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 .
Application 19/231,342 is a continuation of Application 18/223,354 (now US Patent 12335208 B2), which in turn is a continuation of Application 17/133,432 (now US Patent 11729122 B2), filed 12/23/2020.
Applicant’s preliminary amendment dated 11/05/2025 has been received and made of record. Claims 1-20 have been canceled. New claims 21-40 have been added.
Claims 21-40 are currently pending in Application 19/231,342.
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 filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual 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/apply/applying-online/eterminal-disclaimer.
Claims 21-40 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-20 of U.S. Patent No. 12335208 B2. Although the claims at issue are not identical, they are not patentably distinct from each other because the patent claims anticipate the application claims with the differences amounting to matters of drafting choice or obvious variations that would have been obvious to a person having ordinary skill in the art prior to the effective filing date of the application. See also the table below.
Patent US 12335208 B2
Application 19/231,342
1. A content suggestion system for suggesting one or more content items to a client application on a client device, the content suggestion system comprising: a content suggestion service; and a collaborative content management and communication system communicably coupled to the content suggestion service and comprising a store of content items associated with an organization, wherein a respective content item in the store of content items is associated with a respective access vector generated at least in part by analyzing content interactions with the respective content item by one or more users in the organization; wherein the content suggestion service is configured to, during a real-time chat session between a first user and a second user: receive one or more communication events exchanged between the first user and the second user; determine, using the received one or more communication events, a subject of the real-time chat session; obtain an identity vector of the first user; identify, in the store of content items, a candidate content item associated with an access vector that satisfies a similarity threshold with the identity vector of the first user; and cause an identifier of the candidate content item to be displayed to the first user in a graphical user interface associated with the real-time chat session.
2. The content suggestion system of claim 1, wherein: the identity vector is a first identity vector; the candidate content item is a first candidate content item; the access vector is a first access vector; and the content suggestion service is further configured to, during the real-time chat session: obtain a second identity vector of the second user; identify, in the store of content items, a second candidate content item associated with a second access vector that satisfies a similarity threshold with the second identity vector of the second user, the second candidate content item different than the first candidate content item; and cause an identifier of the second candidate content item to be displayed to the second user.
3. The content suggestion system of claim 1, wherein the identity vector of the first user is based at least in part on a content interaction history of the first user.
4. The content suggestion system of claim 3, wherein the identity vector of the first user is based at least in part on a position of the first user in the organization.
5. The content suggestion system of claim 1, wherein determining the subject of the real-time chat session comprises at least one of: analyzing the received one or more communication events to determine a semantic content of the received one or more communication events; or obtaining a user-defined topic of the real-time chat session.
6. The content suggestion system of claim 1, wherein the respective access vector of the respective content item is based at least in part on: an interaction with the respective content item by a third user different from the first user and the second user; and a position of the third user in the organization.
7. A content suggestion system for suggesting one or more content items to a client application on a client device, the content suggestion system comprising: a content suggestion service; and a collaborative content management and communication system communicably coupled to the content suggestion service and comprising: content items associated with an organization; and a multi-dimensional association graph defining associations between the content items and users in the organization, a respective association between a respective content item and a respective user generated at least in part by analyzing a content interaction history between the respective content item and the respective user; wherein the content suggestion service is configured to, during a real-time chat session between a first user and a second user: receive one or more communication events exchanged between the first user and the second user; determine, using the received one or more communications, a subject of the real-time chat session; obtain an identity vector of the first user; analyze the multi-dimensional association graph to identify, based at least in part on the associations between the content items and the users in the organization, a set of candidate content items related to the subject of the real-time chat session and to the identity vector of the first user; and cause an identifier of at least one content item of the set of candidate content items to be displayed to the first user in a graphical user interface associated with the real-time chat session.
8. The content suggestion system of claim 7, wherein the content interaction history between the respective content item and the respective user includes at least one of an access of the respective content item by the respective user, an edit of the respective content item by the respective user, a comment on the respective content item by the respective user, or an authoring of the respective content item by the respective user.
9. The content suggestion system of claim 7, wherein: the set of content items is a first set of content items; and obtaining the identity vector of the first user comprises analyzing the multi-dimensional association graph to identify: a second set of content items with which the first user has interacted; and one or more attributes of each of the second set of content items.
10. The content suggestion system of claim 9, wherein obtaining the identity vector further comprises generating the identity vector based at least in part on the one or more attributes of each of the second set of content items.
11. The content suggestion system of claim 9, wherein the one or more attributes include one or more of: a content type; a technical rating; a file storage location; and an associated department within the organization.
