DETAILED ACTION
This final Office action is responsive to Applicant’s amendment filed March 17, 2026. Claims 1, 11, and 19 have been amended. Claims 1-20 are presented for examination.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
Applicant's arguments filed March 17, 2026 have been fully considered but they are not persuasive.
Regarding the rejection under 35 U.S.C. § 101, Applicant argues that limiting the regeneration of the workflow schedule by only including uncompleted tasks reduces utilized network bandwidth (pages 10-11 of Applicant’s response). While this may be true, this benefit is a natural result of using comparatively less data and both humans and general-purpose machines would be able to process a smaller amount of similar data more quickly. The claims do not present specific technical details (beyond generic processing elements) and corresponding functionality that convey a specific technical solution that reduces network bandwidth usage.
Furthermore, re-generating the workflow schedule by only including uncompleted tasks is an example of filtering data. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.” The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Regarding the rejection under 35 U.S.C. § 103, Applicant submits that the prior art references do not address the claim amendments (pages 12-13 of Applicant’s response). The Guha reference has been introduced into the rejection in order to help address the claim amendments.
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 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claimed invention is directed to “manag[ing] a workflow for a product release” (Spec: ¶ 1) without significantly more.
Step
Analysis
1: Statutory Category?
Yes – The claims fall within at least one of the four categories of patent eligible subject matter. Process (claims 11-18), Apparatus (claims 1-10), Article of Manufacture (claims 19-20)
Independent Claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 1, 11, 19] receive a request to generate a schedule for execution of a workflow associated with at least one product;
retrieve one or more workflow parameters and two or more location parameters for two or more locations, wherein the workflow parameters include a same final date for the two or more locations, the tasks included in the workflow, a sequence for completing the tasks, and one or more resources for completing each task;
input the retrieved one or more workflow parameters and one or more location parameters into a scheduler;
automatically generate a workflow schedule via an algorithm of the scheduler based on the retrieved one or more workflow parameters and location parameters, wherein the algorithm uses historical data and generates the workflow by applying data for a current simulation to the algorithm [and wherein the workflow schedule includes a date of execution for each task (claims 11, 19)];
import the generated workflow schedule;
in a case it is determined that the one or more resources are unavailable for each task:
transmit a message to re-generate the workflow schedule, wherein the message includes one or more constraints causing a resource to be unavailable for a task, the retrieved one or more workflow parameters, and one or more location parameters;
re-generate the workflow schedule, wherein the re-generated workflow schedule includes only uncompleted tasks;
transmit one of the generated workflow schedule and the re-generated workflow schedule to a downstream system;
automatically alter operation of the downstream system based on the re-generated workflow schedule; and
present displays that provide information about the schedule.
It is noted that a “scheduler” is described as a “model algorithm” (e.g., see Spec: ¶ 43). Paragraph 43 of Applicant’s Specification further states, “The scheduler model algorithm 220 may be a machine learning algorithm used to perform automatic scheduling of tasks in a workflow (described in detail with respect to FIG. 13), based in part on the amount of time each task takes to complete.”
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can gather data, input data into a model, generate a workflow schedule, present schedule information on a display, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “manag[ing] a workflow for a product release” (Spec: ¶ 1), which (under its broadest reasonable interpretation) is an example of sales and marketing (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity. Additionally, a human user may be instructed to automatically (e.g., in direct response to a trigger) operate the downstream system based on one of the generated workflow schedule and the re-generated workflow schedule, which is another example of organizing human activity.
Re-generating the workflow schedule by only including uncompleted tasks is an example of filtering data. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
Claim 1 and its dependent claims recite a system implemented via a back-end application computer server of an enterprise, comprising:
(a) a workflow data store containing a plurality of workflows, each workflow including a plurality of executable tasks;
(b) the back-end application computer server, coupled to the workflow data store, including:
a computer processor;
a computer memory coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server to perform the recited functions; and
(c) a communication port coupled to the back-end application computer server to facilitate an exchange of data with a remote device to support interactive user interface displays that provide information about the schedule.
Claim 1 further recites that the generated workflow schedule is imported to a resource management tool; that (in certain cases) it is determined by the resource management tool that the one or more resources are unavailable for each task; and that the one of the generated workflow schedule and the re-generated workflow schedule is transmitted to a downstream system.
Claim 1 recites automatically generating a workflow schedule via a trained machine learning (ML) algorithm of the scheduler based on the retrieved one or more workflow parameters and location parameters, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm and performing further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions.
Claim 1 recites automatically, by the downstream system, altering operation of the downstream system.
Claim 9 recites that the re-generated workflow schedule is imported to the resource management tool.
Claim 10 identifies, via the resource management tool, one or more resources adapted to execute the workflow schedule.
Claim 11 and its dependent claims recite that the recited method is implemented via a back-end application computer server of an enterprise.
Claim 11 further recites that the generated workflow schedule is imported to a resource management tool; that (in certain cases) it is determined by the resource management tool that the one or more resources are unavailable for each task; and that the one of the generated workflow schedule and the re-generated workflow schedule is transmitted to a downstream system.
Claim 11 recites automatically generating a workflow schedule via a trained machine learning (ML) algorithm of the scheduler based on the retrieved one or more workflow parameters and one or more location parameters, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm and performing further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions.
Claim 11 recites automatically, by the downstream system, altering operation of the downstream system.
Claim 17 recites that the re-generated workflow schedule is imported to the resource management tool.
Claim 18 identifies, via the resource management tool, one or more resources adapted to execute the workflow schedule.
Claims 19-20 recite a non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the recited method implemented via a back-end application computer server of an enterprise.
Claim 19 further recites that the generated workflow schedule is imported to a resource management tool; that (in certain cases) it is determined by the resource management tool that the one or more resources are unavailable for each task; and that the one of the generated workflow schedule and the re-generated workflow schedule is transmitted to a downstream system.
Claim 19 recites automatically generating a workflow schedule via a trained machine learning (ML) algorithm of the scheduler based on the retrieved one or more workflow parameters and one or more location parameters, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm, and performing further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions.
