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 .
Status of Application
This office action is in response to the most recent filings filed by applicant on 11/08/2024.
No claims are amended
No claims are cancelled
No claims are added
Claims 1-20 are pending
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 1-20 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., an abstract idea) without significantly more.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 1-9 is/are directed to a method which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 10 is/are directed to a apparatus which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 11 is/are directed to a non-transitory computer-readable medium which is a statutory category.
Step One - First, pursuant to step 1 in the January 2019 Guidance on 84 Fed. Reg. 53, the claims 12 is/are directed to a device which is a statutory category.
Step 2A Prong 1: Identify the Abstract Idea(s)
The Alice framework, steps 2A-Prong One (part 1 of Mayo Test), here, the claims are analyzed to determine if the claims are directed to a judicial exception. MPEP 2106.04(a). In determining, whether the claims are directed to a judicial exception, the claims are analyzed to evaluate whether the claims recite a judicial exception (Prong One of Step 2A), and whether the claims recite additional elements that integrate the judicial exception into a practical application (Prong Two of Step 2A). See 2019 Revised Patent Subject Matter Eligibility Guidance (“PEG” 2019 Revised Patent Subject Matter Eligibility Guidance, 84 Fed. Reg. 50-57 (Jan. 7, 2019)).
Under the 2019 PEG, Step 2A under which a claim is not “directed to” a judicial exception unless the claim satisfies a two-prong inquiry. Further, particular groupings of abstract ideas are consistent with judicial precedent and are based on an extraction and synthesis of the key concepts identified by the courts as being abstract.
Independent claims 1, with respect to the Step 2A, Prong One, when “taken as a whole” the claims as drafted, and given their broadest reasonable interpretation, fall within the Abstract idea grouping of “certain methods of organizing human activity” (business relations; relationships or interactions between people). For instance, independent Method Claim 1 is directed to an abstract idea, as evidenced by claim limitations “determining, in response to a selection operation for a task link corresponding to a target data task, at least one sub-data task comprised in a target task link corresponding to the selection operation; and displaying task time information of the at least one sub-data task comprised in the target task link, according to a hierarchical relationship of the at least one sub-data task, wherein different timing relationships between task completion time of the at least one sub-data task and service level agreement time of the at least one sub-data task through different display styles.”
These claim limitations belong to the grouping of “certain methods of organizing human activity” because the claims are related to managing collaboration of a plurality of business teams to manage business data so that tasks can be performed on time (see specification [0003]-[0004]) for one or more human entities involves organizing human activity based on the description of “certain methods of organizing human activity” provided by the courts. The court have used the phrase “Certain methods of organizing human activity” as —fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
Independent Claims 10-12 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2A for similar reasons to claim 1 above.
Step 2A Prong 2: Additional Elements That Integrate the Judicial Exception into a Practical Application
With respect to the Step 2A, Prong Two - This judicial exception is not integrated into a practical application. In particular, the claim recites additional elements: “A data task management method, comprising: in a form of a Gantt chart, are displayed in the Gantt chart, A data task management apparatus, comprising: a first determination module, a first display module, configured to display, A non-transitory computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processing apparatus, cause the processing apparatus to implement the method according to claim 1, An electronic device, comprising: a storage apparatus having a computer program stored thereon; and a processing apparatus configured to execute the computer program in the storage apparatus to implement a data task management method, wherein the data task management method comprises” at a high level of generality such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). Accordingly, these additional elements do not integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. The claims are directed to an abstract idea with no significantly more elements.
Thus, the additional elements do not integrate the abstract idea into practical application because they do not impose any meaningful limitations on practicing the abstract idea. As a result, claims 1 and 10-12 does not provide any specifics regarding the integration into a practical application when recited in a claim with a judicial exception. See MPEP 2106.05(f).