12. The content suggestion system of claim 7, wherein: the identity vector is a first identity vector; the set of candidate content items is a first set of candidate content items; the graphical user interface is a first graphical user interface displayed on a first client device to the first user; and the content suggestion service is further configured to, during the real-time chat session: obtain a second identity vector of the second user; analyze the multi-dimensional association graph to identify, based at least in part on the associations between the content items and the users in the organization, a second set of candidate content items related to the subject of the real-time chat session and to the identity vector of the second user; and cause an identifier of at least one content item of the second of candidate content items to be displayed to the second user in a second graphical user interface associated with the real-time chat session, the second graphical user interface displayed to the second user on a second client device different from the first client device.
13. A content suggestion system for suggesting one or more content items to a client application on a client device, the content suggestion system comprising: a content suggestion service; and a collaborative content management and communication system communicably coupled to the content suggestion service and comprising content items associated with an organization; wherein the content suggestion service is configured to: generate a multi-dimensional association graph defining associations between the content items and users in the organization by analyzing content interaction histories of a plurality of users in the organization; and during a real-time chat session between a first user and a second user: receive one or more communication events exchanged between the first user and the second user; determine, using the received one or more communications, a subject of the real-time chat session; obtain an identity vector of the first user; analyze the multi-dimensional association graph to identify a set of candidate content items related to the subject of the real-time chat session and to the identity vector of the first user; and cause an identifier of at least one content item of the set of candidate content items to be displayed to the first user in a graphical user interface associated with the real-time chat session.
14. The content suggestion system of claim 13, wherein the content suggestion service is further configured to, after generating the multi-dimensional association graph, generate an updated multi-dimensional association graph by analyzing new content interaction histories of the plurality of users in the organization.
15. The content suggestion system of claim 13, wherein the identity vector of the first user is based at least in part on a content interaction history of the first user.
16. The content suggestion system of claim 15, wherein the identity vector of the first user is based at least in part on a position of the first user in the organization.
17. A content suggestion system for suggesting one or more content items to a client application on a client device, the content suggestion system comprising: a content suggestion service; and a collaborative content management and communication system communicably coupled to the content suggestion service and comprising content items associated with an organization; wherein the content suggestion service is configured to, during a real-time communication session between a first user and a second user: receive one or more communication events exchanged between the first user and the second user during the real-time communication session; determine a first query based at least in part on: the one or more communication events; and an identifier of the first user; and determine a second query based at least in part on: the one or more communication events; and an identifier of the second user; query the collaborative content management and communication system using the first query and the second query; receive, from the collaborative content management and communication system and in response to the first query, a first set of candidate content items related to the first query; receive, from the collaborative content management and communication system and in response to the second query, a second set of candidate content items related to the second query, the second set of candidate content items different from the first set of candidate content items; cause a first identifier of a first content item from the first set of candidate content items to be displayed to the first user; and cause a second identifier of a second content item from the second set of candidate content items to be displayed to the second user.
18. The content suggestion system of claim 17, wherein the operation of determining the first query includes performing a natural language processing operation on the one or more communication events to determine a subject of the one or more communication events.
19. The content suggestion system of claim 17, wherein the content suggestion service is further configured to, after causing the first identifier of the first content item to be displayed to the first user: receive one or more subsequent communication events exchanged between the first user and the second user during the real-time communication session; determine a third query based at least in part on: the one or more subsequent communication events; and the identifier of the first user; query the collaborative content management and communication system using the third query; receive, from the collaborative content management and communication system and in response to the third query, a third set of candidate content items related to the third query, the third set of candidate content items different from the first set of candidate content items; and cause a third identifier of a third content item from the third set of candidate content items to be displayed to the first user.
20. The content suggestion system of claim 19, wherein: the first identifier of the first content item is displayed to the first user while the one or more communication events are displayed to the first user; and the third identifier of the third content item is displayed to the first user while the one or more additional communication events are displayed to the first user.
21. A content suggestion system comprising: a processor resource; a memory resource storing instructions, which upon execution by the processor resource, cause the processor to: instantiate a document management system communicably coupled to a content suggestion service and comprising a plurality of documents and a communication service, wherein the content suggestion service is configured to: receive one or more communication events exchanged between a plurality of users of the document management system via the communication service; determine, using the received one or more communication events, a subject of the one or more communication events; for at least one respective user of the plurality of users: obtain an identity vector associated with the respective user; identify a set of documents for the respective user using the subject of the one or more communication events, the identity vector, and an association graph defining associations between documents based on interaction events associated with the respective user; and cause display, in a user interface instantiated with respect to the respective user and communicably coupled to the communication service, of a content suggestion pane including the set of documents.
