Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
DETAILED ACTION
Status of the Claims
This Non-Final action is in reply to the application filed 7/14/2023.
Claims 1-18 are pending.
Claim Objections
Claims 1, 7 and 13 recite in part, “create a digital create a digital user interface accessible by a plurality of end users associated with an organization; and creating a digital create a digital user interface”, there appears to be a duplication of words. Appropriate correction is requested.
Claim Rejections - 35 USC § 112
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.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
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 of carrying out his invention.
Claims 3 and 9 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains 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, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
Claims 3 and 9 recite in part, “wherein the alert is displayed anonymously in association with the plurality of end users”. Examiner was not able to find support in the disclosure that describes the claimed limitation so that one skilled in the art can recognize what is claimed. The appearance of mere indistinct words in a specification or a claim, even an original claim, does not necessarily satisfy that requirement. The most relevant discussion in applicant’s specification generically occurs in ¶48: “the system 500 may be configured to automatically alert the end user of the attribute whose status reached a threshold by sending a notification. In some embodiments, the notification may be displayed automatically in the UI as an overlay or other visual message to gran the end user's attention”; ¶56: “the processor may automatically send an alert to an administrative end user (or other end user with supervisorial status), and example of which can be seen in Figure 11. An alert may sometimes be provided as an overlay or other prioritized item in any one of the UIs in the app. Multiple flares may be aggregated into an alert so that the administrative end user can use the alert to gauge the current state of health of a project”. The claim defines the invention in functional language specifying a desired result but the disclosure fails to sufficiently identify how the function is performed or the result is achieved. Applicant’s failure to disclose any meaningful processes with the above recited subject matter raises questions as to whether applicant truly had possession of these features at the time of filing. Hence, applicant is required to remove the claim limitation or specifically point out in the specification where support for the limitation is disclosed.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION. The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 1-18 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 7 and 13 recite the limitation, "receive, by the plurality of end users, input associated with a progression of work within one or more of the sub-categories of topics”. There is insufficient antecedent basis for this limitation in the claim(s). Appropriate correction is requested.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-18 are rejected under 35 U.S.C. § 101 because the claimed invention is directed to an abstract idea without significantly more. Claims 1-18 are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices) and a computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media. However, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media is directed to non-statutory subject matter. However, in an effort to analyze all of the claim limitations in accordance with the two-step framework described in Alice/Mayo and the guidance on application of 35USC 101, Examiner interprets the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, of independent claim 7 as, “computer program product comprising: one or more “non-transitory” computer readable storage media, and program instructions collectively stored on the one or more “non-transitory” computer readable storage media. Thus, each of the claims fall within one of the four statutory categories.
Step 2A-Prong 1: Representative independent claim 1 recites in part, “create a digital create a digital user interface accessible by a plurality of end users associated with an organization; receive, from an administrative user of the organization, user defined parameters of a collaborative project outcome, wherein the parameters define sub-categories of attributes associated with a defined success metric of the collaborative project outcome; receive, by the plurality of end users, input associated with a progression of work within one or more of the sub-categories of topics; continuously monitor the received input from the plurality of end users; process the received input from the plurality of end users, using a machine learning modelling module, wherein an operation of the machine learning module includes: building a prediction model correlating a relationship of the attributes; and forecasting a direction of the attributes based on the prediction model and a current status of progression of work in each of the sub-categories; and wherein the program instructions further cause the system to display on the digital user interface: the current status of progression of work in each of the sub-categories, and the forecasted direction of the attributes”
The underlined limitations above demonstrate independent claim 1 is directed toward the abstract idea for receiving and monitoring progress of user defined parameters/attributes of a collaborative project outcome; building a prediction model, forecasting a direction of the attributes, displaying the current status and forecasted direction of the attributes in a computing environment. Applicant’s specification emphasizes a management framework for delivering products and services in an enterprise, whereby the method that embodies the management framework assigns the tactics (e.g., planning and implementation) to the employees and the strategies (e.g., guidance and enablement) to the employers (¶5). The management framework may include creating a virtual collaboration workspace for a plurality of participants and/or employees/engineers/workers/management/leadership and the like to access the workspace based on their role (tactical participant or strategical participant) and linked to a specific collaboration environment (e.g. a meeting or project); and a vision board/grid/graphical element providing a tabular view of data columns, rows, and task boxes for inputting tasks for the respective tactical participants. The vision board is used to identify the people, processes, and tools necessary to deliver a desired goal (¶27, ¶28). Applicant’s disclosure teaches that an artificial intelligence or machine learning engine may be used to model and forecast the direction of work progression to identify counterproductive work progression (¶45). The specification further discloses that the collaborative production management system, method or process, or computer program product can be implemented by computer program instructions provided to the processor of a general-purpose computer (¶63).
