DETAILED ACTION
Notice of Pre-AIA or AIA Status
1. The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Remarks
2. Claims 21-40 have been examined and rejected. This is the first Office action on the merits.
Claim Objections
3. Claim 33 is objected to because of the following informalities: Claim 33 recites the limitation “the respective document-creation histories” in [lines 6-7] of the claim. There is insufficient antecedent basis for this limitation in the claim.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
4. The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
5. Claims 21-35 are rejected under 35 U.S.C. 112(a) as failing to comply with the written description requirement. The claims contain subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, at the time the application was filed, had possession of the claimed invention.
5-1. Regarding independent claim 21, nowhere in Applicant’s originally filed disclosure provides support for the limitation “in response to detecting the user selection of the identifier, causing respective template document content of a document template associated with the selected identifier to be copied into a current document displayed to the user” [claim 21, lines 19-21]. Applicant’s specification only discloses displaying the selected template in [paragraph 72]. Nowhere discloses copying the selected template into a current document displayed to the user.
5-2. Independent claim 30 recites a similar limitation as claim 21 and is thus, rejected for similar reasons.
Claim Rejections - 35 USC § 103
6. 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.
5. Claims 21-22, 24-34, 36-37, and 39-40 are rejected under 35 U.S.C. 103 as being unpatentable over Ahlgren et al (U.S. Patent No. 8,082,301) in view of Chakra et al (U.S. Patent No. 10,963,475).
5-1. Regarding claim 21, Ahlgren teaches the claim comprising, in a collaborative document system comprising a plurality of user-generated documents, each user-generated document associated with a workspace of a plurality of workspaces of the collaborative document system: receiving, from a client application operating on a client device, a document creation request associated with an active workspace of the plurality of workspaces of the collaborative document system, by disclosing a system that includes a processor and an interface for collaborators to perform collaborative activities and connect with people, strategies, technology, and resources [column 4, lines 35-49] where a user may select a New Resource option 1318 [figure 13] on an overview screen to create a resource [column 33, lines 4-7]. Resources are collections of elements defined by users that give the users access to information sources [column 4, lines 61-64]. Resources are organized into a plurality of workspaces, each of which provides a managed environment [column 6, lines 6-8]. Resources are created from resource templates as set by the administrator and assigned to specified workspaces [column 32, lines 65-66].
Although Ahlgren discloses responsive to the selection, displaying a plurality of resource templates [Ahlgren, column 33, lines 4-9; figure 17A], Ahlgren does not expressly teach responsive to receiving the document creation request: obtaining, for the active workspace, data from one or more predictive data sources for a plurality of document templates; analyzing, for the plurality of document templates, the obtained data to predict document template usage for the active workspace; computing, based at least in part on the predicted document template usage, a respective relevance weight for one or more document templates of the plurality of document templates associated with the active workspace; causing identifiers of at least a subset of the plurality of document templates to be displayed to a user in accordance with a rank order, the rank order based at least in part on the computed relevance weights for the plurality of document templates. Chakra discloses an analytics engine that scores retrieved templates with a confidence level, and presents to the user a list of top template matches ranked by confidence level [column 5, line 59 to column 6, line 3; column 7, lines 54-57]. Meta-data surrounding a particular task includes a wide variety of information that would assist the analytics engine in selecting which templates to provide to the user, and includes the identity of the user, the role of the user, and the organization/department of the user [column 5, lines 8-31; column 6, lines 30-37]. Weighting factors are used to rank the relevance of templates [column 6, lines 56-59] such that those pieces of meta-data determined to be more relevant are given more weight [column 7, lines 37-40]. This includes usage history [column 7, lines 15-22; column 13 lines 28-30]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to rank the resource templates in a workspace of Ahlgren based on usage history, as taught by Chakra. This would result in reduction in labor and time.
Ahlgren-Chakra teach detecting a user selection of an identifier of the displayed identifiers; and in response to detecting the user selection of the identifier, causing respective template document content of a document template associated with the selected identifier to be copied into a current document displayed to the user, by disclosing that in response to selecting one of the resource templates, a resource template screen is opened [Ahlgren, column 33, lines 18-19; Chakra, column 5, line 59 to column 6, line 3; column 7, lines 54-57].
5-2. Regarding claim 22, Ahlgren-Chakra teach all the limitations of claim 21, wherein analyzing the obtained data to predict the document template usage comprises: analyzing the obtained data using a pattern recognition technique to predict the document template usage for the active workspace, by disclosing determining relevancy of templates based on a pattern of past usage history [Chakra, column 7, lines 15-32; column 13, lines 16-20].