22. The content suggestion system of claim 21, wherein: the content suggestion service is further configured to: for the at least one respective user of the plurality of users, identify a set of other users using the subject of the one or more communication events, the identity vector, and the association graph; and cause display, in the content suggestion pane, of a personnel content suggestion element including a user identifier of a particular user of the set of other users.
23. The content suggestion system of claim 22, wherein the personnel content suggestion element is a selectable graphical element configured to initiate a communication session between the at least one respective user and the particular user of the set of other users in response to receiving a user selection input.
24. The content suggestion system of claim 22, wherein causing display of the set of documents within the content suggestion pane comprises causing display of a selectable graphical element configured to cause a particular document of the set of documents to be displayed in the user interface or downloaded in response to receiving a user selection input.
25. The content suggestion system of claim 21, wherein: the one or more communication events include an audio recording communication event; the content suggestion system is further configured to analyze the audio recording communication event to produce text based on an audio recording associated with the audio recording communication event; and determining the subject of the one or more communication events includes analyzing the text based on the audio recording.
26. The content suggestion system of claim 21, wherein the interaction events comprise at least one of: a comment interaction event; an edit interaction event; a view interaction event; or an authored interaction event.
27. The content suggestion system of claim 21, wherein the identity vector associated with the respective user is generated based on at least one of: previous content interactions by the respective user; a position of the respective user within an organization; or a technical expertise of the respective user.
28. A method for suggesting content in a collaborative system comprising: receiving one or more communication events exchanged between a plurality of users of a collaborative content management system via a communication service communicably coupling the plurality of users; determining, using the received one or more communication events, a subject of the one or more communication events; for a first user of the plurality of users: obtaining a first identity vector associated with the first user; identifying a first set of content items for the first user using the subject of the one or more communication events, the first identity vector, and an association graph defining associations between content items based on interaction events associated with the first user; and causing display, in a first user interface instantiated with respect to the first user and communicably coupled to the communication service, of a first content suggestion pane including the first set of content items; for a second user of the plurality of users: obtaining a second identity vector associated with the second user; identifying a second set of content items for the second user using the subject of the one or more communication events, the second identity vector, and the association graph defining associations between content items based on interaction events associated with the second user, the second set of content items including at least one content item that is not in the second set of content items; and causing display, in a second user interface instantiated with respect to the second user and communicably coupled to the communication service, of a second content suggestion pane including the second set of content items.
29. The method of claim 28, further comprising generating the association graph, using at least a content interaction history including the interaction events associated with the first user, wherein: the content interaction history for the first user comprises at least one of: an access event between the first user and a content item of the first set of content items; an edit event between the first user and the content item of the first set of content items; or a comment event between the first user and the content item of the first set of content items.
30. The method of claim 29, wherein the content item includes a set of attributes comprising: a content type attribute; a file storage location attribute; a technical rating attribute; and an organization department attribute.
31. The method of claim 30, further comprising generating the first identity vector based at least in part on the set of attributes of the content item.
32. The method of claim 28, wherein determining, using the received one or more communication events, the subject of the one or more communication events includes at least one of: analyzing the one or more communication events to determine a semantic content of the one or more communication events; or retrieving a user-defined topic of the one or more communication events.
33. The method of claim 28, further comprising: subsequent to causing display of either the first content suggestion pane or the second content suggestion pane, update the association graph by analyzing new content interaction histories of a plurality of users including the first user and the second user.
34. The method of claim 28, wherein: the first identity vector is generated based in part on a role of the first user maintained by the collaborative content management system; and the second identity vector is generated based in part on a role of the second user maintained by the collaborative content management system.
35. A method for suggesting content in a collaborative system comprising: a content suggestion service communicably coupled to a real-time communication service communicably coupled to a plurality of user interfaces instantiated with respect to a plurality of users of a collaborative content management system, wherein the content suggestion service is configured to: receive one or more communication events exchanged between the plurality of users via the real-time communication service; determining, using the received one or more communication events, a subject of the one or more communication events; for at least one respective user of the plurality of users: obtaining an identity vector associated with the respective user; identifying a set of content items for the respective user using the subject of the one or more communication events, the identity vector, and an association graph defining associations between content items based on interaction events associated with the respective user; and causing display, in a user interface instantiated with respect to the respective user and communicably coupled to the real-time communication service, of a content suggestion pane including the set of content items.
36. The method of claim 35, further comprising generating, using at least a content interaction history, the association graph, wherein the content interaction history comprises at least one of: an access event between a particular user of the plurality of users and a content item of the set of content items; an edit event between the particular user of the plurality of users and the content item of the set of content items; or a comment event between the particular user of the plurality of users and the content item of the set of content items.