Representative Claim 1 is considered an abstract idea because the steps for, “create a digital create a digital user interface accessible by a plurality of end users associated with an organization; receive, from an administrative user of the organization, user defined parameters of a collaborative project outcome, wherein the parameters define sub-categories of attributes associated with a defined success metric of the collaborative project outcome; receive, by the plurality of end users, input associated with a progression of work within one or more of the sub-categories of topics; continuously monitor the received input from the plurality of end users; process the received input from the plurality of end users, using a machine learning modelling module, wherein an operation of the machine learning module includes: building a prediction model correlating a relationship of the attributes; and forecasting a direction of the attributes based on the prediction model and a current status of progression of work in each of the sub-categories; and wherein the program instructions further cause the system to display on the digital user interface: the current status of progression of work in each of the sub-categories, and the forecasted direction of the attributes”, pertains to certain methods of organizing human activity groupings of abstract ideas (i) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions)) since the steps are directed to receiving from an administrative user of an organization user defined parameters of a collaborative project outcome, wherein the parameters define sub-categories of attributes; receiving and monitoring input associated with a progression of work from a plurality of end users; building a prediction model, correlating the relationship of the attributes, forecasting a direction of the attributes based on the prediction model and current status, and displaying the current status of work in each of the sub-categories, and the forecasted direction of the attributes. Such data input and data gathering steps pertains to (i) managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions. Hence, the claim recites an abstract idea--see MPEP 2106.04(II). Independent claims 7 and 13 recite essentially the same abstract idea as independent claim 1, therefore, they are also abstract based on the same rationale as independent claim 1.
Step 2A-Prong 2: This judicial exception is not integrated into a practical application because the additional elements “processor, “memory” “program instructions” “system”, “digital user interface”, “machine learning modelling module”; “computer program product”, “one or more computer readable storage media”, “program instructions”, “prediction model” [claim 7] merely provide an abstract-idea based solution using data gathering and analysis and merely provide instructions for organizing human activity, and implementing the abstract idea recited above utilizing the “processor, “memory” “program instructions” “system”, “digital user interface”, “machine learning modelling module”; “computer program product”, “one or more computer readable storage media”, “program instructions”, “prediction model” [claim 7] as tools to perform the abstract idea, and generally links the abstract idea to a particular technological environment. See MPEP 2106.05 (f-h). Further, the additional elements do not impose any meaningful limits on practicing the abstract idea—see MPEP 2106.05(g). Independent claim 1 fails to operate the recited “processor, “memory”, “program instructions” , “system”, “digital user interface”, “machine learning modelling module”; “computer program product”, “one or more computer readable storage media”, “program instructions”, “prediction model” [claim 7]”, (which are merely standard computer technology and hardware/software components- see applicant’s disclosure ¶57: “Embodiments may include a machine learning module that builds a prediction model based on the current system attributes. The prediction model may be used by the processor to forecast the direction the attributes are headed toward. For example, if a project is showing turnover in participants and/or a new competing company has just opened up, the prediction model may forecast a shortage of labor (the attribute) needed to progress. The system may display the forecast on a digital display so that end users can identify the counterproductive element and prevent the forecast; ¶58: “the computing device may be a client device that is configured for end user interaction (for example, to receive user input or view displayed data on the cultural health of the organization or a specific project). In