyze tokens within a set of tokens that describe files within a file system, the set of training data including both a first set of vectors, 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, and a second set of vectors, where each vector within the second set of vectors represents a subset of the set of tokens that describes files with a predetermined number of common file path ancestors, (ii) train, using the set of training data, the machine learning model to define a set of latent features from the set of training data, (iii) determine, by providing at least one input token from the set of tokens as input to the trained machine learning model, that the at least one input token is related to at least one additional token within the set of tokens, and (iv) perform an action responsive to observing the input token and involving the additional token and the file system in response to determining that the input token is related to the additional token ).
Ache and Shi and Kayyor are both considered to be analogous to the claimed invention because they are in the same field of token for the content.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache and Shi to incorporate the teachings of Kayyoor and provide enable computing device to perform metadata analytics( col 5, lines 39-40).
Doing so would create a set of training data to train a machine learning model to analyze tokens, thereby increasing to provide the metadata analytics(col 5, lines 39-40).
As per claim 14, this claim is rejected based on the same rational set forth in the claim 5.
As per claim 20, claim is rejected based on the same rational set forth in the claim 5.
Claim(s) 6 is rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/027203 and Shi in view of Picinini US 2022/0043976.
As per claim 6. Ache and Shi disclose the system of claim 1, fails to disclose wherein the processing circuitry is further configured to perform operations that: receive additional data sets from the plurality of disparate electronic platforms; and update the token with the additional data sets.
However, Picinini discloses receive additional data sets from the plurality of disparate electronic platforms ( 0059 after revising the token-tag data set 245 through the tag inconsistency component 250, the reviewer or the tag inconsistency component 250 may input an updated token-tag data set (e.g., revised token-tag data set) to a machine learning training component 220. ); and update the token with the additional data sets (0060] Using the updated token-tag data set as the training data, the machine learning training component 220 may use verified or correct tags for different tokens to then perform different implementations. For example, if a description for a product is translated into different languages (e.g., for different countries or users that use different languages than those originally used to describe the product),).
Ache and Shi and Picinini are both considered to be analogous to the claimed invention because they are in the same field of machine learning training of data.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache and Shi to incorporate the teachings of Picinini and provide change to the token tag(0059).
Doing so would classify tags for tokens may be a neural network, thereby increasing to provide the machine learning training component 220 may use the updated token-tag list to identify corresponding products with those included features to return higher quality search results (0060).
Claim(s) 7 is rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/027203 and Shi in view of Pepin et al US 2002/0042835.
As per claim 7. Ache and Shi disclose the system of claim 1, fails to disclose wherein the processing circuitry is further configured to perform operations that: receive a third data set of the plurality of data sets; and
replace the first data set of the plurality of data sets with the third data set of the plurality of data sets.
However, Pepin discloses receive a third data set of the plurality of data sets (11. The method of claim 1, wherein said first applying step comprises the step of: replacing said first set of data with a third set of data.
); and
replace the first data set of the plurality of data sets with the third data set of the plurality of data sets (31. The system of claim 23, wherein said means for first applying comprises: means for replacing said first set of data with a third set of data.).
Ache ,Shi and Pepin are both considered to be analogous to the claimed invention because they are in the same field of machine handling of data.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache and Shi to incorporate the teachings of Pepin and provide replacement of the data.
Doing so would provide overlap of the data, thereby improve improved bulk loading system (par 0009).
Claim(s) 8 and 16 are rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/027203 and Shi in view of Hu et al US 2018/0204215.
As per claim 8. Ache and Shi discloses the system of claim 1, fails to disclose wherein the processing circuitry is further configured to perform operations that:
access a listing relating to fraudulent activity occurring at one of the plurality of disparate electronic platforms using the token; and
deny the data request based on the listing relating to fraudulent activity occurring at one of the plurality of disparate electronic platforms.
However, Hu discloses access a listing relating to fraudulent activity occurring at one of the plurality of disparate electronic platforms using the token ( 0032 The resource security system 100 may implement access rules to identify fraudulent access requests based on parameters of the access request. Such parameter may correspond to fields (nodes) of a data structure that is used to distinguish fraudulent access requests from authentic access requests); and
deny the data request based on the listing relating to fraudulent activity occurring at one of the plurality of disparate electronic platforms (0032 the resource security system 100 may be used to deny fraudulent access requests that appear to be legitimate access requests of authorized users).
Ache and Shi and Hu are both considered to be analogous to the claimed invention because they are in the same field of access controlling data.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache and Shi to incorporate the teachings of Hu and provide potentially fraudulent authentication requests.
Doing so would provide blacklists can prevent a legitimate user from access a resource, thereby improve resource security system (par 0031)
As per claim 16, this claim is rejected based on the same rational set forth in the claim 8.
Claim(s) 15 is rejected under 35 U.S.C. 103 as being unpatentable over Ache et al US 2006/027203 and Shi in view of Picinini US 2022/0043976 in view of Pepin et al US 2002/0042835.
As per claim 15. Ache and Shi discloses the non-transitory, machine-readable medium of claim 11, wherein the instructions further cause the processor perform operations to:
receive additional data sets from the plurality of disparate electronic platforms; update the token with the additional data sets;
receive a third data set of the plurality of data sets; and replace the first data set of the plurality of data sets with the third data set of the plurality of data sets.
However, Picinini discloses receive additional data sets from the plurality of disparate electronic platforms ( 0059 after revising the token-tag data set 245 through the tag inconsistency component 250, the reviewer or the tag inconsistency component 250 may input an updated token-tag data set (e.g., revised token-tag data set) to a machine learning training component 220. ); and update the token with the additional data sets (0060] Using the updated token-tag data set as the training data, the machine learning training component 220 may use verified or correct tags for different tokens to then perform different implementations. For example, if a description for a product is translated into different languages (e.g., for different countries or users that use different languages than those originally used to describe the product),).
Ache and Shi and Picinini are both considered to be analogous to the claimed invention because they are in the same field of machine learning training of data.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache and Shi to incorporate the teachings of Picinini and provide change to the token tag(0059).
Doing so would classify tags for tokens may be a neural network, thereby increasing to provide the machine learning training component 220 may use the updated token-tag list to identify corresponding products with those included features to return higher quality search results (0060).
The combination fails to disclose receive a third data set of the plurality of data sets; and replace the first data set of the plurality of data sets with the third data set of the plurality of data sets.
However, Pepin discloses receive a third data set of the plurality of data sets (11. The method of claim 1, wherein said first applying step comprises the step of: replacing said first set of data with a third set of data.
); and
replace the first data set of the plurality of data sets with the third data set of the plurality of data sets (31. The system of claim 23, wherein said means for first applying comprises: means for replacing said first set of data with a third set of data.).
Ache and Shi and Picinini and Pepin are both considered to be analogous to the claimed invention because they are in the same field of machine handling of data.
Therefore, it would have been obvious to someone of ordinary skill in the art before the effective filing date of the claimed invention to have modified Ache and Shi including the teaching of Picinini, to incorporate the teachings of Pepin and provide replacement of the data.
Doing so would provide overlap of the data, thereby improve improved bulk loading system (par 0009).
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 ABU S SHOLEMAN whose telephone number is (571)270-7314. The examiner can normally be reached EST: 9am-5pm.
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/ABU S SHOLEMAN/Primary Examiner, Art Unit 2496