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 .
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 amendment
This office action is in response to an amendment filed on May 06, 2026 in response to PTO office action dated February 11, 2026. The amendment has been entered and considered.
Applicant’s arguments with respect to the double patenting rejection of claims have been fully considered. The double patenting rejection has been maintained.
Applicant’s arguments with respect to the rejection of claims under 35 U.S.C. § 103(a) have been fully considered. However, Examiner respectfully disagrees with the applicant’s argument. For details, see response to argument section.
This action is FINAL.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/process/file/efs/guidance/eTD-info-I.jsp.
Claims 1-20 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12079262 and over claims 1-20 of U.S. Patent No. 10,885,090. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1-20 of the instant application are obvious variants of claims 1-20 of U.S. Patent No. 12079262 and claims 1-20 of U.S. Patent No. 10,885,090.
Claims Comparison Table
U.S. Patent No. 12079262
U.S. Patent No.
10,885,090
Application 18818914
1. A method comprising: identifying, via a computing device, an inbox of a user comprising a set of messages; parsing, via the computing device, each identified message in the set; identifying, via the computing device, based on said parsing, message data and metadata for each message; identifying, via the computing device, a criteria associated with a content recommendation, said criteria corresponding to a respective content type; analyzing, via the computing device, the message data and metadata and identifying a number of content types including said respective content type based on said analysis; generating, via the computing device, based on said analysis, a confidence graph comprising an entry for each content type of the number of identified content types, said confidence graph comprising a score for each identified content type, the score for said respective content type being based on the number of messages in the set mapping to said criteria corresponding to said respective content type, said respective content type's score indicating a degree of confidence in the user's interest in said respective content type; and generating, via the computing device, an interest profile for the user based on said generated confidence graph comprising the score for said respective content type, the interest profile comprising information indicating an interest of the user in said respective content type.
1. A method comprising the steps of: identifying, via a computing device, an inbox of a user, said inbox comprising a set of messages associated with the user; parsing, via the computing device, each identified message in the set, and based on said parsing, identifying message data and metadata for each message and account information for said inbox; analyzing, via the computing device, the identified message data and metadata, and based on said analysis, mapping each identified message based on each message's data and metadata, said mapping providing information indicating types of messages within said set of messages; determining, via the computing device, metrics for each type of message, said metrics indicating a heuristic value representing each message type's presence in the user's inbox, said each heuristic value indicating how often a message type appears in said message set and how recent a message type within said message set appeared in said inbox; determining, via the computing device, based on said metrics, a weight for each type of message, said weight determination comprising analyzing said account information and said metrics, and determining weight values for each message type; creating, via the computing device, an n-dimensional vector for each message type based on the determined weights for each message type, each vector comprising nodes corresponding to the message data and metadata of messages of a respective type; generating, via the computing device, a confidence graph based on said created vectors, said confidence graph being a compilation of the created vectors that digitally represents current interests of the user depicted by said inbox; and generating, via the computing device, an interest profile for the user based on said generated confidence graph.
1. A method comprising: identifying, via a computing device, message data and metadata for each message in a set of messages associated with a user; analyzing, via the computing device, the message data and metadata and identifying a number of content types based on the-analysis; generating, via the computing device, based on the analysis, a confidence graph comprising an entry for each content type of the number of identified content types, the confidence graph comprising a score for each identified content type, the score for a respective content type being based on the number of messages in the set mapping to the respective content type, the respective content type’s score that is based on the number of messages indicating a degree of confidence in the user’s interest in the respective content type; and generating, via the computing device, an interest profile for the user based on the generated confidence graph, the interest profile comprising information indicating an interest of the user in the respective content type.
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 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under AIA 35 U.S.C. 103 as being unpatentable over Kursun et al. (US 20150039705 A1) in view of Bhagwan et al. (US 20170272525 A1).