the role of a user device, the computing device 1200 is generally not a server but may instead be desktop computers, tablet or laptop computers, all-in-one computer stations, a mobile computing device (for example, a smart phone, smart wearable devices (glasses, jewelry, watches, ear wear, etc.), smart televisions, smart hubs, robots, or programmable electronics. As will be understood, the end user device may generally provide frontend aspects of the system. In some embodiments however, the frontend computing device may perform one or more of the backend steps where possible. In another role, the computing device 1200 may be a server(s) dedicated to providing artificial intelligence or machine learning processing of data for modelling and forecasting”; ¶60: “The computing device 1200 may be described in the general context of computer system executable instructions, such as the program modules which represent a software embodiment of the system and processes described generally above. The program modules generally carry out the functions and/or methodologies of embodiments as described above. The computing device 1200 may typically include a variety of computer system readable media. Such media could be chosen from any available media that is accessible by the computing device 1200, including non-transitory, volatile and non-volatile media, removable and non-removable media for use by or in connection with an instruction execution system, apparatus, or device”; ¶63: “aspects of the disclosed invention may take the form of an entirely hardware embodiment, an entirely software embodiment (including firmware, resident software, micro-code, etc.) or an embodiment combining software and hardware aspects that may all generally be referred to herein as a "module", "circuit", or "system." Furthermore, aspects of the disclosed invention may take the form of a computer program product embodied in one or more computer readable media having computer readable program code embodied thereon. In some embodiments, the output of the computer program product provides an electronic user interface on the display 1250 which may be controlled via direct contact with the display 1250 or via the I/O interfaces 1260 (which may be for example, interface devices such as keyboards, touchpads, a mouse, a stylus, or the like)”; ¶64: “computer program instructions may be provided to the processor 1210 of a general-purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks in the figures”) in any exceptional manner, and there is no evidence in the disclosure to suggest achieving an actual improvement in the computer functionality itself, or improvement in any specific computer technology other than utilizing ordinary computational tools to automate and perform the abstract idea for receiving and monitoring progress of user defined parameters/attributes of a collaborative project outcome; building a prediction model, forecasting a direction of the attributes, displaying the current status and forecasted direction of the attributes in a computing environment—see MPEP 2106.05(a). The use of applicant’s computing components are well-known, routine, and conventional activity. The court describes the use of a computer to create electronic records, track information/data and issue simultaneous instructions as purely conventional computer functions and notes that nearly every computer has a data processing system with a communications controller and a data storage unit. Their collective functions merely provide conventional computer implementation. Accordingly, applicant has not shown an improvement or practical application under the guidance of MPEP section 2106.04(d) or 2106.05(a).
Dependent claims 2-6, 8-12 and 14-18 fail to cure the deficiencies of the above noted independent claim from which they depend and are therefore rejected under the same grounds. The dependent claims further recite the abstract idea without imposing any meaningful limits on practicing the abstract idea. Dependent claims 2-6, 8-12 and 14-18 recite additional data gathering and processing steps. For example dependent claims 2, 5, 8, 11, 14 recite in part, “wherein the program instructions further cause the system to: receive by the processor”; claims 3, 9 and 15 recite in part, “wherein the alert is displayed anonymously”; claims 4, 10 and 16 recite in part, “wherein the sentiment is expressive of”; claims 6, 12 and 18 recite in part, “wherein the sub-categories of attributes include”; which are still directed toward the abstract idea identified previously and are no more than mere instructions to apply the exception using a computer or with computing components.