Claim 19 recites automatically, by the downstream system, altering operation of the downstream system.
Regarding claims 1, 11, and 19, “reducing used network bandwidth” is a natural result of processing a comparatively smaller amount of data, which would be achieved by generic processing performed by any general-purpose processor.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 35-40, 60-76).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas 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(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 66 – “As described in more detail below, the historical data 1304 is employed to train a machine learning model to provide an output that indicates an identified performance metric and/or an algorithm to generate a schedule for a workflow, and the current data 1306 is thereafter analyzed by the model. Moreover, as time goes by, and results become known from processing current workflow schedules, at least some of the current decisions may be used to perform further training of the model. Consequently, the model may thereby adapt itself to changing conditions.“; ¶ 71 – “The machine learning model component 1318 may operate generally in accordance with conventional principles for machine learning models, except, as noted herein, for at least some of the types of data to which the machine learning model component is applied. Those who are skilled in the art are generally familiar with programming of predictive/machine learning models. It is within the abilities of those who are skilled in the art, if guided by the teachings of this disclosure, to program a predictive/machine learning model to operate as described herein.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Dependent Claims:
Step
Analysis
2A – Prong 1: Judicial Exception Recited?
Yes – Aside from the additional elements identified in Step 2A – Prong 2 below, the claims recite:
[Claims 2, 11, 19] wherein the workflow schedule includes a date of execution for each task.
[Claims 3, 12] wherein the request includes a release date associated with the at least one product.
[Claims 4, 13, 20] wherein the two or more location parameters are linked to a location and define one or more location-specific rules, and a date of execution for at least one task is based on the one or more location-specific rules.
[Claims 5, 14] wherein the executable task is at least one of: researching regulations, defining requirements, executing a pricing engine, and filing the product with an approval organization.
[Claims 6, 15] wherein the workflow is associated with one of: an update of the product and an extension of the product.
[Claims 7, 16] wherein the product is at least one of an automobile product and a home product.
[Claim 8] wherein the location is at least one state of the United States of America.
[Claims 9, 17] receive a modification to a first task in the workflow of the request;
re-generate the workflow schedule in response to the received modification; and
import the re-generated workflow schedule.
[Claims 10, 18] identify one or more resources adapted to execute the workflow schedule.
The dependent claims further define the abstract ideas identified in regard to the independent claims above.
It is noted that a “scheduler” is described as a “model algorithm” (e.g., see Spec: ¶ 43). Paragraph 43 of Applicant’s Specification further states, “The scheduler model algorithm 220 may be a machine learning algorithm used to perform automatic scheduling of tasks in a workflow (described in detail with respect to FIG. 13), based in part on the amount of time each task takes to complete.”
Aside from the additional elements, the aforementioned claim details exemplify the abstract idea(s) of a mental process (since the details include concepts performed in the human mind, including an observation, evaluation, judgment, and/or opinion). As explained in MPEP § 2106(a)(2)(C)(III), “The courts consider a mental process (thinking) that ‘can be performed in the human mind, or by a human using a pen and paper’ to be an abstract idea. CyberSource Corp. v. Retail Decisions, Inc., 654 F.3d 1366, 1372, 99 USPQ2d 1690, 1695 (Fed. Cir. 2011). As the Federal Circuit explained, ‘methods which can be performed mentally, or which are the equivalent of human mental work, are unpatentable abstract ideas the ‘basic tools of scientific and technological work’ that are open to all.’’ 654 F.3d at 1371, 99 USPQ2d at 1694 (citing Gottschalk v. Benson, 409 U.S. 63, 175 USPQ 673 (1972)).” The limitations reproduced above, as drafted, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind but for the recitation of generic computer components. That is, other than reciting the additional elements identified in Step 2A – Prong 2 below, nothing in the claim elements precludes the steps from practically being performed in the mind and/or by a human using a pen and paper. For example, but for the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the respectively recited steps/functions of the claims, as drafted and set forth above, are a process that, under its broadest reasonable interpretation, covers performance of the limitations in the mind and/or with the use of pen and paper. A human user can gather data, input data into a model, generate a workflow schedule, present schedule information on a display, etc. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind (and/or with pen and paper) but for the recitation of generic computer components, then it falls within the “Mental Processes” grouping of abstract ideas. Accordingly, the claims recite an abstract idea.
Aside from the additional elements, the aforementioned claim details exemplify a method of organizing human activity (since the details include examples of commercial or legal interactions, including advertising, marketing or sales activities or behaviors, and/or business relations and managing personal behavior or relationships or interactions between people, including social activities, teaching, and following rules or instructions). More specifically, the evaluated process is related to “manag[ing] a workflow for a product release” (Spec: ¶ 1), which (under its broadest reasonable interpretation) is an example of sales and marketing (i.e., organizing human activity); therefore, aside from the recitations of generic computer and other processing components (identified in Step 2A – Prong 2 below), the limitations identified in the more detailed claim listing above encompass the abstract idea of organizing human activity. Additionally, a human user may be instructed to automatically (e.g., in direct response to a trigger) operate the downstream system based on one of the generated workflow schedule and the re-generated workflow schedule, which is another example of organizing human activity.
Re-generating the workflow schedule by only including uncompleted tasks is an example of filtering data. MPEP § 2106.04(a)(2)(II)(C) cites the following as an example of managing personal behavior, i.e., organizing human activity: “filtering content, BASCOM Global Internet v. AT&T Mobility, LLC, 827 F.3d 1341, 1345-46, 119 USPQ2d 1236, 1239 (Fed. Cir. 2016) (finding that filtering content was an abstract idea under step 2A, but reversing an invalidity judgment of ineligibility due to an inadequate step 2B analysis).” MPEP § 2106.04(a)(2)(III)(D) cites the following as an example of a mental process: “An application program interface for extracting and processing information from a diversity of types of hard copy documents – Content Extraction, 776 F.3d at 1345, 113 USPQ2d at 1356.”
2A – Prong 2: Integrated into a Practical Application?