5-3. Regarding claim 24, Ahlgren-Chakra teach all the limitations of claim 21, wherein: computing the respective relevance weight for the one or more document templates is further based at least in part on a project calendar associated with the document creation request, by disclosing determining relevancy of templates based in part on a user’s electronic calendar [Chakra, column 9, line 58 to column 10, line 2].
5-4. Regarding claim 25, Ahlgren-Chakra teach all the limitations of claim 21, further comprising: obtaining data from content of the document template, wherein the respective relevance weight is computed further based at least in part on the data from the content of the document template, by disclosing determining relevancy of templates based on the structure and content of a template [Chakra, column 5, lines 36-41; column 6, line 56 to column 7, line 9].
5-5. Regarding claim 26, Ahlgren-Chakra teach all the limitations of claim 21, wherein: the one or more predictive data sources comprise a calendar, a project timeline, a historical data, or a search query history, by disclosing determining relevancy of templates based on past usage history [Chakra, column 7, lines 15-32; column 13, lines 16-20] and a user’s electronic calendar [Chakra, column 9, line 58 to column 10, line 2].
5-6. Regarding claim 27, Ahlgren-Chakra teach all the limitations of claim 26, wherein: the one or more predictive data sources further comprise a title, a note, a description, or metadata associated with the calendar, the project timeline, the historical data, or the search query history, by disclosing determining relevancy of templates based on past usage history [Chakra, column 5, lines 36-59; column 7, lines 15-32; column 13, lines 16-20] and a user’s electronic calendar [Chakra, column 9, line 58 to column 10, line 2].
5-7. Regarding claim 28, Ahlgren-Chakra teach all the limitations of claim 21, wherein: the one or more predictive data sources comprise a calendar associated with an individual, an organization, the active workspace, or a project of the active workspace, by disclosing determining relevancy of templates based in part on a user’s electronic calendar [Chakra, column 9, line 58 to column 10, line 2].
5-8. Regarding claim 29, Ahlgren-Chakra teach all the limitations of claim 21, wherein: the one or more predictive data sources comprise a project milestone, a production target, a due date, or a phase completion deadline, by disclosing determining relevancy of templates based on past usage history, which includes time of year that reports have been created [Chakra, column 5, lines 36-59; column 7, lines 15-32; column 13, lines 16-20] and a user’s electronic calendar indicating a date of an upcoming meeting [Chakra, column 9, line 58 to column 10, line 2].
5-9. Regarding claim 30, Ahlgren teaches the claim comprising, in a collaborative document system comprising a plurality of user-generated documents associated with workspaces of the collaborative document system:… detecting, from a user, a first document creation request originating from the first workspace, by disclosing a system that includes a processor and an interface for collaborators to perform collaborative activities and connect with people, strategies, technology, and resources [column 4, lines 35-49] where a user may select a New Resource option 1318 [figure 13] on an overview screen to create a resource [column 33, lines 4-7]. Resources are collections of elements defined by users that give the users access to information sources [column 4, lines 61-64]. Resources are organized into a plurality of workspaces, each of which provides a managed environment [column 6, lines 6-8]. Resources are created from resource templates as set by the administrator and assigned to specified workspaces [column 32, lines 65-66].