Similarly dependent claims 2-9 and 13-20 are also directed to an abstract idea under 2A, first and second prong. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. For instance, dependent claims 2 recite “wherein the displaying task time information of the at least one sub-data task comprised in the target task link in a form of a Gantt chart, comprises: for each sub-data task comprised in the target task link, displaying an average execution time period of the sub-data task in a first rectangular box, an actual execution time period of the sub-data task in a second rectangular box, and service level agreement time of the sub-data task in a graphic marker, wherein the first rectangular box and the second rectangular box are displayed in different display styles.” Dependent claims 3 recite “wherein the displaying task time information of the at least one sub-data task comprised in the target task link in a form of a Gantt chart, comprises: for each sub-data task comprised in the target task link, when the task completion time of the sub-data task is earlier than the service level agreement time of the sub-data task, displaying a timing relationship between the task completion time of the sub-data task and the service level agreement time of the sub-data task in a rectangular box with a first style; when the task completion time of the sub-data task is later than the service level agreement time of the sub-data task, displaying a timing relationship between the task completion time of the sub-data task and the service level agreement time of the sub-data task in a rectangular box with a second style, wherein the first style is different from the second style.”. Here, these claims offer further descriptive limitations of elements found in the independent claims which are similar to the abstract idea noted in the independent claim above.
Dependent claims 5 recites “wherein the Gantt chart displays execution time information of the at least one sub-data task in the form of a rectangular box, and the method further comprises: in response to a hover operation or a selection operation for a target rectangular box in the Gantt chart”. In this claim, “hover operation” is an additional element. Dependent claims 6 recites “wherein the Gantt chart displays execution time information of the at least one sub-data task in a form of a rectangular box”. In this claim, “rectangular box” is an additional element. Dependent claims 7 recites “determining, in response to a configuration operation for a display mode of the target data task, a display mode corresponding to the configuration operation, and displaying a Gantt chart interface when the display mode indicates to display the target data task in the form of a Gantt chart; the determining, in response to a selection operation for a task link corresponding to a target data task, at least one sub-data task comprised in a target task link corresponding to the selection operation, comprises: determining, in response to the selection operation for the task link corresponding to the target data task in the Gantt chart interface, the at least one sub-data task comprised in the target task link corresponding to the selection operation.” In this claim, “display mode” is an additional element. Dependent claims 8 recites “further comprising: determining, in response to a switching operation for a display mode of the target data task, first display mode corresponding to the switching operation; and switching from the Gantt chart interface to first management interface corresponding to the first display mode, wherein the first display mode comprises a list mode and/or a directed acyclic graph mode, the list mode is used to display task information of all sub-data tasks under the target data task in the form of a list, and the directed acyclic graph mode is at least used to display an execution dependency relationship between respective sub-data task in the target data task in a form of a directed acyclic graph.” In this claim, “a directed acyclic graph mode, the list mode” is an additional element. It is still being recited such that it amounts to no more than: adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea, as discussed in MPEP 2106.05(f). As a result, Examiner asserts that dependent claims, such as dependent claims 2-9 and 13-20 are also directed to the abstract idea identified above.
Step 2B: Determine Whether Any Element, Or Combination, Amount to “Significantly More” Than the Abstract Idea Itself
With respect to Step 2B, the claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception. First, the invention lacks improvements to another technology or technical field [see Alice at 2351; 2019 IEG at 55], and lacks meaningful limitations beyond generally linking the use of an abstract idea to a particular technological environment [Alice at 2360, 2019 IEG at 55], and fails to effect a transformation or reduction of a particular article to a different state or thing [2019 IEG, 55]. For the reasons articulated above, the claims recite an abstract idea that is limited to a particular field of endeavor (MPEP § 2106.05(h)) and recites insignificant extra-solution activity (MPEP § 2106.05(g)). By the factors and rationale provided above with respect to these MPEP sections, the additional elements of the claims that fail to integrate the abstract idea into a practical application also fail to amount to “significantly more” than the abstract idea.
As discussed above with respect to integration of the abstract idea into a practical application, the additional element(s) of “A data task management method, comprising: in a form of a Gantt chart, are displayed in the Gantt chart, A data task management apparatus, comprising: a first determination module, a first display module, configured to display, A non-transitory computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processing apparatus, cause the processing apparatus to implement the method according to claim 1, An electronic device, comprising: a storage apparatus having a computer program stored thereon; and a processing apparatus configured to execute the computer program in the storage apparatus to implement a data task management method, wherein the data task management method comprises” are insufficient to amount to significantly more. Applicants originally submitted specification describes the computer components above at least in page/ paragraph [0052]-[0060] and [0070]-[0081]. In light of the specification, it should be noted that the components discussed above did not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.05(f). (See MPEP 2106.05(f) - Mere Instructions to Apply an Exception - “Thus, for example, claims that amount to nothing more than an instruction to apply the abstract idea using a generic computer do not render an abstract idea eligible.” Alice Corp., 134 S. Ct. at 235). Mere instructions to apply an exception using computer component cannot provide an inventive concept.). The additional elements amount to no more than a recitation of generic computer elements utilized to perform generic computer functions, such as performing repetitive calculations, Bancorp Services v. Sun Life, 687 F.3d 1266, 1278, 103 USPQ2d 1425, 1433 (Fed. Cir. 2012) ("The computer required by some of Bancorp’s claims is employed only for its most basic function, the performance of repetitive calculations, and as such does not impose meaningful limits on the scope of those claims."); and storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015); OIP Techs., 788 F.3d at 1363, 115 USPQ2d at 1092-93; see MPEP 2106.05(d)(II).