The additional elements in the dependent claims “a signal”, “alert”, amounts to no more than applying the judicial exception using generic computing components, and linking the use of the judicial exception to a computing environment. In this case, the “signal”, “alert” are generically used to further process data and fails to integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea (see applicant’s disclosure, ¶47: “Signal types for a capability may be user added to define a capability. A Define Component library includes files that define tools or other items used to progress toward the outcome. Signal types associated with the components used within the system may be user defined in this library”; ¶48: “a computer processor may continuously monitor signals provided by end users contributing toward the project …when an attribute for a project triggers a signal that reaches or crosses a threshold value, the dashboard may display an indicator showing the change in status (whether positive or negative as an indicator …the system 500 may be configured to automatically alert the end user of the attribute whose status reached a threshold by sending a notification. In some embodiments, the notification may be displayed automatically in the UI as an overlay or other visual message to gran the end user's attention”). Hence is nonetheless directed towards fundamentally the same abstract idea as their respective independent claim since they fail to impose any meaningful limits on practicing the abstract idea. Therefore, the abstract idea fails to integrate into any practical application. Thus, under Step 2A-Prong Two the claims are directed to an abstract idea.
Step 2B: The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because as discussed above, with respect to integration of the abstract idea into a practical application, the additional elements ““processor, “memory”, “program instructions”, “system”, “digital user interface”, “machine learning modelling module”; “computer program product”, “one or more computer readable storage media”, “program instructions”, “prediction model” [claim 7]”, amount to no more than mere instructions to apply the exception using generic computer components which does not integrate a judicial exception into a practical application nor provide an inventive concept (significantly more than the abstract idea).
Further, the additional elements including applicant’s “a signal”, “alert”, also amounts to no more than applying the judicial exception using generic computing components, and linking the use of the judicial exception to a computing environment. In this case, the “signal”, “alert” are generically used further process information via common computing components Applicant’s “signal”, “alert” are merely used to communicate/transmit data/information -and fails to integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea.
Accordingly, even when considered as a whole, the claims do not transform the abstract idea into a patent-eligible invention since the claim limitations do not amount to a practical application or significantly more than an abstract idea for for receiving and monitoring progress of user defined parameters/attributes of a collaborative project outcome; building a prediction model, forecasting a direction of the attributes, displaying the current status and forecasted direction of the attributes in a computing environment. Hence, claims 1-18 are directed to non-statutory subject matter and are rejected as ineligible subject matter under 35 USC 101. See 2019 PEG and MPEP 2106.
Claims 7-12 recite in part, “A computer program product for providing collaborative production management in an organization, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising” and are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The aforementioned claims are directed toward providing information in relation to an electronic communication device via a data signal. The USPTO is obliged to give claims their broadest reasonable interpretation consistent with the specification during proceedings before the USPTO. See In re Zletz, 893 F.2d 319 (Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). The broadest reasonable interpretation of a claim drawn to a computer readable medium (also called machine readable medium and other such variations) typically covers forms of non-transitory tangible media and transitory propagating signals per se in view of the ordinary and customary meaning of computer readable media, particularly when the specification is silent. Se MPEP 2111.01. When the broadest reasonable interpretation of a claim covers a signal per se, the claim must be rejected under 35 USC 101 as covering non-statutory subject matter. See In re Nuijten, 500 F.3d 1346, 1356-57 (Fed. Ccir. 2007) (transitory embodiments are not directed to statutory subject matter) and Interim Examination Instructions for Evaluating Subject Matter Eligibility under 35 USC 101, Aug 24, 2009; p2. The USPTO recognizes that applicants may have claims directed to computer readable media that cover signals per se, which the USPTO must reject under 35 USC 101 as covering both non-statutory subject matter and statutory subject matter. A claim drawn to such a computer readable medium that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 USC 101 by adding the limitation “non-transitory” to the claim. Applicant' s disclosure recites at ¶60: “The computing device 1200 may typically include a variety of computer system readable media. Such media could be chosen from any available media that is accessible by the computing device 1200, including non-transitory, volatile and non-volatile media, removable and non-removable media for use by or in connection with an instruction execution system, apparatus, or device”, therefore claims 7-12 as recited can be interpreted to be embodied on abstract mediums such as carrier waves and signals, and therefore not eligible for patent protection. The term "computer-readable storage medium" may also include solid-state memories, optical and magnetic disks, and carrier wave signals. Accordingly, these claims 7-12 are not eligible for patent protection.