37. The method of claim 35, wherein: the one or more communication events include an audio recording communication event; the content suggestion service is further configured to analyze the audio recording communication event to produce text based on an audio recording associated with the audio recording communication event; and determining the subject of the one or more communication events includes analyzing the text based on the audio recording.
38. The method of claim 35, wherein causing display of the set of content items within the content suggestion pane comprises causing display of a selectable graphical element configured to, in response to receiving a user selection input of the selectable graphical element, cause a particular content item of the set of content items to be displayed in the user interface or downloaded.
39. The method of claim 35, wherein the identity vector associated with the respective user is generated based on at least one of: previous content interactions by the respective user; a position of the respective user within an organization; or a technical expertise of the respective user.
40. The method of claim 35, wherein: the content suggestion service is further configured to: for the at least one respective user of the plurality of users, identify a set of other users using the subject of the one or more communication events, the identity vector, and the association graph; and cause display, in the content suggestion pane, of a personnel content suggestion element including a user identifier of a particular user of the set of other users.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claim(s) 21-24, 26-36, and 38-40* is/are rejected under 35 U.S.C. 103 as being unpatentable over Calagrosso (US 2019/0179494 A1) in view of Yu (US 2010/0131523 A1).
Regarding claim 21, Calagrosso discloses A content suggestion system (Calagrosso: Paragraph [0096], “the computer system 1400…”) comprising:
a processor resource (Calagrosso: Paragraph [0096], “the computer system 1400 may include a processing device 1402, a volatile memory 1404 (e.g., random access memory (RAM)), a non-volatile memory 1406 (e.g., read-only memory (ROM) or electrically-erasable programmable ROM (EEPROM)), and a data storage device 1416, which may communicate with each other via a bus 1408”);
a memory resource storing instructions, which upon execution by the processor resource (Calagrosso: Paragraph [0096], “the computer system 1400 may include a processing device 1402, a volatile memory 1404 (e.g., random access memory (RAM)), a non-volatile memory 1406 (e.g., read-only memory (ROM) or electrically-erasable programmable ROM (EEPROM)), and a data storage device 1416, which may communicate with each other via a bus 1408”), cause the processor to:
instantiate a document management system communicably coupled to a content suggestion service and comprising a plurality of documents and a communication service (Calagrosso: Paragraph [0027], “the data store 114 may be a distributed data store including multiple repositories, databases, etc. and may store data of various collaboration tools including document applications (e.g., word processor, presentation, and spreadsheet applications), a cloud-based document storage service, an online calendar service, an email service, a messenger”), wherein the content suggestion service is configured to:
receive one or more communication events exchanged between a plurality of users of the document management system via the communication service (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages. In some implementations, the analyzed actions of the user may be within the last predetermined number of days. The identified users may represent past collaborators the user have worked with”);
determine, using the received one or more communication events, properties of the one or more communication events (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages”);
for at least one respective user of the plurality of users:
obtain an identity vector associated with the respective user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
identify a set of documents for the respective user using the subject of the one or more communication events, the identity vector, and an association graph defining associations between documents based on interaction events associated with the respective user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
and cause display, in a user interface instantiated with respect to the respective user and communicably coupled to the communication service, of a content suggestion pane including the set of documents (Calagrosso: Figure 13 and Paragraph [0090], “FIG. 13 illustrates an example UI 1300 of the cloud-based content management platform 115 displaying a list of documents shared with a user in a first area 1310 of the UI along with predicted collaboration information for the user (prediction cards 1302, 1304, and 1306) in a second area 1307 of the UI, in accordance with one implementation of the disclosure”).
Calagrosso does not explicitly disclose determining a subject of the communication events.
However, Yu teaches this feature (Yu: Paragraph [0043], “the system defines "People", "Mail Subject", "Keywords", and "Documents" as nodes of the graph”, and Paragraph [0038], “certain information is extracted as an email context from the email, such as, for example, email properties (e.g., sender, recipient, subject, and/or keyword). At block 402, a search is performed in a database based on the extracted information. The database stores relationship information between certain prior emails and certain documents. At block 403, a list of one or more documents are identified and generated that are related to at least a portion of the extracted information from the email context. At block 404, the list of one or more related documents is suggested and presented to the user with ratings of the documents representing a relevancy level of each identified document”).
Calagrosso and Yu are analogous art in the same field of endeavor as the instant invention as all are drawn to document recommendation systems. 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; that is, it would have been obvious to incorporate Yu’s context properties including subject/topic into the system of Calagrosso to improve the quality of suggestions.