Therefore, the claims at issue do not require any nonconventional computer, network, or display components, or even a “non-conventional and non-generic arrangement of know, conventional pieces,” but merely call for performance of the claimed on a set of generic computer components” and display devices. All of these additional elements are significantly more because these, again, are merely the software and/or hardware components used to implement the abstract idea on a general-purpose computer. Generically recited computer elements do not add a meaningful limitation to the abstract idea because the Alice decision noted that generic structures that merely apply abstract ideas are not significantly more than the abstract ideas.
The computing elements with a computing device is recited at high level of generality (e.g. a generic device performing a generic computer function of processing data). Thus, this step is no more than mere instructions to apply the exception on a generic computer. In addition, using a processor to process data has been well- understood routing, conventional activity in the industry for many years. Generic computer features, such as system or storage, do not amount to significantly more than the abstract idea. These limitations merely describe implementation for the invention using elements of a general-purpose system, which is not sufficient to amount to significantly more. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am. Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1791 (Federal Circuit 2015).
The claim fails to recite any improvements to another technology or technical field, improvements to the functioning of the computer itself, use of a particular machine, effecting a transformation or reduction of a particular article to a different state or thing, adding unconventional steps that confine the claim to a particular useful application, and/or meaningful limitations beyond generally linking the use of an abstract idea to a particular environment. See 84 Fed. Reg. 55. Viewed individually or as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
Independent Claims 10-12 is/are recite substantially similar limitations to independent claim 1 and is/are rejected under 2B for similar reasons to claim 1 above.
Further, it should be noted that additional elements of the claimed invention such as claim limitations when considered individually or as an ordered combination along with the other limitations discussed above in method claim 1 also do not meaningfully limit the abstract idea because they merely linked the use of the abstract idea to a particular technological environment (i.e., "implementation via computers"). In light of the specification, it should be noted that the claim limitations discussed above are merely instructions to implement the abstract idea on a computer. See MPEP 2106.
Similarly, dependent claims 2-9 and 13-20 also do not include limitations amounting to significantly more than the abstract idea under the second prong or 2B of the Alice framework. In the present application, all of the dependent claims have been evaluated and it was found that they all inherit the deficiencies set forth with respect to the independent claims. Further, it should be noted that the dependent claims do not include limitations that overcome the stated assertions. Here, the dependent claims recite features/limitations that include computer components identified above in part 2B of analysis of independent claims 1, 10-12. As a result, Examiner asserts that dependent claims, such as dependent claims 2-9 and 13-20 are also directed to the abstract idea identified above.
Further, Examiner notes that the addition limitations, when considered as an ordered combination, add nothing that is not already present when looking at the additional elements individually.
For more information on 101 rejections, see MPEP 2106, January 2019 Guidance at https://www.govinfo.gov/content/pkg/FR-2019-01 -07/pdf/2018-28282.pdf
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.
Claim(s) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over (US 2019/0205792 A1) Huang, further in view of (US 2017/0076246 A1) Volkov et al. and (US 2019/0095839 A1) Itabayashi et al.