Regarding claims 1, 11 and 20 Kursun discloses a method comprising: identifying, via a computing device, message data and metadata for each message in a set of messages associated with a user (see Kursun paragraph [0006], a method may include (1) accessing a message repository for a user, the message repository comprising a plurality of messages; (2) for each of the plurality of messages: (a) collect user behavior with regard to the message);
analyzing, via the computing device, the message data and metadata and identifying a number of content types based on the analysis (see Kursun paragraph [0006], analyze the message; (c) generate an initial message priority based on the user behavior and the analysis; (d) compare the initial message priority to historical message priority data for the user; and (e) adjust the initial message priority based on the historical message priority data);
…a score for each identified content type, the score for a respective content type being based on the number of messages in the set mapping to the respective content type (see Kursun paragraphs [0188], a custom weight factor may be calculated for the message based on one or more factor. In one embodiment, the custom weight factor may consider more than one of the request markers/scores, action markers/scores, question markers/scores, time markers/scores, priority markers/scores markers. In one embodiment, the factors may be given different weightings), the respective content type's score that is based on the number of messages (see Kursun paragraphs [0046], the recipient electronic message delivery priority data may be based on a number of messages in an electronic message repository for the recipient) indicating a degree of confidence in the user's interest in the respective content type (see Kursun paragraphs [0210]-[0213], the message may be linked to the associated cluster for visualization and categorization. [0211] In step 555, external, internal, and or user-specified taxonomy may be linked to the keyword categorization of the messages. For example, external taxonomy may be based on third party databases, such as Wikipedia. Internal taxonomy may be based on user workspaces. [0212] In step 560, the message may be categorized hierarchically based on the taxonomy. [0213] In step 565, the message may be visually presented to the user. For example, the message may be presented based on one or more of the topic, hierarchy and thread information);
generating, via the computing device, an interest profile for the user based on the generated confidence graph, the interest profile comprising information indicating an interest of the user in the respective content type (see Kursun paragraphs [0153]-[154], a priority metric for the message or group of messages may be calculated. In one embodiment, the combined priority metric may be based on two or more of the following factors: interactive user behavior, explicit user feedback, history table, keyword match, and priority match. In one embodiment, each factor may be given a specific weighing. The factors that are considered and the weighting may be based, for example, the individual, the organization, etc. and may vary as necessary and/or desired; the user's priority history for messages may be retrieved from, for example, a database. In one embodiment, the priority history table may be indexed by, for example, user, project, topic, etc. [0155] In step 235, a check is made to see if the priority metric matches the priority history. If it does, in step 240, a confidence score may be updated along with the historical database).
Bhagwan expressly discloses generating, via the computing device, based on the analysis, a confidence graph comprising an entry for each content type of the number of identified content types (see Bhagwan paragraphs [0042]-[0043], the data that is collected can be used to generate a detailed and accurate interest graph of each messaging user.
[0043] While embodiments of the present disclosure are described using terms such as interest graph, interest taxonomy and interest profile, any structure can be used for associating a user and the interests identified for the user).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Bhagwan into the method of Kursun to have generating, via the computing device, based on the analysis, a confidence graph comprising an entry for each content type of the number of identified content types. Here, combining Bhagwan with Kursun, which are both related to data processing, improves Kursun by providing system for using at least one interest taxonomy, an interest of the user using the collected information, the at least one user interest for use in identifying at least one content item to be presented to the user at the user's computing device (see Bhagwan paragraph [0004]).
Regarding claims 2 and 12 Kursun expressly discloses identifying, by the computing device, content using the interest profile (see Kursun paragraph [0061], machine learning based message prioritization that is based on users behavior is disclosed. For example, the user's response patterns, time to respond, time to read, order of read, time and order of action (e.g., respond, forward, schedule, email, etc.) may be considered to prioritize messages.).
Regarding claims 3 and 13 Kursun discloses: providing, by the computing device, the identified content to a device of the user (see Kursun paragraph [0128] the prioritization of incoming electronic messages, such as email, and the use of machine learning and advanced visualization techniques to assist in prioritizing and presenting messages to users).