No – The judicial exception(s) is/are not integrated into a practical application.
The dependent claims incorporate the additional elements from the independent claims from which they depend.
Claim 1 and its dependent claims recite a system implemented via a back-end application computer server of an enterprise, comprising:
(a) a workflow data store containing a plurality of workflows, each workflow including a plurality of executable tasks;
(b) the back-end application computer server, coupled to the workflow data store, including:
a computer processor;
a computer memory coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server to perform the recited functions; and
(c) a communication port coupled to the back-end application computer server to facilitate an exchange of data with a remote device to support interactive user interface displays that provide information about the schedule.
Claim 1 further recites that the generated workflow schedule is imported to a resource management tool; that (in certain cases) it is determined by the resource management tool that the one or more resources are unavailable for each task; and that the one of the generated workflow schedule and the re-generated workflow schedule is transmitted to a downstream system.
Claim 1 recites automatically generating a workflow schedule via a trained machine learning (ML) algorithm of the scheduler based on the retrieved one or more workflow parameters and location parameters, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm and performing further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions.
Claim 1 recites automatically, by the downstream system, altering operation of the downstream system.
Claim 9 recites that the re-generated workflow schedule is imported to the resource management tool.
Claim 10 identifies, via the resource management tool, one or more resources adapted to execute the workflow schedule.
Claim 11 and its dependent claims recite that the recited method is implemented via a back-end application computer server of an enterprise.
Claim 11 further recites that the generated workflow schedule is imported to a resource management tool; that (in certain cases) it is determined by the resource management tool that the one or more resources are unavailable for each task; and that the one of the generated workflow schedule and the re-generated workflow schedule is transmitted to a downstream system.
Claim 11 recites automatically generating a workflow schedule via a trained machine learning (ML) algorithm of the scheduler based on the retrieved one or more workflow parameters and one or more location parameters, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm and performing further training of the ML algorithm as results become known from processing generated
workflow schedules such that the ML algorithm adapts itself to changing conditions.
Claim 11 recites automatically, by the downstream system, altering operation of the downstream system.
Claim 17 recites that the re-generated workflow schedule is imported to the resource management tool.
Claim 18 identifies, via the resource management tool, one or more resources adapted to execute the workflow schedule.
Claims 19-20 recite a non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform the recited method implemented via a back-end application computer server of an enterprise.
Claim 19 further recites that the generated workflow schedule is imported to a resource management tool; that (in certain cases) it is determined by the resource management tool that the one or more resources are unavailable for each task; and that the one of the generated workflow schedule and the re-generated workflow schedule is transmitted to a downstream system.
Claim 19 recites automatically generating a workflow schedule via a trained machine learning (ML) algorithm of the scheduler based on the retrieved one or more workflow parameters and one or more location parameters, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm, and performing further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions.
Claim 19 recites automatically, by the downstream system, altering operation of the downstream system.
Regarding claims 1, 11, and 19, “reducing used network bandwidth” is a natural result of processing a comparatively smaller amount of data, which would be achieved by generic processing performed by any general-purpose processor.
The claims as a whole merely describe how to generally “apply” the abstract idea(s) in a computer environment. The claimed processing elements are recited at a high level of generality and are merely invoked as a tool to perform the abstract idea(s). Simply implementing the abstract idea(s) on a general-purpose processor is not a practical application of the abstract idea(s); Applicant’s specification discloses that the invention may be implemented using general-purpose processing elements and other generic components (Spec: ¶¶ 35-40, 60-76).
The use of a processor/processing elements (e.g., as recited in all of the claims) facilitates generic processor operations. The use of a memory or machine-readable media with executable instructions facilitates generic processor operations.
The additional elements are recited at a high-level of generality (i.e., as generic processing elements performing generic computer functions) such that the incorporation of the additional processing elements amounts to no more than mere instructions to apply the judicial exception(s) using generic computer components. There is no indication in the Specification that the steps/functions of the claims require any inventive programming or necessitate any specialized or other inventive computer components (i.e., the steps/functions of the claims may be implemented using capabilities of general-purpose computer components). Accordingly, the additional elements do not integrate the abstract ideas 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(s).
The processing components presented in the claims simply utilize the capabilities of a general-purpose computer and are, thus, merely tools to implement the abstract idea(s). As seen in MPEP § 2106.05(a)(I) and § 2106.05(f)(2), the court found that accelerating a process when the increased speed solely comes from the capabilities of a general-purpose computer is not sufficient to show an improvement in computer-functionality and it amounts to a mere invocation of computers or machinery as a tool to perform an existing process (see FairWarning IP, LLC v. Iatric Sys., 839 F.3d 1089, 1095, 120 USPQ2d 1293, 1296 (Fed. Cir. 2016)).
Considering that the implementation of the machine learning model and/or the training of the model is performed using generic processing elements, such an implementation is presented as a generic recitation of machine learning in the claims and as a general link to technology. The machine learning-based processing elements are simply tools to generally automate the underlying process that could be performed by a human. It is further noted that, as described in Applicant’s Specification, the machine learning operations are generic machine learning operations (Spec: ¶ 66 – “As described in more detail below, the historical data 1304 is employed to train a machine learning model to provide an output that indicates an identified performance metric and/or an algorithm to generate a schedule for a workflow, and the current data 1306 is thereafter analyzed by the model. Moreover, as time goes by, and results become known from processing current workflow schedules, at least some of the current decisions may be used to perform further training of the model. Consequently, the model may thereby adapt itself to changing conditions.“; ¶ 71 – “The machine learning model component 1318 may operate generally in accordance with conventional principles for machine learning models, except, as noted herein, for at least some of the types of data to which the machine learning model component is applied. Those who are skilled in the art are generally familiar with programming of predictive/machine learning models. It is within the abilities of those who are skilled in the art, if guided by the teachings of this disclosure, to program a predictive/machine learning model to operate as described herein.”). The Specification presents no assertion that there is any improvement in the automated machine learning process itself. Such a generic recitation of machine learning, as recited in the claims, is little more than automating an analogous process that can be performed by a human.