Although Ahlgren discloses responsive to the selection, displaying a plurality of resource templates [Ahlgren, column 33, lines 4-9; figure 17A], Ahlgren does not expressly teach obtaining, for a first workspace of the collaborative document system, data from a first predictive data source; obtaining, for a second workspace of the collaborative document system, data from a second predictive data source; analyzing, for a plurality of document templates, the obtained data from the first predictive data source and the obtained data from the second predictive data source to predict document template usage for the first workspace; computing, based at least in part on the predicted document template usage, a respective relevance weight for one or more document templates of the plurality of document templates associated with the first workspace;… responsive to detecting the first document creation request, causing identifiers of at least a subset of the plurality of document templates to be displayed to the user in accordance with a first rank order, the first rank order based at least in part on the respective relevance weights for the one or more document templates. Chakra discloses an analytics engine that scores retrieved templates with a confidence level, and presents to the user a list of top template matches ranked by confidence level [column 5, line 59 to column 6, line 3; column 7, lines 54-57]. Meta-data surrounding a particular task includes a wide variety of information that would assist the analytics engine in selecting which templates to provide to the user, and includes the identity of the user, the role of the user, and the organization/department of the user [column 5, lines 8-31; column 6, lines 30-37]. Weighting factors are used to rank the relevance of templates [column 6, lines 56-59] such that those pieces of meta-data determined to be more relevant are given more weight [column 7, lines 37-40]. This includes usage history of the requesting user [column 5, lines 36-38; column 7, lines 15-28] as well as other users [column 7, lines 28-33; column 11, lines 3-7; column 13 lines 16-20, 28-30]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to rank the resource templates in a workspace of Ahlgren based on usage history of the requesting user and other users, as taught by Chakra. This would result in reduction in labor and time. Since a template may be assigned to specified workspaces [Ahlgren, column 32, lines 65-66] of which different users are assigned [Ahlgren, column 6, lines 6-14] such that each user has access to components of their respectively assigned workspace including the ability to access resource templates belonging to their assigned workspace [Ahlgren, column 33, lines 4-7], and determining which resource templates to display for a workspace [Ahlgren, column 33, lines 4-9; figure 17A] is based on ranking the templates according to usage history of the requesting user [Chakra, column 5, lines 36-38; column 7, lines 15-28] as well as other users [Chakra, column 7, lines 28-33; column 11, lines 3-7; column 13 lines 16-20, 28-30], data sources from a first and a second workspace may be obtained and analyzed to predict a resource template usage for the first workspace and display a list of top template matches ranked by confidence level [Chakra, column 5, line 59 to column 6, line 3; column 7, lines 54-57].
Ahlgren-Chakra teach detecting a user selection of an identifier of the displayed identifiers; and in response to detecting the user selection of the identifier, causing respective template document content of a document template associated with the selected identifier to be copied into a current document displayed to the user, by disclosing that in response to selecting one of the resource templates, a resource template screen is opened [Ahlgren, column 33, lines 18-19; Chakra, column 5, line 59 to column 6, line 3; column 7, lines 54-57].
5-10. Regarding claim 31, Ahlgren-Chakra teach all the limitations of claim 30, wherein the collaborative document system comprises a plurality of workspaces, each workspace associated with a different set of user-generated documents organized in a different organizational hierarchy, by disclosing that domains, initiatives, and resources are organized into a plurality of workspaces, each of which provides a managed environment [Ahlgren, column 6, lines 6-8]. The system gives each collaborator/user access to one or more workspaces where a user may have different roles in different workspaces [Ahlgren, column 6, lines 8-10]. Resources may be organized into resource hierarchies, and the resource hierarchies belong to domains, which themselves may be hierarchically organized [Ahlgren, column 5, lines 15-18].
5-11. Regarding claim 32, Ahlgren-Chakra teach all the limitations of claim 30, further comprising: receiving, from the user, a definition for a first user-generated document template for use in the first workspace, by disclosing that new resource templates may be created [Ahlgren, column 27, lines 32-46].
Ahlgren-Chakra teach including the first user-generated document template in the plurality of document templates and not in a second plurality of document templates associated with the second workspace, by disclosing that resource templates can be associated or disassociated with a workspace [Ahlgren, column 28, lines 16-19].
5-12. Regarding claim 33, Ahlgren-Chakra teach all the limitations of claim 30, wherein: detecting, from the user, a second document creation request originating from the second workspace, by disclosing that the user may navigate to another workspace [Ahlgren, column 30, lines 16-18] and select a New Resource option 1318 [figure 13] on an overview screen to create a resource [Ahlgren, column 33, lines 4-7].
Ahlgren-Chakra teach responsive to detecting the second document creation request, causing identifiers of at least a subset of a second plurality of document templates to be displayed to the user in accordance with a second rank order, the second rank order based at least in part on the respective document-creation histories for the second plurality of document templates, by disclosing responsive to the selection, displaying a plurality of resource templates [Ahlgren, column 33, lines 4-9; figure 17A] that are ranked according to usage history of the requesting user [Chakra, column 5, lines 36-38; column 7, lines 15-28] as well as other users [Chakra, column 7, lines 28-33; column 11, lines 3-7; column 13 lines 16-20, 28-30]
5-13. Regarding claim 34, Ahlgren-Chakra teach all the limitations of claim 30, wherein analyzing the obtained data to predict the document template usage comprises: analyzing the obtained data using a pattern recognition technique to predict the document template usage for the first workspace, by disclosing determining relevancy of templates based on a pattern of past usage history [Chakra, column 7, lines 15-32; column 13, lines 16-20].