Claim Rejections - 35 USC § 102
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 the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claims 1-18 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Mann et al., US Patent Application Publication No US 2021/0342785 A1.
With respect to claims 1, 7 and 13,
Mann discloses,
a processor; and a memory coupled to the processor, the memory including program instructions stored thereon that, upon execution by the processor, cause the system to: (¶295: “the machine may be implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described in this disclosure may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU”; ¶296: “The memory may include one or more separate storage devices collocated or disbursed, capable of storing data structures, instructions, or any other data. The memory may further include a memory portion containing instructions for the processor to execute”)
A computer program product for providing collaborative production management in an organization, the computer program product comprising: one or more computer readable storage media, and program instructions collectively stored on the one or more computer readable storage media, the program instructions comprising (¶295: “a non-transitory computer readable medium containing instructions that when executed by at least one processor, cause the at least one processor to perform a method. Non-transitory computer readable mediums may be any medium capable of storing data in any memory in a way that may be read by any computing device with a processor to carry out methods or any other instructions stored in the memory… the software may preferably be implemented as an application program tangibly embodied on a program storage unit or computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture”; ¶297: “The instructions executed by at least one processor may, for example, be pre-loaded into a memory integrated with or embedded into the controller or may be stored in a separate memory”)
create a digital create a digital user interface accessible by a plurality of end users associated with an organization (¶18: “Systems, methods, devices, and non-transitory computer readable media may include a workflow management system for triggering table entries characterizing workflow-related communications occurring between workflow participants including at least one processor that is configured to present a table via a display, the table containing rows and columns defining cells, the rows and cells being configured to manage respective roles of the workflow participants, present on the display at least one active link for enabling workflow participants to join in a video or an audio communication, log in memory, characteristics of the communication including identities of the workflow participants who joined in the communication, and generate an object associated with the table, the object containing the characteristics of the communication logged in memory”; ¶36: “such disclosed embodiments may integrate a unified filing engine within a workflow management system that may permit files to be associated with entries in the workflow management system. Some disclosed embodiments may involve systems, methods, and computer readable media relating to a workflow management system having an integrated unified filing engine”; ¶47: “FIG. 2 is a block diagram of an exemplary computing architecture for collaborative work systems, consistent with embodiments of the present disclosure”; ¶291: “the disclosed embodiments are not limited to any specific structure, but rather may be practiced in conjunction with any desired organizational arrangement. In addition, a tablature may include any suitable information. When used in conjunction with a workflow management application, the tablature may include any information associated with one or more tasks, such as one or more status values, projects, countries, persons, teams, progresses, a combination thereof, or any other information related to a task”0)
receive, from an administrative user of the organization, user defined parameters of a collaborative project outcome, wherein the parameters define sub-categories of attributes associated with a defined success metric of the collaborative project outcome (¶16: “Systems, methods, devices, and non-transitory computer readable media may include a system for graphically aggregating data from a plurality of distinct tables, and enabling dissociation of underlying aggregated data from the associated distinct tables including at least one processor that is configured to maintain the plurality of distinct tables, wherein each distinct table contains a plurality of items, with each item being made up of a plurality of cells categorized by category indicators, and wherein the plurality of distinct tables contain a common category indicator. The at least one processor may be further configured to generate a graphical representation of a plurality of variables within the plurality of cells associated with the common category indicator, the graphical representation including a plurality of sub-portions, each sub-portion representing a differing variable of the common category indicator, and receive a selection of a sub-portion of the graphical representation”; ¶293: “permissions may be set to limit board access to the board's “owner” while in other embodiments a user's board may be accessed by other users …When one user makes a change in a board, that change may be updated to the board stored in a memory or repository and may be pushed to the other user devices that access that same board. These changes may be made to cells, items, columns, boards, dashboard views, logical rules, or any other data associated