There is no transformation or reduction of a particular article to a different state or thing recited in the claims.
Additionally, even when considering the operations of the additional elements as an ordered combination, the ordered combination does not amount to significantly more than what is present in the claims when each operation is considered separately.
2B: Claim(s) Provide(s) an Inventive Concept?
No – The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception(s). As discussed above with respect to integration of the abstract idea(s) into a practical application, the use of the additional elements to perform the steps identified in Step 2A – Prong 1 above amounts to no more than mere instructions to apply the exceptions using a generic computer component(s). Mere instructions to apply an exception using a generic computer component(s) cannot provide an inventive concept. The claims are not patent eligible.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over
Seki et al. (JP-2023017452-A, citing the English Translation of JP 2023017452 A, Original document published February 7, 2023) in view of Bissonette et al. (US 2020/0233662) in view of Iida (JP-2022143426-A, citing the English Translation of JP 2022143426 A, Original document published October 3, 2022) in view of Guha et al. (US 2024/0104467).
[Claim 1] Seki discloses a system implemented via a back-end application computer server of an enterprise (p. 3 – “An example of the flow of processing executed by the control unit 12 of the recommended filing date providing system 10 configured as described above will be described with reference to FIG.”; p. 10 – “In the first embodiment described above, the recommended filing date providing system 10 is connected to the user's terminal 30 via the network 20.” While Seki does not explicitly disclose that the back-end application computer server is “of an enterprise,” this limitation does not impart significant patentable weight on the claims as a whole and does not serve to patentably distinguish the claims over the prior art since it is limited to the preamble and does not breathe life into the body of the claims. Additionally, the phrase “of the enterprise” does not affect any limiting structural or functional elements of the claims.), comprising:
(a) a workflow data store containing a plurality of workflows, each workflow including a plurality of executable tasks (p. 1 – “A storage unit stores a main database including a required number of days from an application date to a laying-open date of the design application in each country.”; p. 5 -- “For example, as shown in FIG. 6, if China is entered in addition to Japan as the country to which the application is to be filed on the input screen IS, the required number of days from the filing dates of the Chinese application to the publication date is four months based on the main database 111. and the recommended filing date for China is calculated as March 10, 2021, four months earlier than January 10, 2022. On the output screen OS, the recommended filing date for China is displayed together with the recommended filing date for Japan. According to such a configuration, the user can collectively check the recommended filing dates of each country.”; p. 10 – “In the first embodiment and the second embodiment described above, the recommended filing date or the predicted filing date of publication of each country is calculated for an application relating to one design.”; pp. 2-3 – “The control unit 12 is configured to calculate the recommended filing date of the design. The control unit 12 has an input reception unit 121 , a required number of days identification unit 122 , a recommended filing date calculation unit 123 , and an output unit 124 . The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20 . Page 2 Page 2 The required number of days specifying unit 122 refers to the main database 111 and specifies the required number of days in the country where the application is scheduled to be filed acquired by the input receiving unit 121 . The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122. . As the recommended filing date, the filing date is calculated so that the application will be filed before the publication of the product pertaining to the design and will be published after the publication of the product pertaining to the design. Specifically, the recommended filing date calculation unit 123 calculates the recommended filing date by subtracting the required number of days from the planned product release date of the design (going back by the required number of days). Information on the recommended filing date is output from the output unit 124 to the user's terminal 30 . The output device 32 of the user's terminal 30 displays the recommended filing date as an image, for example.”);
(b) the back-end application computer server, coupled to the workflow data store (p. 3 – “The control unit 12 can be realized by a processor operating in cooperation with a memory. Examples of processors include CPU, MPU, and GPU. Examples of memory include RAM and ROM. The processor designates at least a part of the computer-readable instructions stored in the ROM, develops them on the RAM, and cooperates with the RAM to execute a predetermined process, whereby the input reception unit 121 and the required number of days identification unit 122 , recommended filing date calculation unit 123, and other functions. ROM is an example of nontransitory storage means for storing computer readable instructions by a processor.”; p. 10 – “In the first embodiment described above, the recommended filing date providing system 10 is connected to the user's terminal 30 via the network 20.”), including:
a computer processor (p. 3 – “The control unit 12 can be realized by a processor operating in cooperation with a memory. Examples of processors include CPU, MPU, and GPU. Examples of memory include RAM and ROM. The processor designates at least a part of the computer-readable instructions stored in the ROM, develops them on the RAM, and cooperates with the RAM to execute a predetermined process, whereby the input reception unit 121 and the required number of days identification unit 122 , recommended filing date calculation unit 123, and other functions. ROM is an example of nontransitory storage means for storing computer readable instructions by a processor.”; p. 10 – “In the first embodiment described above, the recommended filing date providing system 10 is connected to the user's terminal 30 via the network 20.”);
a computer memory coupled to the computer processor and storing instructions that, when executed by the computer processor, cause the back-end application computer server (p. 3 – “The control unit 12 can be realized by a processor operating in cooperation with a memory. Examples of processors include CPU, MPU, and GPU. Examples of memory include RAM and ROM. The processor designates at least a part of the computer-readable instructions stored in the ROM, develops them on the RAM, and cooperates with the RAM to execute a predetermined process, whereby the input reception unit 121 and the required number of days identification unit 122 , recommended filing date calculation unit 123, and other functions. ROM is an example of nontransitory storage means for storing computer readable instructions by a processor.”) to:
receive a request to generate a schedule for execution of a workflow associated with at least one product (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”);
retrieve one or more workflow parameters and two or more location parameters for two or more locations, wherein the workflow parameters include a same final date for the two or more locations (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”; p. 4 – “Further, in the present embodiment, recommended filing dates may be calculated for each of a plurality of countries where filing is planned. Specifically, the input reception unit 121 acquires information on a plurality of countries where applications are planned from the terminal 30 of the user. For example, as shown in FIG. 6, the user inputs a plurality of planned filing countries in the second input area I2. The required number of days specifying unit 122 refers to the main database and specifies the required number of days in each of the plurality of countries in which the application is scheduled to be filed obtained by the input receiving unit 121 The recommended filing date calculation unit 123 calculates the recommended filing date based on the planned product publication date of the design and the required number of days in each of the plurality of planned filing countries.” As seen at the bottom of page 3, the planned product publication date is entered as January 10, 2022 and the recommended filing date in Japan is calculated based on the January 10, 2022 date. The top of page 5 describes a multi-country example. Using the same planned product publication date of January 10, 2022, a recommended filing date is also recommended for a Chinese application. In other words, two or more locations may be identified as two or more countries and a schedule is established for each respective location/country in light of a same final date, the final date being the planned product publication date.);
input the retrieved one or more workflow parameters and one or more location parameters into a scheduler (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”);
automatically generate a workflow schedule via the scheduler based on the retrieved one or more workflow parameters and location parameters (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”); and
(c) a communication port coupled to the back-end application computer server to facilitate an exchange of data with a remote device to support interactive user interface displays that provide information about the schedule (p. 10 – “In the first embodiment described above, the recommended filing date providing system 10 is connected to the user's terminal 30 via the network 20.”; p. 3 – “The output device 32 of the user's terminal 30 displays the recommended filing date as an image, for example.”; p. 1 – “The input acceptance unit acquires a scheduled product laying-open date of the design and a scheduled application country of the design.”).