As per claims 1, 10-12: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
A data task management method, comprising (Huang shows: [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. [0043], [0066], Fig. 3: The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. FIG. 9 is a user interface for the workflow recommender. Huang [0099]-[0100]; Fig. 9):
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
determining, in response to a selection operation for a task link corresponding to a target data task, at least one sub-data task comprised in a target task link corresponding to the selection operation
Claim interpretation: in the above claim limitation, it is unclear what a task link or a target data task means. In the specification, the claim limitation is shown in Fig. 3, and [0051]: For example, as shown in FIG. 3, FIG. 3 is a target data task. It can be seen from the arrow pointing in the figure that the target data task may be disassembled into five task links (a first task link: 1-2-5-10-16, a second task link: 1-2-6-11-16, a third task link: 1-3-7-12-11 (14)-16, a fourth task link: 1-3-8-15-16, and a fifth task link: 1-4-9-13-15-16). Therefore, the corresponding sub-data task may be obtained through the selection operation for the task link. For example, by selecting the second task link, the sub-data task 1, the sub-data task 2, the sub-data task 6, the sub-data task 11, and the sub-data task 16 may be obtained. As such, it is reasonably understood that a task link is understood to be the sequence of steps or sub-tasks performed within a task and target data task is understood as all the subtasks put together. In light of this description:
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A); and
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
displaying task time information of the at least one sub-data task comprised in the target task link in a form of a Gantt chart, according to a hierarchical relationship of the at least one sub-data task, wherein different timing relationships between task completion time of the at least one sub-data task and service level agreement time of the at least one sub-data task are displayed in the Gantt chart through different display styles.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 2 and 13: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the displaying task time information of the at least one sub-data task comprised in the target task link in a form of a Gantt chart, comprises:
for each sub-data task comprised in the target task link, displaying an average execution time period of the sub-data task in a first rectangular box, an actual execution time period of the sub-data task in a second rectangular box, and service level agreement time of the sub-data task in a graphic marker, wherein the first rectangular box and the second rectangular box are displayed in different display styles.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “displaying an average execution time period of the sub-data task in a first rectangular box, an actual execution time period of the sub-data task in a second rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 3 and 14: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the displaying task time information of the at least one sub-data task comprised in the target task link in a form of a Gantt chart, comprises:
for each sub-data task comprised in the target task link, when the task completion time of the sub-data task is earlier than the service level agreement time of the sub-data task, displaying a timing relationship between the task completion time of the sub-data task and the service level agreement time of the sub-data task in a rectangular box with a first style
Reference Huang shows [0094] FIG. 8 illustrates a method for recommending possible sequences, according to some example embodiments. At each step, there may be more than one candidate for the next step (e.g., at steps 804, 806, and 808). All the candidate next steps are ranked based on their likelihood scores, and a threshold τ is used to filter out unlikely candidates or candidates with low scores. As the algorithm traverses and selects the next step, the algorithm forms at least one candidate sequence to be recommended. Although higher-scored thresholds produce better sequences, high thresholds may become too limiting and make the algorithm unable to complete a sequence. [0097] It is noted that some sequences may include subsequences that may be executed in parallel, and the possibilities for the parallel subsequences may be explored in parallel in order to get to the solution faster. [0098] In some example embodiments, when a path is completed (or terminated), the process goes back to the previous step to see if there are more candidates to explore. If there are, the process continues generating more viable paths until all candidates at each position level are exhausted or preset constraints are reached, such as a maximum number of paths or a maximum processing time for generating sequences;
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
when the task completion time of the sub-data task is later than the service level agreement time of the sub-data task, displaying a timing relationship between the task completion time of the sub-data task and the service level agreement time of the sub-data task in a rectangular box with a second style, wherein the first style is different from the second style.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 4 and 15: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the displaying a timing relationship between the task completion time of the sub-data task and the service level agreement time of the sub-data task in a rectangular box with a first style, comprises:
displaying, in a first display color, a rectangular box representing an actual execution time period of the sub-data task, and/or displaying the rectangular box representing the actual execution time period of the sub-data task and a graphic marker representing the service level agreement time of the sub-data task in a non-overlapping manner; and
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “displaying an average execution time period of the sub-data task in a first rectangular box, an actual execution time period of the sub-data task in a second rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
the displaying a timing relationship between the task completion time of the sub-data task and the service level agreement time of the sub-data task in a rectangular box with a second style, comprises:
displaying, in a second display color, a rectangular box representing the actual execution time period of the sub-data task, and/or displaying the rectangular box representing the actual execution time period of the sub-data task and the graphic marker representing the service level agreement time of the sub-data task in an overlapping manner.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “displaying an average execution time period of the sub-data task in a first rectangular box, an actual execution time period of the sub-data task in a second rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 5 and 16: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the Gantt chart displays execution time information of the at least one sub-data task in the form of a rectangular box, and the method further comprises:
in response to a hover operation or a selection operation for a target rectangular box in the Gantt chart, displaying task time information of a sub-data task corresponding to the target rectangular box,