Calagrosso-Yu teaches 22. The content suggestion system of claim 21, wherein:
the content suggestion service is further configured to:
for the at least one respective user of the plurality of users, identify a set of other users using the subject of the one or more communication events, the identity vector, and the association graph (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
and cause display, in the content suggestion pane, of a personnel content suggestion element including a user identifier of a particular user of the set of other users (Calagrosso: Figure 13 and Paragraph [0090], “a prediction card 1302, 1304, or 1306 based on the predicted collaborators and predicted responses”).
Calagrosso-Yu teaches 23. The content suggestion system of claim 22, wherein the personnel content suggestion element is a selectable graphical element configured to initiate a communication session between the at least one respective user and the particular user of the set of other users in response to receiving a user selection input (Calagrosso: Paragraph [0036], “ In response to the user selecting the predicted response UI component 204B, the server 112 may open the document and provide a comment view in the document. In the comment view, the server 112 may allow the user to view the full comment by the predicted collaborator, “Liam O'Connor” and to reply or resolve (e.g., accept or reject) the comment”).
Calagrosso-Yu teaches 24. The content suggestion system of claim 22, wherein causing display of the set of documents within the content suggestion pane comprises causing display of a selectable graphical element configured to cause a particular document of the set of documents to be displayed in the user interface or downloaded in response to receiving a user selection input (Calagrosso: Paragraph [0036], “ In response to the user selecting the predicted response UI component 204B, the server 112 may open the document and provide a comment view in the document. In the comment view, the server 112 may allow the user to view the full comment by the predicted collaborator, “Liam O'Connor” and to reply or resolve (e.g., accept or reject) the comment”).
Calagrosso-Yu teaches 26. The content suggestion system of claim 21, wherein the interaction events comprise at least one of:
a comment interaction event (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);
an edit interaction event (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);
a view interaction event (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);
or an authored interaction event (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”).
Calagrosso-Yu teaches 27. The content suggestion system of claim 21, wherein the identity vector associated with the respective user is generated based on at least one of:
previous content interactions by the respective user (Calagrosso: Paragraphs [0061]-[0062], “The collaboration attributes may include a frequency of collaboration with the user, a recency of collaboration with the user, a responsiveness of the user to actions of a respective other user associated with the plurality of documents hosted by the cloud-based content management platform 115, and affinity of the user for the respective other user based on the affiliations of the user in association with the cloud-based content management platform 115… the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115”);
a position of the respective user within an organization (Calagrosso: Paragraphs [0061]-[0062], “The collaboration attributes may include a frequency of collaboration with the user, a recency of collaboration with the user, a responsiveness of the user to actions of a respective other user associated with the plurality of documents hosted by the cloud-based content management platform 115, and affinity of the user for the respective other user based on the affiliations of the user in association with the cloud-based content management platform 115… the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115”);
or a technical expertise of the respective user.
Regarding claim 28, Calagrosso discloses 28. A method for suggesting content in a collaborative system (Calagrosso: Claim 1, “A method… identifying the predicted one or more collaborators to direct the user to a subset of documents”) comprising:
receiving one or more communication events exchanged between a plurality of users of a collaborative content management system via a communication service communicably coupling the plurality of users (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages. In some implementations, the analyzed actions of the user may be within the last predetermined number of days. The identified users may represent past collaborators the user have worked with”);
determining, using the received one or more communication events, properties of the one or more communication events (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages”);
for a first user of the plurality of users:
obtaining a first identity vector associated with the first user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
identifying a first set of content items for the first user using the subject of the one or more communication events, the first identity vector, and an association graph defining associations between content items based on interaction events associated with the first user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
and causing display, in a first user interface instantiated with respect to the first user and communicably coupled to the communication service, of a first content suggestion pane including the first set of content items (Calagrosso: Figure 13 and Paragraph [0090], “FIG. 13 illustrates an example UI 1300 of the cloud-based content management platform 115 displaying a list of documents shared with a user in a first area 1310 of the UI along with predicted collaboration information for the user (prediction cards 1302, 1304, and 1306) in a second area 1307 of the UI, in accordance with one implementation of the disclosure”);
for a second user of the plurality of users (the process of Calagrosso is repeatable for multiple users of the system):
obtaining a second identity vector associated with the second user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
identifying a second set of content items for the second user using the subject of the one or more communication events, the second identity vector, and the association graph defining associations between content items based on interaction events associated with the second user, the second set of content items including at least one content item that is not in the second set of content items (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
and causing display, in a second user interface instantiated with respect to the second user and communicably coupled to the communication service, of a second content suggestion pane including the second set of content items (Calagrosso: Figure 13 and Paragraph [0090], “FIG. 13 illustrates an example UI 1300 of the cloud-based content management platform 115 displaying a list of documents shared with a user in a first area 1310 of the UI along with predicted collaboration information for the user (prediction cards 1302, 1304, and 1306) in a second area 1307 of the UI, in accordance with one implementation of the disclosure”).