Seki does not explicitly import the generated workflow schedule to a resource management tool. However, Bissonette discloses the use of a software-based product development portfolio management system and method that may be implemented using software as a service (SaaS) (Bissonette: abstract) and Bissonette allows for multiple project activities to be planned and managed separately and then rolled up as sub-schedules to be integrated into a master schedule for validation of task status (Bissonette: ¶¶ 82, 97). Seki seeks to manage product releases and the filing of corresponding applications in multiple countries based on each country’s respective guidelines (Seki: pp. 2-3). The coordination of the product releases and filing of applications in each of multiple countries is an example of a workflow of scheduled tasks. Seki does not explicitly import a generated workflow schedule to a project plan, like Bissonette does. While not explicitly stated, Seki describes procedures that are related to seeking intellectual property rights. Iida shows that the type of tasks scheduled by Seki may be part of a larger product development management function that manages multiple product development schedules, intellectual property procedure schedules, etc. For example, Iida explains:
The schedule management function is a function for managing product development schedules, intellectual property procedure schedules, and the like. For example, the date and time when the product development plan was submitted (registered), the date and time when the prototype was planned to be developed, the date and time when the prototype was actually created, the date and time when the prototype was scheduled to be released, etc. In other words, the scheduled work date and time for each process The date and time when each step is actually completed is stored in the schedule recording section 65 of the information recording section 56 and managed. In addition, for example, the date of meeting with a patent attorney for patent application and design registration application, the date of receipt of the application manuscript, the date of patent application, the date of publication of the patent application, the date of application for design registration, and the scheduled date of publication of the registration gazette It is saved in the recording unit 65 and managed. (Iida: p. 9)
Iida demonstrates that Seki’s planned workflow tasks can easily and conveniently fit into a larger master plan and corresponding master schedule that manages an entire coordinated product development and intellectual property procedure. Bissonette shows how a master schedule may import tasks from sub-schedules to better coordinate and monitor overall task performance and Bissonette’s teachings may easily be applied to the coordinated types of tasks disclosed in Seki and in Iida. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki to import the generated workflow schedule to a resource management tool to more easily and effectively track tasks that should be timed properly (such as timing product releases and application filings in multiple countries as well as any updates thereto) while enabling users to more conveniently “create and update valid project plans using integrated management tools and techniques, view near-real-time project data and metrics; enable lean project management; send messages to other users via system alerts and/or e-mails and receive messages/alerts from other SPM System users; input data; establish and change organizational governance guidelines; and approve, conditionally approve or reject decisions” (as suggested in Bissonette: abstract).
Seki does not explicitly disclose:
wherein the workflow parameters include the tasks included in the workflow, a sequence for completing the tasks, and one or more resources for completing each task;
that the workflow schedule is automatically generated via a trained machine learning (ML) algorithm of the scheduler, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm;
in a case it is determined by the resource management tool that the one or more resources are unavailable for each task:
transmit a message to re-generate the workflow schedule, wherein the message includes one or more constraints causing a resource to be unavailable for a task, the retrieved one or more workflow parameters, and one or more location parameters;
re-generate the workflow schedule, wherein the re-generated workflow schedule includes only uncompleted tasks, reducing used network bandwidth;
transmit one of the generated workflow schedule and the re-generated workflow schedule to a downstream system;
automatically, by the downstream system, alter operation of the downstream system based on the re-generated workflow schedule;
perform further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions.