Regarding claims 4 and 14 Kursun expressly discloses: analyzing, by the computing device, the interest profile, and based on the analysis, identifying a current interest of the user, the identified content corresponding to the identified current interest of the user (see Kursun paragraphs [0158]-[160], the keyword and/or priority list may be updated. In one embodiment, the user may be asked to confirm the update if the confidence in the keywords is not above a predetermined threshold. [0159] Referring to FIG. 3, a method for determining a priority of an electronic message is provided. In step 310, for each incoming email, email content, header information, etc. may be accessed to determine connectivity. [0160] In step 320, the system may calculate a hierarchy priority score for the message).
Regarding claims 5 and 15, Kursun expressly discloses recursively updating the confidence graph, the recursive updating comprising performing the identifying, analyzing and generating elements for a new set of messages (see Kursun paragraphs [0158]-[160], the keyword and/or priority list may be updated. In one embodiment, the user may be asked to confirm the update if the confidence in the keywords is not above a predetermined threshold. [0159] Referring to FIG. 3, a method for determining a priority of an electronic message is provided. In step 310, for each incoming email, email content, header information, etc. may be accessed to determine connectivity. [0160] In step 320, the system may calculate a hierarchy priority score for the message).
Regarding claims 6 and 16, Kursun expressly discloses detecting a trigger and recursively updating the confidence graph in response to the detected trigger (see Kursun paragraphs [0158]-[160], the keyword and/or priority list may be updated. In one embodiment, the user may be asked to confirm the update if the confidence in the keywords is not above a predetermined threshold. [0159] Referring to FIG. 3, a method for determining a priority of an electronic message is provided. In step 310, for each incoming email, email content, header information, etc. may be accessed to determine connectivity. [0160] In step 320, the system may calculate a hierarchy priority score for the message).
Regarding claims 7 and 17, Kursun discloses wherein the trigger is selected from a group consisting of: a time period, when a new message is received, when the user logs into a messaging account, when a user action is detected in connection an inbox of the user, when the user logs out of the messaging account, and at a preset time or date (see Kursun paragraphs [0018], at least one computer processor analyzing a message content; (2) the at least one computer processor identifying a request aspect in the message content; (3) the at least one computer processor identifying a time aspect in the message content; (4) the at least one computer processor identifying at least one project associated with the message content and retrieving the priority of the associated project; and (5) the at least one computer processor generating a custom weight factor based on the request, the time aspect, and the associated project priority; see Kursun paragraphs [0158]-[160], the keyword and/or priority list may be updated. In one embodiment, the user may be asked to confirm the update if the confidence in the keywords is not above a predetermined threshold. [0159] Referring to FIG. 3, a method for determining a priority of an electronic message is provided. In step 310, for each incoming email, email content, header information, etc. may be accessed to determine connectivity. [0160] In step 320, the system may calculate a hierarchy priority score for the message).
Regarding claim 8, Kursun identifying, by the computing device, a user group comprising the user and one or more other users using information about each user in the user group (see Kursun paragraphs [0268], a system for message processing according to one embodiment is disclosed. An organization, enterprise, corporation, agency, group of users, etc. may be provided Message Control and Priority Engine 1210 that may receive external messages, store messages, prioritize messages as they are received or sent within the organization, control the manner in which messages are delivered to users, control registration, set organizational policies, etc);
… a score for each content type identified for the user group, the score for a respective content type being based on the number of messages in the second set mapping to the respective content type (see Kursun paragraphs [0188], a custom weight factor may be calculated for the message based on one or more factor. In one embodiment, the custom weight factor may consider more than one of the request markers/scores, action markers/scores, question markers/scores, time markers/scores, priority markers/scores markers. In one embodiment, the factors may be given different weightings), the respective content type's score indicating a degree of confidence in the user group's interest in the respective content type (see Kursun paragraphs [0210]-[0213], the message may be linked to the associated cluster for visualization and categorization. [0211] In step 555, external, internal, and or user-specified taxonomy may be linked to the keyword categorization of the messages. For example, external taxonomy may be based on third party databases, such as Wikipedia. Internal taxonomy may be based on user workspaces. [0212] In step 560, the message may be categorized hierarchically based on the taxonomy. [0213] In step 565, the message may be visually presented to the user. For example, the message may be presented based on one or more of the topic, hierarchy and thread information); and