Calagrosso does not explicitly disclose determining a subject of the communication events.
However, Yu teaches this feature (Yu: Paragraph [0043], “the system defines "People", "Mail Subject", "Keywords", and "Documents" as nodes of the graph”, and Paragraph [0038], “certain information is extracted as an email context from the email, such as, for example, email properties (e.g., sender, recipient, subject, and/or keyword). At block 402, a search is performed in a database based on the extracted information. The database stores relationship information between certain prior emails and certain documents. At block 403, a list of one or more documents are identified and generated that are related to at least a portion of the extracted information from the email context. At block 404, the list of one or more related documents is suggested and presented to the user with ratings of the documents representing a relevancy level of each identified document”).
Calagrosso and Yu are analogous art in the same field of endeavor as the instant invention as all are drawn to document recommendation systems. 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; that is, it would have been obvious to incorporate Yu’s context properties including subject/topic into the system of Calagrosso to improve the quality of suggestions.
Calagrosso-Yu teaches 29. The method of claim 28, further comprising generating the association graph, using at least a content interaction history including the interaction events associated with the first user (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages. In some implementations, the analyzed actions of the user may be within the last predetermined number of days. The identified users may represent past collaborators the user have worked with”), wherein:
the content interaction history for the first user comprises at least one of:
an access event between the first user and a content item of the first set of content items (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);
an edit event between the first user and the content item of the first set of content items (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);
or a comment event between the first user and the content item of the first set of content items (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”).
Calagrosso-Yu teaches 30. The method of claim 29, wherein the content item includes a set of attributes (Yu: Paragraph [0033], “relationship builder 205 is configured to build certain relationships among the collected information (e.g., people, subject, and keyword, etc.) and the associated document or documents, including, for example, people/document relationship 207, subject/document relationship 208, and/or keyword/document relationship 209”).
Yu does not explicitly disclose the attributes comprising:
a content type attribute;
a file storage location attribute;
a technical rating attribute;
and an organization department attribute.
However, these amount to non-functional descriptive material and are not afforded patentable weight (see MPEP 2111.05). Examiner also takes Official Notice that various attributes for files and documents were well-known. It would have been obvious to a person having ordinary skill in the art to have incorporated these attributes in the system of Calagrosso-Yu to cover any desired implementation prior to the effective filing date of the instant application.
Calagrosso-Yu teaches 31. The method of claim 30, further comprising generating the first identity vector based at least in part on the set of attributes of the content item (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages. In some implementations, the analyzed actions of the user may be within the last predetermined number of days. The identified users may represent past collaborators the user have worked with”; Yu: Paragraph [0033], “relationship builder 205 is configured to build certain relationships among the collected information (e.g., people, subject, and keyword, etc.) and the associated document or documents, including, for example, people/document relationship 207, subject/document relationship 208, and/or keyword/document relationship 209”).
Calagrosso-Yu teaches 32. The method of claim 28, wherein determining, using the received one or more communication events, the subject of the one or more communication events includes at least one of:
analyzing the one or more communication events to determine a semantic content of the one or more communication events (Yu: Paragraph [0033], “relationship builder 205 is configured to build certain relationships among the collected information (e.g., people, subject, and keyword, etc.) and the associated document or documents, including, for example, people/document relationship 207, subject/document relationship 208, and/or keyword/document relationship 209”);
or retrieving a user-defined topic of the one or more communication events (Yu: Paragraph [0031], “when a user explicitly creates an association between a remote document and a current email (e.g. tagging), the information extractor 204 automatically collects current email context information including "People", "Subject", and "Keywords" and associates such information with a "User Define" action”).
Calagrosso-Yu teaches 33. The method of claim 28, further comprising:
subsequent to causing display of either the first content suggestion pane or the second content suggestion pane, update the association graph by analyzing new content interaction histories of a plurality of users including the first user and the second user (item (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages”, and Paragraph [0062], “the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702” – this graph is continuously updated as the system continues operation; Yu: Paragraph [0031], “when a user performs a user action (e.g. open and view a document) while accessing an email, information extractor 204 automatically collects and associates the current contextual information, including "People", "Subject", and "Keywords" extracted from the email context, with a "View" action.”).
Calagrosso-Yu teaches 34. The method of claim 28, wherein:
the first identity vector is generated based in part on a role of the first user maintained by the collaborative content management system (Calagrosso: Paragraph [0059], “Examples of the affiliations can be from the user's contact list and a membership to a group space on the cloud-based content management platform 115”);
and the second identity vector is generated based in part on a role of the second user maintained by the collaborative content management system (Calagrosso: Paragraph [0059], “Examples of the affiliations can be from the user's contact list and a membership to a group space on the cloud-based content management platform 115”).