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the task time information comprises at least one selected from the group consisting of average completion time, actual completion time, and service level agreement time of the sub-data task; and/or when task identification information of a sub-data task is displayed, displaying task attribute information of the sub-data task corresponding to the task identification information in response to a hover operation or a selection operation for the task identification information, wherein the task attribute information comprises a signature status of the service level agreement time of the sub-data task and/or business team information to which the sub-data task belongs.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 6 and 17: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the Gantt chart displays execution time information of the at least one sub-data task in a form of a rectangular box, and the Gantt chart displays the service level agreement time of the at least one sub-data task in a first graphic marker, and the method further comprises:
displaying first graphic markers corresponding to sub-data tasks with different signature statuses in different colors, wherein the signature statuses are signature statuses of the service level agreement time of the sub-data tasks; and/or
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” and “displaying first graphic markers corresponding to sub-data tasks with different signature statuses in different colors, wherein the signature statuses are signature statuses of the service level agreement time of the sub-data tasks” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
when task identification information of a sub-data task is displayed, displaying a signature status around the task identification information through a second graphic marker, wherein different signature statuses are displayed in different styles of second graphic markers, and the first graphic marker is different from the second graphic marker.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” and “displaying first graphic markers corresponding to sub-data tasks with different signature statuses in different colors, wherein the signature statuses are signature statuses of the service level agreement time of the sub-data tasks” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 7 and 18: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
further comprising:
determining, in response to a configuration operation for a display mode of the target data task, a display mode corresponding to the configuration operation, and displaying a Gantt chart interface when the display mode indicates to display the target data task in the form of a Gantt chart;
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
the determining, in response to a selection operation for a task link corresponding to a target data task, at least one sub-data task comprised in a target task link corresponding to the selection operation, comprises:
determining, in response to the selection operation for the task link corresponding to the target data task in the Gantt chart interface, the at least one sub-data task comprised in the target task link corresponding to the selection operation.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
As per claims 8 and 19: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
further comprising:
determining, in response to a switching operation for a display mode of the target data task, first display mode corresponding to the switching operation; and
switching from the Gantt chart interface to first management interface corresponding to the first display mode,
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A);
Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
wherein the first display mode comprises a list mode and/or a directed acyclic graph mode, the list mode is used to display task information of all sub-data tasks under the target data task in the form of a list, and the directed acyclic graph mode is at least used to display an execution dependency relationship between respective sub-data task in the target data task in a form of a directed acyclic graph.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Itabayashi shows “display an execution dependency relationship between respective sub-data task in the target data task in a form of a directed acyclic graph”: The matrix 1101 of the relevance of FIG. 11 shows an example of a result obtained when the DSM is used to perform partitioning on the matrix 1000 before correction to sequence the subtasks. The partitioning is an analysis technique to optimize the process. For example, the probabilities indicating a dependent relation between the subtasks are moved down below the diagonal by changing the order of rows and columns of the DSM. Therefore, it is possible to remove a loop that the subtask is reversely progressed from the later process to the previous process. In other words, the process is reduced in reworking and correcting, and thus is optimized. In an actual DSM, it is not possible to move down all the probabilities below the diagonal. In that case, the probabilities are arranged above the diagonal as many as possible to easily perform the subtasks repeatedly by linking the subtasks. Therefore, it is possible to finish the cycle in a short period of time.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A);
As per claims 9 and 20: Regarding the claim limitations below, Reference Huang, Reference Volkov, Reference Itabayashi shows:
further comprising:
determining, in response to a task screening operation, a screening condition corresponding to the task screening operation, and updating the task time information displayed in the Gantt chart according to the screening condition, so as to make the Gantt chart display task time information of one or more sub-data tasks that meet the screening condition.
Huang shows in [0041], [0159]-[0161]: a system for creating a workflow is provided. The system includes a sequence generator, a workflow engine, and a workflow recommender. The sequence generator is to generate a plurality of training sequences. Also see: Fig. 1, 12. The workflow engine 306 and the machine-learning algorithm are trained with the generated sequences and the workflow data 314 created and stored in the database. Huang [para. 0043, 0066; Fig. 3. FIG. 3 is an architecture of a system for evaluating the performance of example embodiments. Huang [0062]; Fig. 3. During stage 1, the system is trained with sample workflows, context, and constraints 202. This workflow training 202 includes operations 206, 208, and 210. … At operation 210, associations are established among contexts, constraints and step attributes, and the system accumulated associations are kept in the associative memory. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. Huang [0032], [0053]-[0057], [0161]; Fig. 2-3. … FIG. 6 illustrates a method for workflow learning, according to some example embodiments. The workflow engine 306 learns the relationships between the contexts 502 and the steps 504 before recommending sequences. Huang [0082]-[0087]; Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16. In some example embodiments, sample sequences are learned by the system, and then the system is asked to create a sequence based on related constraints. The results are compared to the original sequences to determine if the system is capable of creating valid sequences. … For a complex workflow, the problem is recursively decomposed into simpler sub-problems until the sub-problems may be solved at the component level. Then, all solutions to sub problems are merged to form the final workflow plan.