5-14. Regarding claim 36, Ahlgren teaches the claim comprising, at a client device of a collaborative document system, the collaborative document system comprising a plurality of user-generated documents associated with workspaces of the collaborative document system: displaying a graphical user interface on a display, by disclosing a system that includes a processor and an interface for collaborators to perform collaborative activities and connect with people, strategies, technology, and resources [column 4, lines 35-49]. Resources are collections of elements defined by users that give the users access to information sources [column 4, lines 61-64]. Resources are organized into a plurality of workspaces, each of which provides a managed environment [column 6, lines 6-8]. Resources are created from resource templates as set by the administrator and assigned to specified workspaces [column 32, lines 65-66]. An overview screen is provided that allows a user to interact with the system and navigate through the system [column 30, lines 12-16; figure 13].
Ahlgren teaches displaying, in a first region of the graphical user interface, a plurality of workspace identifiers, each respective workspace identifier corresponding to a respective workspace that is associated with a respective set of user-generated documents, by disclosing displaying a list of workspaces the user has been assigned [column 6, lines 8-10; column 27, lines 62-63; column 30, lines 16-18; figure 13]. The components of a workspace include resources [column 6, lines 11-14] which are collections of elements defined by users that give the users access to information sources [column 4, lines 61-64].
Ahlgren teaches receiving a selection of a workspace identifier from the plurality of workspace identifiers, the workspace identifier associated with a workspace within the collaborative document system; in response to receiving the selection of the workspace identifier, displaying, in the first region, a plurality of document links, each respective document link corresponding to a respective user-generated document associated with the workspace, by disclosing that the user may navigate to a workspace [column 30, lines 16-18] such that the overview screen displays a navigator screen 1302 for the workspace [column 30, lines 18-19; figure 13] that displays created resources [column 31, lines 43-45, 53-57; column 31, line 66 to column 32, line 2]. Selecting a resource displays the resource’s objects in a workspace details view [column 30, lines 22-24; column 31, lines 45-47; column 32, lines 2-4; figure 15C].
Ahlgren teaches detecting, from a user, a document creation request originating from the workspace, by disclosing that the user may select a New Resource option 1318 [figure 13] on the overview screen to create a resource [column 33, lines 4-7].
Although Ahlgren discloses responsive to the selection, displaying a plurality of resource templates [Ahlgren, column 33, lines 4-9; figure 17A], Ahlgren does not expressly teach responsive to detecting the document creation request: obtaining, for the workspace, data from one or more predictive data sources for a plurality of document templates associated with the workspace; determining, for the plurality of document templates and the workspace, a predicted document template usage based at least in part on the obtained data; determining, based at least in part on the predicted document template usage, a respective relevance weight for one or more document templates of the plurality of document templates associated with the workspace; displaying, in a second region of the graphical user interface, identifiers of at least a subset of the plurality of document templates in accordance with a rank order, the rank order based at least in part on the determined relevance weights for the plurality of document templates. Chakra discloses an analytics engine that scores retrieved templates with a confidence level, and presents to the user a list of top template matches ranked by confidence level [column 5, line 59 to column 6, line 3; column 7, lines 54-57]. Meta-data surrounding a particular task includes a wide variety of information that would assist the analytics engine in selecting which templates to provide to the user, and includes the identity of the user, the role of the user, and the organization/department of the user [column 5, lines 8-31; column 6, lines 30-37]. Weighting factors are used to rank the relevance of templates [column 6, lines 56-59] such that those pieces of meta-data determined to be more relevant are given more weight [column 7, lines 37-40]. This includes usage history [column 7, lines 15-22; column 13 lines 28-30]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to rank the resource templates in a workspace of Ahlgren based on usage history, as taught by Chakra. This would result in reduction in labor and time.
Ahlgren-Chakra teach detecting a user selection of an identifier of the displayed identifiers; and in response to detecting the user selection of the identifier, causing a document template associated with the selected identifier to be displayed in a third region of the graphical user interface, by disclosing that in response to selecting one of the resource templates, a resource template screen is opened [Ahlgren, column 33, lines 18-19; Chakra, column 5, line 59 to column 6, line 3; column 7, lines 54-57].