Regarding claim 35, Calagrosso discloses 35. A method for suggesting content in a collaborative system (Calagrosso: Claim 1, “A method… identifying the predicted one or more collaborators to direct the user to a subset of documents”) comprising:
a content suggestion service (Calagrosso: Claim 1, “A method… identifying the predicted one or more collaborators to direct the user to a subset of documents”) communicably coupled to a real-time communication service communicably coupled to a plurality of user interfaces instantiated with respect to a plurality of users of a collaborative content management system (Calagrosso: Paragraph [0027], “the data store 114 may be a distributed data store including multiple repositories, databases, etc. and may store data of various collaboration tools including document applications (e.g., word processor, presentation, and spreadsheet applications), a cloud-based document storage service, an online calendar service, an email service, a messenger”), wherein the content suggestion service is configured to:
receive one or more communication events exchanged between the plurality of users via the real-time communication service (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages. In some implementations, the analyzed actions of the user may be within the last predetermined number of days. The identified users may represent past collaborators the user have worked with”);
determining, using the received one or more communication events, properties of the one or more communication events (Calagrosso: Paragraph [0054], “ At block 702, the collaborator predictor 117 (e.g., collaborator prediction engine 372) may identify, for a user, other users that may have a relationship with the user and may be associated with documents hosted by the cloud-based content management platform 115. The collaborator predictor 117 may determine these other users by analyzing the user's actions (or the user's behavior) associated with documents hosted by the cloud-based content management platform 115, calendar events, email messages and text messages, and identifying other users also associated with those documents, calendar events, email messages and text messages”);
for at least one respective user of the plurality of users:
obtaining an identity vector associated with the respective user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
identifying a set of content items for the respective user using the subject of the one or more communication events, the identity vector, and an association graph defining associations between content items based on interaction events associated with the respective user (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”);
and causing display, in a user interface instantiated with respect to the respective user and communicably coupled to the real-time communication service, of a content suggestion pane including the set of content items (Calagrosso: Figure 13 and Paragraph [0090], “FIG. 13 illustrates an example UI 1300 of the cloud-based content management platform 115 displaying a list of documents shared with a user in a first area 1310 of the UI along with predicted collaboration information for the user (prediction cards 1302, 1304, and 1306) in a second area 1307 of the UI, in accordance with one implementation of the disclosure”).
Calagrosso does not explicitly disclose determining a subject of the communication events.
However, Yu teaches this feature (Yu: Paragraph [0043], “the system defines "People", "Mail Subject", "Keywords", and "Documents" as nodes of the graph”, and Paragraph [0038], “certain information is extracted as an email context from the email, such as, for example, email properties (e.g., sender, recipient, subject, and/or keyword). At block 402, a search is performed in a database based on the extracted information. The database stores relationship information between certain prior emails and certain documents. At block 403, a list of one or more documents are identified and generated that are related to at least a portion of the extracted information from the email context. At block 404, the list of one or more related documents is suggested and presented to the user with ratings of the documents representing a relevancy level of each identified document”).
Calagrosso and Yu are analogous art in the same field of endeavor as the instant invention as all are drawn to document recommendation systems. 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; that is, it would have been obvious to incorporate Yu’s context properties including subject/topic into the system of Calagrosso to improve the quality of suggestions.
Calagrosso-Yu teaches 36. The method of claim 35, further comprising generating, using at least a content interaction history, the association graph, wherein the content interaction history comprises at least one of:
an access event between a particular user of the plurality of users and a content item of the set of content items (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);;
an edit event between the particular user of the plurality of users and the content item of the set of content items (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);;
or a comment event between the particular user of the plurality of users and the content item of the set of content items (Calagrosso: Paragraph [0020], “online collaboration (also referred to herein as “a collaboration” or “a collaborative event”) represents actions of multiple users with respect to a document stored in a cloud-based data storage system. These actions may include, for example, reviewing, sharing, editing, or commenting on a document”);.
Calagrosso-Yu teaches 38. The method of claim 35, wherein causing display of the set of content items within the content suggestion pane comprises causing display of a selectable graphical element configured to, in response to receiving a user selection input of the selectable graphical element, cause a particular content item of the set of content items to be displayed in the user interface or downloaded (Calagrosso: Paragraph [0036], “ In response to the user selecting the predicted response UI component 204B, the server 112 may open the document and provide a comment view in the document. In the comment view, the server 112 may allow the user to view the full comment by the predicted collaborator, “Liam O'Connor” and to reply or resolve (e.g., accept or reject) the comment”).