generating, via the computing device, an interest profile for the user group based on the user group's confidence graph, the user group's interest profile comprising information indicating an interest of the user group in the respective content type (see Kursun paragraphs [0153]-[154], a priority metric for the message or group of messages may be calculated. In one embodiment, the combined priority metric may be based on two or more of the following factors: interactive user behavior, explicit user feedback, history table, keyword match, and priority match. In one embodiment, each factor may be given a specific weighing. The factors that are considered and the weighting may be based, for example, the individual, the organization, etc. and may vary as necessary and/or desired; the user's priority history for messages may be retrieved from, for example, a database. In one embodiment, the priority history table may be indexed by, for example, user, project, topic, etc. [0155] In step 235, a check is made to see if the priority metric matches the priority history. If it does, in step 240, a confidence score may be updated along with the historical database; see Kursun paragraphs [0252] In step 1030, a visual entity for the message in the graph database may be generated. In one embodiment, a graph database (or any other suitable database) may store the connectivity information among users, emails, projects, etc. In one embodiment, the message may be linked to other messages in a thread. The timing of the messages may be represented visually.).
Bhagwan expressly discloses generating, via the computing device, a confidence graph for the identified user group using message data and metadata for a second set of messages associated with the identified user group, the user group's confidence graph comprising an entry for each content type of a number of content types identified for the user group using the second set of messages, (see Bhagwan paragraphs [0042]-[0043], the data that is collected can be used to generate a detailed and accurate interest graph of each messaging user. [0043] While embodiments of the present disclosure are described using terms such as interest graph, interest taxonomy and interest profile, any structure can be used for associating a user and the interests identified for the user).
It would have been obvious to a person of ordinary skill in art before the effective filing date of the claimed invention to incorporate the teaching of Bhagwan into the method of Kursun to have generating, via the computing device, based on the analysis, a confidence graph comprising an entry for each content type of the number of identified content types. Here, combining Bhagwan with Kursun, which are both related to data processing, improves Kursun by providing system for using at least one interest taxonomy, an interest of the user using the collected information, the at least one user interest for use in identifying at least one content item to be presented to the user at the user's computing device (see Bhagwan paragraph [0004]).
Regarding claims 9 and 19 Kursun discloses one or more of demographic information, geographic location information, types of activities, and types of messages (see Kursun paragraph [0133], the type of message (e.g., attachment, plain text, etc.) may be considered. In still another embodiment, the location of the user when the message was first presented (e.g., office, home, remote, etc.) may be considered. In yet another embodiment, the activity of the user (e.g., what the user was doing when the email was first presented, such as working on a document, on a conference call, scheduled as busy, etc.) as well as other factors to predict the user's future behavior).
Regarding claim 10 Kursun expressly discloses identifying, by the computing device, content using the interest profile generated for the user group; and providing, by the computing device, the identified content to a device of a user in the user group (see Kursun paragraph [0063] message prioritization that may be based on collective feedback from the receivers of a group messages is disclosed. For example, a message that is given a low priority or unread by a group of people may be given a low priority for all receivers; see Kursun paragraph [0268], a system for message processing according to one embodiment is disclosed. An organization, enterprise, corporation, agency, group of users, etc. may be provided Message Control and Priority Engine 1210 that may receive external messages, store messages, prioritize messages as they are received or sent within the organization, control the manner in which messages are delivered to users, control registration, set organizational policies, etc.).
Response to arguments
Applicant’s argument states that “[a]ccording to the claimed subject matter, message data and metadata of each message in the claimed set of messages is analyzed to identify a number of content types. Kursun’s description of analyzing a message to identify a priority for the message fails to discloses at least this claimed analysis”. Examiner respectfully disagrees with the applicant’s argument because Kursun discloses analyzing messages to identify a number of content types and identify a priority for the message. Kursun discloses, see Kursun paragraph [0018], a method that may include (1) at least one computer processor analyzing a message content; (2) the at least one computer processor identifying a request aspect in the message content; (3) the at least one computer processor identifying a time aspect in the message content; (4) the at least one computer processor identifying at least one project associated with the message content and retrieving the priority of the associated project; and (5) the at least one computer processor generating a custom weight factor based on the request, the time aspect, and the associated project priority.