5-15. Regarding claim 37, Ahlgren-Chakra teach all the limitations of claim 36, wherein determining the predicted document template usage comprises: analyzing the obtained data using a pattern recognition technique to predict the document template usage for the workspace, by disclosing determining relevancy of templates based on a pattern of past usage history [Chakra, column 7, lines 15-32; column 13, lines 16-20].
5-16. Regarding claim 39, Ahlgren-Chakra teach all the limitations of claim 36, wherein: computing the respective relevance weight for the one or more document templates is further based at least in part on a time of year associated with the document creation request, by disclosing that meta-data to assist the analytics engine includes information gathered when the request is made, such as the date, the day of the week, and time of day [Chakra, column 5, lines 8-20].
5-17. Regarding claim 40, Ahlgren-Chakra teach all the limitations of claim 36, further comprising: obtaining data from content of the document template, wherein the respective relevance weight is computed further based at least in part on the data from the content of the document template, by disclosing determining relevancy of templates based on the structure and content of a template [Chakra, column 5, lines 36-41; column 6, line 56 to column 7, line 9].
6. Claims 23, 35, and 38 are rejected under 35 U.S.C. 103 as being unpatentable over Ahlgren et al (U.S. Patent No. 8,082,301), in view of Chakra et al (U.S. Patent No. 10,963,475), and further in view of Kumar et al (U.S. Patent No. 10,963,636).
6-1. Regarding claim 23, Ahlgren-Chakra teach all the limitations of claim 22. Ahlgren-Chakra do not expressly teach wherein the pattern recognition technique comprises: a classification algorithm, a clustering algorithm, an ensemble learning algorithm, a regression algorithm, or a probabilistic classifier. Kumar discloses using a machine learning model trained using historical data with known outcomes to select a template for recommendation to the user [column 3, lines 3-6]. The historical data can vary depending on user preferences, and can be based on commodity, region, user, process owner, item to be procured, last or previously used templates, etc. [column 3, lines 6-10]. The machine learning model can take a variety of forms including, without limitation, an extreme gradient boosting algorithm, a neural network model (including deep learning), a logistic regression model, a support vector machine, a random forest, a nearest neighbor model, a Bayesian model, a genetic algorithm, and/or the like [column 3, lines 15-20; column 4, lines 63-67]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the list of top template matches of Ahlgren-Chakra using a machine learning model, as taught by Kumar. This would increase efficiency by providing more suitable recommendations.
6-2. Regarding claim 35, Ahlgren-Chakra teach all the limitations of claim 34. Ahlgren-Chakra do not expressly teach wherein the pattern recognition technique comprises: a classification algorithm, a clustering algorithm, an ensemble learning algorithm, a regression algorithm, or a probabilistic classifier. Kumar discloses using a machine learning model trained using historical data with known outcomes to select a template for recommendation to the user [column 3, lines 3-6]. The historical data can vary depending on user preferences, and can be based on commodity, region, user, process owner, item to be procured, last or previously used templates, etc. [column 3, lines 6-10]. The machine learning model can take a variety of forms including, without limitation, an extreme gradient boosting algorithm, a neural network model (including deep learning), a logistic regression model, a support vector machine, a random forest, a nearest neighbor model, a Bayesian model, a genetic algorithm, and/or the like [column 3, lines 15-20; column 4, lines 63-67]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the list of top template matches of Ahlgren-Chakra using a machine learning model, as taught by Kumar. This would increase efficiency by providing more suitable recommendations.
6-3. Regarding claim 38, Ahlgren-Chakra teach all the limitations of claim 37. Ahlgren-Chakra do not expressly teach wherein the pattern recognition technique comprises: a classification algorithm, a clustering algorithm, an ensemble learning algorithm, a regression algorithm, or a probabilistic classifier. Kumar discloses using a machine learning model trained using historical data with known outcomes to select a template for recommendation to the user [column 3, lines 3-6]. The historical data can vary depending on user preferences, and can be based on commodity, region, user, process owner, item to be procured, last or previously used templates, etc. [column 3, lines 6-10]. The machine learning model can take a variety of forms including, without limitation, an extreme gradient boosting algorithm, a neural network model (including deep learning), a logistic regression model, a support vector machine, a random forest, a nearest neighbor model, a Bayesian model, a genetic algorithm, and/or the like [column 3, lines 15-20; column 4, lines 63-67]. It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to determine the list of top template matches of Ahlgren-Chakra using a machine learning model, as taught by Kumar. This would increase efficiency by providing more suitable recommendations.
Conclusion
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/ALVIN H TAN/Primary Examiner, Art Unit 2118