Calagrosso-Yu teaches 39. The method of claim 35, wherein the identity vector associated with the respective user is generated based on at least one of:
previous content interactions by the respective user (Calagrosso: Paragraphs [0061]-[0062], “The collaboration attributes may include a frequency of collaboration with the user, a recency of collaboration with the user, a responsiveness of the user to actions of a respective other user associated with the plurality of documents hosted by the cloud-based content management platform 115, and affinity of the user for the respective other user based on the affiliations of the user in association with the cloud-based content management platform 115… the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115”) ;
a position of the respective user within an organization (Calagrosso: Paragraphs [0061]-[0062], “The collaboration attributes may include a frequency of collaboration with the user, a recency of collaboration with the user, a responsiveness of the user to actions of a respective other user associated with the plurality of documents hosted by the cloud-based content management platform 115, and affinity of the user for the respective other user based on the affiliations of the user in association with the cloud-based content management platform 115… the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115”);
or a technical expertise of the respective user.
Calagrosso-Yu teaches 40. The method of claim 35, wherein:
the content suggestion service is further configured to:
for the at least one respective user of the plurality of users, identify a set of other users using the subject of the one or more communication events, the identity vector, and the association graph (Calagrosso: Paragraph [0062], “the collaboration attributes may include an overlap between the actions and affiliations of the user in association with the cloud-based content management platform 115 and actions and affiliations of the respective other user in association with the cloud-based content management platform 115. To identify the overlap, the collaborator predictor 117 may use a bipartite graph that maps the user and the potential collaborators from block 702 to recent actions and affiliations as described with regards to block 702. In the bipartite graph, the user and the potential collaborators are placed on one side and items involved in the actions and affiliations of the user and potential collaborators in association with the cloud-based content management platform 115 on the other side. The bipartite graph also includes edges from the user side to the item side. The items may be a document, a user space, a group space, a calendar event, an email or a message thread. Then, the collaborator predictor 117 may determine a degree of overlap to which a potential collaborator's mapping overlaps with the user's mapping. In some implementations, the collaborator predictor 117 may measure the degree of overlap based on an absolute number of overlapping items, which indicates on how many items the user and the potential collaborator work together, a coverage of the potential collaborator on the user's items which signals how much the potential collaborator is involved in what the user does, a coverage of the user on the potential collaborator's items, which signals how much the user is involved with what the potential collaborator does, a percentage of overlapping items in all items between the user and the potential collaborator which shows another indication of how much the user and the potential collaborator work together. The collaborator predictor 117 can apply weights on the edges by frequency and recency to apply intensity of collaboration by the user and the potential collaborators”); ;
and cause display, in the content suggestion pane, of a personnel content suggestion element including a user identifier of a particular user of the set of other users (Calagrosso: Figure 13 and Paragraph [0090], “a prediction card 1302, 1304, or 1306 based on the predicted collaborators and predicted responses”).
Claim(s) 25 and 37 is/are rejected under 35 U.S.C. 103 as being unpatentable over Calagrosso (US 2019/0179494 A1) and Yu (US 2010/0131523 A1) as applied to claim(s) 21 and 35 above, and further in view of Lim (US 2016/0239547 A1).
Calagrosso-Yu teaches 25/37. The content suggestion system of claim 21/method of claim 35.
Calagrosso-Yu does not explicitly disclose that:
the one or more communication events include an audio recording communication event;
the content suggestion system is further configured to analyze the audio recording communication event to produce text based on an audio recording associated with the audio recording communication event;
and determining the subject of the one or more communication events includes analyzing the text based on the audio recording.
However, Lim teaches these features (Lim: Paragraph [0475], “If the image-capturing unit 155 and the microphone 150 are formed, the controller 170 may perform a control operation according to a user's voice input via the microphone 150 or a user's motion recognized by the image-capturing unit 155. For example, the device 100 may operate in a motion control mode or a voice control mode. If the device 100 operates in the motion control mode, the controller 170 may activate the image-capturing unit 155 and may capture an image of the user, may trace a change in motions of the user, and may perform a control operation corresponding thereto. If the device 100 operates in the voice control mode (i.e., a voice recognition mode), the controller 170 may analyze a user's voice input via the microphone 150, and may perform a control operation according to the analyzed user's voice”).
Calagrosso-Yu and Lim are analogous art in the same field of endeavor as the instant invention as all are drawn to document recommendation systems. 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; that is, it would have been obvious to incorporate Lim’s audio recognition function into the system of Calagrosso-Yu to allow for voice communications to be analyzed in addition to other communications.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Broster (US 642968 B1) describes a system for suggesting documents by means of an associative graph.
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/IMAD HUSSAIN/Primary Examiner, Art Unit 2453
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