However, Guha discloses:
wherein the workflow parameters include the tasks included in the workflow, a sequence for completing the tasks, and one or more resources for completing each task (Guha: ¶ 16 – “Also, with regard to arrangement and performance of tasks, it can be desirable to be mindful of deadlines to perform individual tasks and an overall task comprised of a group of individual tasks (e.g., sub-tasks), and health conditions associated with persons who may be performing the tasks. Furthermore, when multiple people are involved in a task, such as a group meeting or collaborative performance of a task (e.g., an overall or large task comprising various sub-tasks to be performed by different users of the group or team), this can create additional constraints as, for example, different abilities of different users to perform various types of sub-tasks have to be accommodated, different schedules and availabilities of different users to work on sub-tasks have to be accommodated, dependency of the performance of one sub-task on the performance of another sub-task have to be accommodated, and/or sequencing of sub-tasks have to be managed, and/or there can be other constraints relating to the task.”);
that the workflow schedule is automatically generated via a trained machine learning (ML) algorithm of the scheduler, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm (Guha: ¶ 35 – “In some embodiments, the TMC 128 can recommend or suggest that a user (e.g., user 108) refine a task list of the user (e.g., by adding tasks to or deleting tasks from the task list) based at least in part on historical task completions and productivities of the user (e.g., user 108) and/or other users (e.g., users 110 and/or 112) across various tasks.”; ¶ 47 – “The TMC 128 can continue to monitor the performance of tasks and/or other activities by the user 108 and/or the other users (e.g., users 110 and/or 112, and/or other users) and feedback relating to the users and/or tasks, and can collect (e.g., via the communicator component 202) additional task-related information, feedback information (e.g., biometric feedback information, verbal or written feedback information, and/or other feedback information), and/or other desired information relating to the users and/or tasks. The task organizer component 204 and/or the AI component 206 can perform an analysis (e.g., AI-based analysis) on the additional task-related information, feedback information, and/or other desired information, and/or the previous (e.g., historical) task-related information, feedback information, and/or other desired information relating to the users (e.g., users 108, 110, and/or 112, and/or other users) and/or tasks.”; ¶ 48 – “In some embodiments, based at least in part on such information analysis, the TMC 128 (e.g., the task organizer component 204 and/or AI component 206) can learn, determine, or infer that there is a correlation or causation between the user 108 performing, or at least trying to perform, tasks that day and the higher than normal amount of stress or fatigue the user 108 is experiencing.”; ¶ 55 – “Additionally or alternatively, the task organizer component 204 can determine an amount of time to allocate for the user 108 to perform such particular task based at least in part on the higher level of expertise of the user 108, historical amounts of time for performance of the task by the user 108 or other users (e.g., taking into account their respective levels of expertise with respect to performing the particular task or a similar task), or a predicted amount of time (e.g., as predicted based on the AI-based analysis) that it will take for the user 108 to perform the particular task. The task organizer component 204 can adjust (e.g., adaptively adjust) the scheduling of tasks for the user 108, including adjusting attributes associated with the tasks, to include the particular task in the group of tasks the user is to perform and/or to allocate the determined amount of time for performance of the particular task by the user 108.”; ¶ 65 – “In certain embodiments, the task organizer component 204 can determine whether a task can and/or should be divided (e.g., split or partitioned) up into multiple sub-tasks, and, if so, can determine the sub-tasks of the task, based at least in part on the results of analyzing information relating to the task, including type of task, attributes associated with the task, historical information relating to performance of the task by users, and/or other task-related information. Also, in a case where a task is being divided up into sub-tasks, the task organizer component 204 also can determine whether it can be more desirable (e.g., more suitable, more favorable, or optimal) or at least appropriate for the sub-tasks of the task to be performed by the same user (e.g., user 108) or whether it can be more desirable or at least appropriate for respective sub-tasks of the task to be performed by respective users (e.g., users 108, 110, and/or 112), based at least in part on the analysis results, in accordance with the defined task management criteria.”; ¶ 80 – “Based at least in part on the results of the analysis, the AI component 206 can determine, train, and generate a model that can relate to tasks and users, wherein the model can model or be representative of historical performance of tasks by users, characteristics (e.g., education, work experience or skill relating to tasks, age, personality, employment role or position, demographic, or other characteristics) associated with users, attributes associated with tasks, levels of user expertise associated with tasks, environments associated with users or tasks, and/or other features relating to the users (e.g., users 108, 110, and/or 112) and tasks to be performed by users, such as described herein. The AI component 206 can update (e.g., modify, adjust, or change), and further train and enhance, the model as additional data (e.g., task-related information, user feedback information, sensor information, biometric information, environmental information, or other information) associated with users or tasks is received and analyzed by the AI component 206. In some embodiments, as part of the data analysis, and the determining and training of the model, the AI component 206 can employ (and/or train) Markov chains, a neural network(s), or other AI-based or ML-based modeling, techniques, functions, or algorithms.”);
in a case it is determined by the resource management tool that the one or more resources are unavailable for each task (Guha: ¶ 48 – “Based at least in part on the results of this subsequent analysis of additional information and/or previous information, the task organizer component 204 can determine that the user 108 is experiencing a higher than normal amount of stress or fatigue during a particular work afternoon (e.g., based at least in part on biometric feedback information, such as blood pressure, heart rate, focus of attention, or other biometric feedback information, associated with the user 108), can determine that the user has been performing the assigned tasks in accordance with the task schedule and the respective priority levels of the respective tasks (although the user 108 recently has been slowing down on performance of tasks due in part to the stress or fatigue), and can further determine that the remaining tasks the user 108 has yet to perform can still be performed by the user 108 by their respective due dates (e.g., hard deadline due dates or other type of due dates) and in accordance with the respective priority levels of the respective remaining tasks even if the user 108 were to end the work day an hour early that afternoon. In some embodiments, based at least in part on such information analysis, the TMC 128 (e.g., the task organizer component 204 and/or AI component 206) can learn, determine, or infer that there is a correlation or causation between the user 108 performing, or at least trying to perform, tasks that day and the higher than normal amount of stress or fatigue the user 108 is experiencing.”; ¶ 56 – “As an alternative example scenario relating to expertise, if the user 108 is unavailable or unable to accommodate having to perform the particular task, or if it is otherwise desirable (e.g., desirable to give another user more experience in performing such task) to assign the particular task to another user (e.g., user 110), the task organizer component 204 can assign the particular task to the other user 110, and can allocate a desirable (e.g., suitable or sufficient) amount of time for the user 110 to perform the particular task (e.g., an amount of time that can be longer than the amount of time that would have been allocated to user 108, since user 110 has a relatively lower expertise level than the user 108 with respect to that particular task). In some embodiments, based at least in part on analysis (e.g., which can comprise an AI-based analysis) of the information relating to the users and the task, the task organizer component 204 can learn, determine, or infer a difference(s) between the level of expertise associated with the user 110 and the level(s) of expertise associated with the user 108 (and/or another user(s)) with respect to the task. The task organizer component 204 can adaptively adjust one or more attributes (e.g., amount of time allocated to perform the task, instructions for performing the task, or other attribute) associated with the task based at least in part on the difference(s) between the level of expertise of the user 110 and the level of expertise(s) of the user 108 (and/or another user(s)) and/or based at least in part on other performance metrics with respect to the task, in accordance with the defined task management criteria.”):