Huang shows in “displaying an average execution time period of the sub-data task in a first rectangular box, an actual execution time period of the sub-data task in a second rectangular box” Fig. 9, [0099]-[0102]: FIG. 9 is a user interface for the workflow recommender, according to some example embodiments. The user interface 902 provides options to the data analyst for entering inputs and interacting with the workflow engine. In some example embodiments, the user interface 902 includes a plurality of screens, such as “new & interesting,” “missing links,” and “sequence” 904. The user interface 902 shows the sequence 904 option selected. Here, Huang shows the rectangular boxes in Fig. 9 that is similar to the rectangular boxes referred in the claim above, which is further referencing Figs.4-7 in the drawings of the current application.
Huang also shows “service level agreement“: [0038] The system also allows for self-managed planning by decomposing sub-goals and exploring plans automatically, refining constraints and managing contexts autonomously, automatically incorporating new contextual information into the planning process, and interact with the user by recognizing and prompting for irresolvable goals. [0092] The next step is encoded, and since the previous step is t20 708, t30 716 gets encoded as prev1:t20, prev2:t10, and prev3:t1. Once a candidate selected as the next step, it becomes the new “current” step, and the contexts and relative attributes are updated to form new set of attributes querying for new next steps. The process is then repeated for t30 716 to calculate the next step until the sequence is completed by reaching the desired goal. [0101] For example, a request is entered to create a workflow for building a taco restaurant. The constraints may include items such as “build the restaurant,” “taco restaurant.” “in California.” and “with at least 33% Hispanic population in town.” A way to relax the constraints would be by specifying, “in California or in Texas.” Further, a way to increase the constraints would be by adding a constraint such as “city population greater than 200,000.” [0076]: the inputs to the sequence generator 508 include contexts 502, steps 504, context labels 512, and step labels 516. The outputs of the sequence generator 508 include a next step 506, contexts 502 for the next step 506, and output data 510. The contexts 502, in an example embodiment, are unordered binary properties representing the metadata or conditions about the sequence. The number of steps in sequences may vary or may be fixed. Further, a maximum number of steps may be defined by the administrator. In some example embodiments, the maximum number of steps and the available task labels can be specified to simulate the scale of the target problem. [0143] FIG. 16 illustrates how to connect workflow components, according to some example embodiments. Sequences can be created step-by-step going either forward or backwards given certain input parameters. In some example embodiments, creating the sequence is broken into sub-problems by decomposing the original problem into the sub-problems with updated contexts and goals. The system then proceeds to solve all the sub-problems.
Although Reference Huang shows the ability to reduce a task sequence into “sub” problems, Huang does not explicitly show “at least one sub-data task” as is recited in the claim. However, the function taught in Huang for decomposing workflows into simpler sub problems could reasonably understood as reading on the claim above.
Volkov shows the above limitation at least in FIG. 1 shows one example of a workforce management system 100 according to one embodiment of the present invention. … The workflow management module 202 manages tasks and generates tasks for a workflow … The workflow management module 202 maintains information associated with tasks as task data 212. This task data 212 may be stored within the workforce management server 104 and/or on one or systems coupled to the server 104. Volkov et al. [0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]: In certain embodiments, task results may undergo a features extraction process to program machine learning features in a model in order to become training data. … As discussed above, machine learning models may continue to learn using additional training data received from concurrent completion of human tasks. … As another example, a workflow optimization may include splitting a particular task up into smaller sub-tasks. … The workflow optimization may suggest splitting up this single task into various subtasks.
Reference Huang and Reference Volkov are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Volkov, particularly the ability to acquire data about sub tasks in a task sequence or workflow ([0014], [0017]-[0019]; Fig. 1-2. … [0044]-[0055]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that recommends tasks and subtasks for the completion of a design product to modify the functions of Huang to include functions for defining subtasks with respect to the task design history data and generating subtask relevant data indicating a relevance between subtasks with respect to the plurality of pieces of design process data as taught by Volkov in [0051] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Volkov, the results of the combination were predictable (MPEP 2143 A).