Applicant further argues that “[a]ccording to the claimed subject matter, the claimed score is for a respective content type, is based on the number of messages that map to the respective content type; and indicates a degree of confidence in the claimed user’s interest in the respective content type. Kursun’s description of using a custom weight factor based on a “marker” to determine a priority for a given message fails to disclose or suggest at least this claimed subject matter”. Examiner respectfully disagrees with the applicant’s argument because Kursun discloses the score for a respective content type. Kursun discloses, see Kursun paragraph [0012], a method that includes (1) the at least one computer processor calculating an internal hierarchy score for a message based on a position of a sender of the message within the organization; (2) the at least one computer processor determining an associated project priority score for at least one project that is associated with the message; (3) the at least one computer processor calculating a related message priority connectivity score with high priority items; and (4) the at least one computer processor calculating a combined priority metric for the message based on the internal hierarchy score, the associated project priority score, and the related message score.
Applicant argues that “[i]n stark contrast to Kursun’s description of prioritizing delivery of a message based on the number of messages in a recipient repository, the claimed subject matter is determining a score for one or more of the identified content types based on the number of messages in the claimed set that map to the identified content type”. Examiner respectfully disagrees with the applicant’s argument because Kursun discloses, see Kursun paragraph [0012], determining an associated project priority score for at least one project that is associated with the message.
Kursun fails to disclose or even suggest the claimed interest profile generation, let alone the claimed use of the claimed confidence graphic as a basis for generating the claimed interest profile, where the claimed interest profile comprises information indicating an interest of the user in the claimed respective content. Examiner respectfully disagrees with the applicant’s argument because Kursun discloses, see Kursun paragraph [076], an enterprise/organization messaging controller may oversee the process of messaging controller interactions and message exchange patterns and may create a priority for messages through collecting priority from all parties involved, history behavior, policies preferences, etc, Kursun further discloses a dynamic profile and learning engine that tracks a user's behavioral profile phases and identifies phase patterns to adopt, switches between phases as pattern identified, etc. is disclosed. According to Kursun, see Kusun paragraph [0080], graph databases and profile tables that may store individual user profiles, connectivity through projects/socially/enterprise hierarchy, external connectivity, message exchange connectivity (connectivity info can be stored in graph databases, other profile data can be stored in profile tables or other) are disclosed.
In stark contrast to Bhagwan’s interest graph, the claimed subject matter recites a confidence graph and an interest profile, where the claimed confidence graph is used to generate the claimed interest profile. Bhagwan’s description of generation an interest graph using data associated with a user-selected URL fails to disclose or suggest at least this claimed subject matter. Examiner respectfully, see Kusun paragraph [0080], graph databases and profile tables that may store individual user profiles, connectivity through projects/socially/enterprise hierarchy, external connectivity, message exchange connectivity (connectivity info can be stored in graph databases, other profile data can be stored in profile tables or other) are disclosed.
Remarks
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Lu et al. (US 20170308533 A1) discloses the scoring module 230 is configured to determine that certain emails from among a set of emails within a user's email inbox are important to the specific member and therefore of interest to the member. The determining certain emails are of potential interest is based on member profile information and member activities. In some embodiments, the scoring module 230 determines certain emails are of interest to the member is further based on a relevance score transgressing a threshold, the relevance score being calculated based on a relevance ranking using vector space model comparison between each email within the set of emails and the member profile.
Conclusion
THIS ACTION IS MADE FINAL. 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 extension fee 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 DINKU GEBRESENBET whose telephone number is 571-270-1636. The examiner can normally be reached between 8am-5pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Amy Ng can be reached on 571- 270-1698. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DINKU W GEBRESENBET/Primary Examiner, Art Unit 2164
Prosecution Insights