transmit a message to re-generate the workflow schedule, wherein the message includes one or more constraints causing a resource to be unavailable for a task, the retrieved one or more workflow parameters, and one or more location parameters (Guha: ¶ 37 – “The task-related information can comprise information regarding or relating to, for example, a type of task, sub-tasks of a task, a level of user expertise desired with regard to a task, a location(s) where the task is to be performed, a due date(s) for performance and/or completion of the task or its sub-task(s), instructions for performance of a task, equipment desired to be utilized to perform a task, and/or other desired task-related information.”);
re-generate the workflow schedule, wherein the re-generated workflow schedule includes only uncompleted tasks, reducing used network bandwidth (Guha: ¶ 69 – “As tasks are completed by users, progress is made on tasks by users, new tasks are added, priority levels associated with tasks are set or modified, task completion due dates associated with tasks are set or modified, tasks are reassigned from one user to another user, and/or other changes are made with respect to tasks or users, as determined by the TMC 128, the schedule component 214 can update (e.g., modify, adjust, or change) the respective schedules of respective tasks to be performed by the respective users and/or the other desired information relating to the tasks, users, events, or other matters to reflect or account for (e.g., in accordance with) the completion of tasks by users, the progress made on tasks by users, the addition of new tasks, the setting or modifying of priority levels associated with tasks, the setting or modifying of task completion due dates associated with tasks, the reassignment of tasks from one user to another user, and/or the other changes made with respect to tasks or users. For instance, with regard to progress made on a task by a user (e.g., user 108), the task organizer component 204 can monitor the amount of progress the user 108 is making on a task based at least in part on the results of analyzing information relating to progress being made on that task. As the amount of progress on the task is determined to increase, the task organizer component 204 can communicate task progress information relating to the progress being made on the task by the user 108 to the schedule component 214 and/or notification component 208. In response to receiving the task progress information, the schedule component 214 can update (e.g., modify) information in the task schedule of the user 108 to indicate the amount (e.g., updated or increased) of progress made towards completion of the task, and/or the notification component 208 can present notification information, which can indicate the amount of progress made towards completion of the task, to the user 108 (e.g., via an interface(s) of the TMC 128, communication device 102, or VA 116), and/or to another entity (e.g., a supervisor, manager, or other entity associated with the user 108 or the enterprise).”; ¶ 56 – “As an alternative example scenario relating to expertise, if the user 108 is unavailable or unable to accommodate having to perform the particular task, or if it is otherwise desirable (e.g., desirable to give another user more experience in performing such task) to assign the particular task to another user (e.g., user 110), the task organizer component 204 can assign the particular task to the other user 110, and can allocate a desirable (e.g., suitable or sufficient) amount of time for the user 110 to perform the particular task (e.g., an amount of time that can be longer than the amount of time that would have been allocated to user 108, since user 110 has a relatively lower expertise level than the user 108 with respect to that particular task). In some embodiments, based at least in part on analysis (e.g., which can comprise an AI-based analysis) of the information relating to the users and the task, the task organizer component 204 can learn, determine, or infer a difference(s) between the level of expertise associated with the user 110 and the level(s) of expertise associated with the user 108 (and/or another user(s)) with respect to the task. The task organizer component 204 can adaptively adjust one or more attributes (e.g., amount of time allocated to perform the task, instructions for performing the task, or other attribute) associated with the task based at least in part on the difference(s) between the level of expertise of the user 110 and the level of expertise(s) of the user 108 (and/or another user(s)) and/or based at least in part on other performance metrics with respect to the task, in accordance with the defined task management criteria.”; ¶ 137 – “For instance, the TMC can allocate additional time for the user to complete the task, and adjust the scheduling of one or more of the other remaining tasks and/or their allocated times for performance and completion, to try to create a suitable revised schedule for completion of the remaining tasks.”; It is noted that “reducing used network bandwidth” is a natural result of processing a comparatively smaller amount of information.);
transmit one of the generated workflow schedule and the re-generated workflow schedule to a downstream system (Guhu: ¶ 19 – “Based at least in part on the results of the analysis, the TMC can adaptively adjust (e.g., modify, alter, or change) respective attributes (e.g., properties, elements, or characteristics) associated with respective tasks associated with respective users, which can thereby result in respective adjusted attributes associated with the respective tasks. Based at least in part on the respective adjusted attributes, the TMC can determine task information relating to the respective tasks (e.g., task and work schedule information), including the respective adjusted attributes associated with the respective tasks. The TMC can present (e.g., communicate or display) the task information to a communication device(s) or VA(s) associated with the user(s) to facilitate performance of the tasks by the user(s).”; ¶ 69 – “As tasks are completed by users, progress is made on tasks by users, new tasks are added, priority levels associated with tasks are set or modified, task completion due dates associated with tasks are set or modified, tasks are reassigned from one user to another user, and/or other changes are made with respect to tasks or users, as determined by the TMC 128, the schedule component 214 can update (e.g., modify, adjust, or change) the respective schedules of respective tasks to be performed by the respective users and/or the other desired information relating to the tasks, users, events, or other matters to reflect or account for (e.g., in accordance with) the completion of tasks by users, the progress made on tasks by users, the addition of new tasks, the setting or modifying of priority levels associated with tasks, the setting or modifying of task completion due dates associated with tasks, the reassignment of tasks from one user to another user, and/or the other changes made with respect to tasks or users. For instance, with regard to progress made on a task by a user (e.g., user 108), the task organizer component 204 can monitor the amount of progress the user 108 is making on a task based at least in part on the results of analyzing information relating to progress being made on that task. As the amount of progress on the task is determined to increase, the task organizer component 204 can communicate task progress information relating to the progress being made on the task by the user 108 to the schedule component 214 and/or notification component 208. In response to receiving the task progress information, the schedule component 214 can update (e.g., modify) information in the task schedule of the user 108 to indicate the amount (e.g., updated or increased) of progress made towards completion of the task, and/or the notification component 208 can present notification information, which can indicate the amount of progress made towards completion of the task, to the user 108 (e.g., via an interface(s) of the TMC 128, communication device 102, or VA 116), and/or to another entity (e.g., a supervisor, manager, or other entity associated with the user 108 or the enterprise).”; ¶ 137 – “For instance, the TMC can allocate additional time for the user to complete the task, and adjust the scheduling of one or more of the other remaining tasks and/or their allocated times for performance and completion, to try to create a suitable revised schedule for completion of the remaining tasks.”);