with the boards”; Fig 3, ¶347: “if a user changed one column heading to “Project,” then the system may determine that the project may have a deadline and the system may recommend changing the “Date” to be a “Due Date” column rather than just simply “Date””; Fig 4, ¶349: “the user may customize column 316 of FIG. 3 to have a customized column heading of “Deadline” 416 rather than “Date,” as shown in FIG. 4. Then, the system may recognize the customized name for that heading and perform a lookup to identify a data type associated with the customized name (the system may recognize a “People” data type)”; ¶373: “Aspects of this disclosure may involve at least one pre-population rule drawing from at least one preexisting table at least one of a capacity, a count, an identity, a budget, variable numerical data, a timeline value, a status value, and a progress value… A progress value may include any numerical or graphical value (such as the amount of development in a project) that indicates an extent of completion associated with an item”; Fig 7, ¶375: “a request may be received from a user clicking an “add item” icon 610 of FIG. 6 or FIG. 8 to generate the new item and fill cells associated with that item (person, status, and date) with values that were previously set using interface 700 of FIG. 7”; Fig 16, ¶411: “common view 1600 displaying both the first row 1602 of the main table, which has been expanded to include the first sub-table 1606. In addition, the common view 1600 may also simultaneously display the other rows 1604 of the main table”; ¶412: “the summary information may be presented in a form of a number, range of numbers, percentage, chart, or any other form of data representation. A graphical summarization may include a bar chart, a pie chart, or any other chart or diagram divided proportionally based on corresponding percentages. For example, a column of a first sub-table may contain three statuses marked as “done” and two statuses marked as “in progress.” A graphical representation displaying the summary information of the first sub-table may be a chart that may be split in two parts to indicate that 40% of work is “in progress” (two out of five statuses) and 60% of work is “done” (three out of five statuses). The graphical representation may be sized or shaped in any other manner, such as by volume, by a count, by size of individual icons representing individuals, or any other representation to reflect a count, a priority, or any other indication in a table”; Fig 28, ¶447: “Each item may be made up of a plurality of cells categorized by category indicators and may include each row being organized by category indicators. Category indicators may include values or representations employed for purposes of organization or grouping. For example, a category indicator may include a column heading (e.g., Status, Person, Description, Date, Timeline, and so on). Information associated with a common category indicator may be attributed to similar characteristics”; ¶577: “input options for a definable condition may be based on authorization or permission to access data within a linked table. For example, a table may include restricted, confidential, or privileged information stored in cells that may only be accessed by entities such as an administrator, a project manager, an investor, a particular team or entity, or other authorized individuals or entities”; Fig 70, ¶711: “status filter 6904 allows filtering of the view 6900 to only show rows with specific status 6912 or exclude certain statuses. For example, if a user troubleshooting the automation only would like to check on failed activities, the user may utilize view 6900 to view by a “failed” status and reconfigure those particular automations. As depicted, “Success” status 6922 corresponds to a configured automation that did not encounter any issues and performed as expected; “Pending” status 6918 corresponds to an automation currently processing that may be monitored by the user in a real-time; “Failed” Status 6920 corresponds to an automation that did not perform as expected and may display a reason for failure as depicted, and a button (or any other interactive element) 6924 to assist in resolving the issue. The “Failed” status may be an example of an indication of an irregularity”; ¶835: “the system may determine whether the user is efficiently utilizing the system to achieve the user's goals and recommend more efficient tools if the system determine that the user is not using tools that may improve their workflows. Monitoring software usage may include an analyzing the historical usage of tools in the system to determine tools that have been historically used, determine whether unused tools may improve data processing efficiency, and storing such determination to present recommendations to a user. Advantageously, disclosed embodiments may address this issue by enabling software applications to self-monitor tool usage to identify and present tools that may increase efficiency and optimize performance of the application's intended use”)
Applicant’s disclosure generically teaches at ¶46: “Figure 5 shows a system 500 that includes a plurality of modules whose data contribute to building an operating one or more digital user interfaces (UIs) for managing the production of workflow and deliverables within the organization. "Actors" as shown may be end users. In some cases, the end users include one or more administrative end users that set up the system 500 for input by the other end users”. Giving the broadest reasonable interpretation of applicant’s claim limitation in light of the specification, Examiner interprets at least the user and/or owner of a board as taught by Mann as teaching applicant’s administrative user.