Neither Reference Huang nor Reference Volkov show “Gantt chart”. Reference Itabayashi shows the above limitations at least in [0063]: Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. Further, this device can normally operate even under an environment where the date information of the task is not possible to be normally acquired.
Reference Huang and Reference Itabayashi are analogous prior art to the claimed invention because the references generally relate to field of workflow management. Further, said references are part of the same classification, i.e., G06Q10. Lastly, said references are filed before the effective filing date of the instant application; hence, said references are analogous prior-art references.
It would have been obvious to one of ordinary skill in the art before the effective filing date of this application for AIA to provide the teachings of Reference Itabayashi, particularly the ability to use a Gantt chart in the sub task data extraction process ([0063]), in the disclosure of Reference Huang, particularly in the system that extracts training data to train the machine learning algorithm for creating sub-tasks (Fig. 6. Huang [0057]-[0058], [0084]-[0092], [0137]-[0143]; Fig. 6-7, 14-16), in order to provide for a system that acquires the date information, for example, when a keyword such as a project name and a product name included in a creation file of the design history data TB1 is retrieved, the attribute information such as the creation date and the work person is narrowed down in a hit file to select the design procedure table. The date information is acquired on the basis of the contents. Even in a case where a design process table is not yet established, the date is acquired on the basis of a file in which a Gantt chart and a due data are indicated to acquire the date information such as a task due date. as taught by Itabayashi in [0063] in order to accommodate unforeseen changes in workflows so that the process of workflow management can be made more efficient and effective.
Further, the claimed invention is merely a combination of old elements in a similar workflow management field of endeavor, and in the combination each element merely would have performed the same function as it did separately, and one of ordinary skill in the art would have recognized that, given the existing technical ability to combine the elements as evidenced by Reference Huang in view of Reference Itabayashi, the results of the combination were predictable (MPEP 2143 A).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
NPL Reference:
Y. Lao, Y. Wang, Z. Chen, Y. Kong, J. Shen and H. Li, "A Machine Learning Process-based Training Task Execution Method in the Field of Power Grid Regulation," 2022 Asian Conference on Frontiers of Power and Energy (ACFPE), Chengdu, China, 2022, pp. 203-210, doi: 10.1109/ACFPE56003.2022.9952265.
With the rapid development of artificial intelligence technology, the power industry has entered the era of big data, business data is rapidly accumulated, and the traditional Spring-Boot-based microservice architecture raises more and more requirements for hardware resources, which can no longer meet our requirements for service invocation performance, data consistency, elastic scaling and flexible deployment requirements. In response to the above problems, the distributed container technology which is based on Kubernetes and Docker is introduced, and a unified JSON-based machine learning process description language structure is proposed, some useful configuration templates are provided for machine learning training processes, including algorithm selection, hyper-parameter setting, loss function, optimization function and execution plan. In response to the needs of enterprise business development, a machine learning model training task scheduling system adapted to business scenarios in the field of power grid regulation is designed and constructed, which solves the problems of inability to reuse sample data and waste of resources and realizes resource isolation and elastic scaling. By building a visualized machine learning task process, implementing model training and evaluation, supporting real-time display of the execution status of each algorithm node, the platform implements a multi-tenant resource isolation and elastic scaling containerized machine learning model training environment.
Foreign Reference:
(WO 2018125337 A2) Huang. System For Creating Workflow For Desired Task In Machine, Has Workflow Engine That Trains Machine Learning Algorithm Utilizing Learning Sequence, Receive Workflow Definition, Utilize Machine-learning Algorithm And Select Result Sequence.
This reference discusses the system (300) has workflow engine (306) that trains a machine-learning algorithm utilizing learning sequences, in which each learning sequence is made up of a learning context, at least one learning step, and a learning result. The workflow engine then receives a workflow definition that includes at least one input context and a desired result, the input context including at least one input constraint, generate, utilizing the machine-learning algorithm, at least one result sequence that implements the workflow definition, and select one of the at least one result sequence. A workflow recommender (308) causes the selected result sequence to be presented on a display.
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/N.N.P/Examiner, Art Unit 3624 /PATRICIA H MUNSON/Supervisory Patent Examiner, Art Unit 3624