receive, by the plurality of end users, input associated with a progression of work within one or more of the sub-categories of topics (Fig 11, Person A, Person B… Person D; #614 “Status”; Fig 13 #1308 sub-item Task”, “Owner”, “Progress”; Task 2”, ”Timeline”, “Progress”; ¶143: “FIG. 98 illustrates an example of a graphical representation changed in response to an update of information in a cell”; ¶273: “FIGS. 231 & 232 illustrate example views of updating the specific first cell from being empty to having an updated milestone indicator”; ¶293: “When one user makes a change in a board, that change may be updated to the board stored in a memory or repository and may be pushed to the other user devices that access that same board. These changes may be made to cells, items, columns, boards, dashboard views, logical rules, or any other data associated with the boards”; Fig 28, ¶476: “a user may select the “Status” cell of “Task 2” 2804 of aggregated “This Week” table 3104 of FIG. 31 in order to change the status from “Working on it” to “Complete.” By another example, a user may add items to underlying tables or aggregated tables via, for example, add buttons 2810 and 2814 of FIG. 28, add button 3014 of FIG. 30, and add button 3106 of FIG. 31”; Fig 126A, Fig 126B, ¶948: “the system that may include a platform for enabling collaboration among several entities. The system may, for example, share information about a task or a project (e.g., due states, current status, priority, collaborators, collaboration notes, or any other information) among the several users. This information may be contained in tables and boards hosted by the platform”) Examiner interprets the limitation as, “receive, by the plurality of end users, input associated with a progression of work within one or more of the sub-categories of tasks”.
continuously monitor the received input from the plurality of end users (¶39: “embodiments may involve at least one processor configured to maintain and cause to be displayed a workflow table having rows, columns and cells at intersections of rows and columns; track a workflow milestone via a designated cell, the designated cell being configured to maintain data indicating that the workflow milestone is reached”; ¶316: “the items in the table may be unifying rows or columns that represent projects, tasks, property, people, or any object, action, or group of actions that may be tracked”; ¶527: “Tracking statuses of items may include monitoring or maintaining a log of statuses or progress. For example, the system may monitor and track due dates and statuses of items to a current date to determine whether specific items are overdue (e.g., the current date is after a due date and the status is not “done the table may be configured to track due dates and statuses of items associated with a workflow. Tracking due dates of items may include monitoring or maintaining a log of dates that may be compared to a current date. Tracking statuses of items may include monitoring or maintaining a log of statuses or progress. For example, the system may monitor and track due dates and statuses of items to a current date to determine whether specific items are overdue (e.g., the current date is after a due date and the status is not “done)”; ¶604: “One or more automations in an automation package may be customized for a profession, a vocation, an industry, a technology, an occupation, a business, or other entities having collaborative workspaces. One or more automations in an automation package may also be customized based on specific use cases for certain tasks, such as tracking project progress, enabling communications between remote individuals, or managing files between teams”).
process the received input from the plurality of end users, using a machine learning modelling module (¶305: “validation examples and/or test examples may include example inputs together with the desired outputs corresponding to the example inputs, a trained machine learning algorithm and/or an intermediately trained machine learning algorithm may be used to estimate outputs for the example inputs of the validation examples and/or test examples, the estimated outputs may be compared to the corresponding desired outputs, and the trained machine learning algorithm and/or the intermediately trained machine learning algorithm may be evaluated based on a result of the comparison”; ¶842; “Characterized functions, capabilities, computational efficiencies, and any other associated attributes may be predefined for each tool, or they may be determined based on the monitored tool usage by applying machine learning and/or artificial intelligence to stored usage data”; ¶843: “Artificial intelligence (i.e., machine learning), as described in more detail earlier, may refer to a system or device's ability to interpret data, to learn from such data, and/or to use such learnings to achieve specific goals and tasks through flexible adaptation”)
wherein an operation of the machine learning module includes: building a prediction model correlating a relationship of the attributes (¶305: “a trained machine learning algorithm may comprise an inference model, such as a predictive model, a classification model, a regression model, a clustering model, a segmentation model, an artificial neural network (such as a deep neural network, a convolutional neural network, a recursive neural network, etc.), a random forest, a support vector machine, and so forth… validation examples and/or test examples may include example inputs together with the desired outputs corresponding to the example inputs, a trained machine learning algorithm and/or an intermediately trained machine learning algorithm may be used to estimate outputs for the example inputs of the validation examples and/or test examples, the estimated outputs may be compared to the corresponding desired outputs, and the trained machine learning algorithm and/or the intermediately trained machine learning algorithm may be evaluated based on a result of the comparison”; ¶842: “Some disclosed embodiments may include comparing an at least one tool historically used by an entity with information relating to a plurality of tools to thereby identify at least one alternative tool in the plurality of tools whose substituted usage is configured to provide improved performance over the at least one historically used tool. An alternative tool may include any tool in the system that an entity is not currently utilizing or a tool in the system that the entity has not utilized often. The alternative tool may provide increased performance over another tool, for example, by its relative ease of use, increased automation, its capabilities, and/or computational efficiency… Characterized functions, capabilities, computational efficiencies, and any other associated attributes may be predefined for each tool, or they may be determined based on the monitored tool usage by applying machine learning and/or artificial intelligence to stored usage data”)
forecasting a direction of the attributes based on the prediction model and a current status of progression of work in each of the sub-categories (¶28: “One aspect of the present disclosure is directed to a systems, methods, and computer readable media for predicting required functionality and for identifying application modules for accomplishing the predicted required functionality”; ¶162; “FIG. 119 is a block diagram of an exemplary process for predicting required functionality and for identifying application modules for accomplishing the predicted required functionality, consistent with some embodiments of the present disclosure”; ¶305: “a trained machine learning algorithm may comprise an inference model, such as a predictive model… the training examples may include example inputs together with the desired outputs corresponding to the example inputs. Further, in some examples, training machine learning algorithms using the training examples may generate a trained machine learning algorithm, and the trained machine learning algorithm may be used to estimate outputs for inputs not included in the training examples. In some examples, engineers, scientists, processes and machines that train machine learning algorithms may further use validation examples and/or test examples. For example, validation examples and/or test examples may include example inputs together with the desired outputs corresponding to the example inputs, a trained machine learning algorithm and/or an intermediately trained machine learning algorithm may be used to estimate outputs for the example inputs of the validation examples and/or test examples, the estimated outputs may be compared to the corresponding desired outputs, and the trained machine learning algorithm and/or the intermediately trained machine learning algorithm may be evaluated based on a result of the comparison”; ¶373; ¶843: “A comparison may be performed, by way of non-limiting example, through the use of artificial intelligence. Artificial intelligence (i.e., machine learning), as described in more detail earlier, may refer to a system or device's ability to interpret data, to learn from such data, and/or to use such learnings to achieve specific goals and tasks through flexible adaptation”; ¶861: “Exemplary application modules may include at least one logical sentence structure (e.g., automation) that triggers different actions when certain conditions are met. Application modules may include a plurality of automations that trigger various actions, thereby providing various functionalities. A functionality may include an output of an application module that may be triggered upon one or more conditions relating to a status of one or more data structures. For example, an application module (e.g., automation) may be configured to monitor a condition and to determine if a particular status is “complete” before the application module triggers an action of archiving a completed task. This application module may be said to include an archiving functionality. Predicting a required functionality may include an analysis, comparison, or any other lookup of characteristics associated with a table to determine commonly associated functions of the table. For example, where a table is organized with team members and contact information (e.g., email addresses, phone numbers, or any other contact information), the system may be configured to predict that the author of the table may des