automatically, by the downstream system, alter operation of the downstream system based on the re-generated workflow schedule (Guhu: ¶ 19 – “Based at least in part on the results of the analysis, the TMC can adaptively adjust (e.g., modify, alter, or change) respective attributes (e.g., properties, elements, or characteristics) associated with respective tasks associated with respective users, which can thereby result in respective adjusted attributes associated with the respective tasks. Based at least in part on the respective adjusted attributes, the TMC can determine task information relating to the respective tasks (e.g., task and work schedule information), including the respective adjusted attributes associated with the respective tasks. The TMC can present (e.g., communicate or display) the task information to a communication device(s) or VA(s) associated with the user(s) to facilitate performance of the tasks by the user(s).”; ¶ 69 – “As tasks are completed by users, progress is made on tasks by users, new tasks are added, priority levels associated with tasks are set or modified, task completion due dates associated with tasks are set or modified, tasks are reassigned from one user to another user, and/or other changes are made with respect to tasks or users, as determined by the TMC 128, the schedule component 214 can update (e.g., modify, adjust, or change) the respective schedules of respective tasks to be performed by the respective users and/or the other desired information relating to the tasks, users, events, or other matters to reflect or account for (e.g., in accordance with) the completion of tasks by users, the progress made on tasks by users, the addition of new tasks, the setting or modifying of priority levels associated with tasks, the setting or modifying of task completion due dates associated with tasks, the reassignment of tasks from one user to another user, and/or the other changes made with respect to tasks or users. For instance, with regard to progress made on a task by a user (e.g., user 108), the task organizer component 204 can monitor the amount of progress the user 108 is making on a task based at least in part on the results of analyzing information relating to progress being made on that task. As the amount of progress on the task is determined to increase, the task organizer component 204 can communicate task progress information relating to the progress being made on the task by the user 108 to the schedule component 214 and/or notification component 208. In response to receiving the task progress information, the schedule component 214 can update (e.g., modify) information in the task schedule of the user 108 to indicate the amount (e.g., updated or increased) of progress made towards completion of the task, and/or the notification component 208 can present notification information, which can indicate the amount of progress made towards completion of the task, to the user 108 (e.g., via an interface(s) of the TMC 128, communication device 102, or VA 116), and/or to another entity (e.g., a supervisor, manager, or other entity associated with the user 108 or the enterprise).”; ¶ 137 – “For instance, the TMC can allocate additional time for the user to complete the task, and adjust the scheduling of one or more of the other remaining tasks and/or their allocated times for performance and completion, to try to create a suitable revised schedule for completion of the remaining tasks.”; claim 14 – “The system of claim 13, wherein the group of tasks comprises the task, and wherein the adaptively modifying comprises: adaptively re-arranging a first order of performance of remaining tasks of the group of tasks to generate a second order of performance of the remaining tasks; adaptively modifying respective scheduling of the performance of the remaining tasks; adaptively modifying a priority level associated with the task; adaptively modifying an amount of time allocated to perform the task; adaptively modifying instructions relating to performance of the task; adaptively modifying a determination relating to an amount of progress that has been made towards completion of the task or the group of tasks; adaptively modifying a reminder, notification, or motivation message associated with the task; adaptively modifying calendar data in an electronic calendar, wherein the calendar data is associated with the task; or adaptively modifying reward data relating to a reward that is able to be presented in connection with completion of the task or the group of tasks.”);
perform further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions (Guha: ¶ 47 -- The TMC 128 can continue to monitor the performance of tasks and/or other activities by the user 108 and/or the other users (e.g., users 110 and/or 112, and/or other users) and feedback relating to the users and/or tasks, and can collect (e.g., via the communicator component 202) additional task-related information, feedback information (e.g., biometric feedback information, verbal or written feedback information, and/or other feedback information), and/or other desired information relating to the users and/or tasks. The task organizer component 204 and/or the AI component 206 can perform an analysis (e.g., AI-based analysis) on the additional task-related information, feedback information, and/or other desired information, and/or the previous (e.g., historical) task-related information, feedback information, and/or other desired information relating to the users (e.g., users 108, 110, and/or 112, and/or other users) and/or tasks.”; ¶ 80 – “Based at least in part on the results of the analysis, the AI component 206 can determine, train, and generate a model that can relate to tasks and users, wherein the model can model or be representative of historical performance of tasks by users, characteristics (e.g., education, work experience or skill relating to tasks, age, personality, employment role or position, demographic, or other characteristics) associated with users, attributes associated with tasks, levels of user expertise associated with tasks, environments associated with users or tasks, and/or other features relating to the users (e.g., users 108, 110, and/or 112) and tasks to be performed by users, such as described herein. The AI component 206 can update (e.g., modify, adjust, or change), and further train and enhance, the model as additional data (e.g., task-related information, user feedback information, sensor information, biometric information, environmental information, or other information) associated with users or tasks is received and analyzed by the AI component 206. In some embodiments, as part of the data analysis, and the determining and training of the model, the AI component 206 can employ (and/or train) Markov chains, a neural network(s), or other AI-based or ML-based modeling, techniques, functions, or algorithms.”).
The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki to incorporate the following:
wherein the workflow parameters include the tasks included in the workflow, a sequence for completing the tasks, and one or more resources for completing each task;
that the workflow schedule is automatically generated via a trained machine learning (ML) algorithm of the scheduler, wherein the ML algorithm is trained with historical data and generates the workflow by applying data for a current simulation to the trained ML algorithm;
in a case it is determined by the resource management tool that the one or more resources are unavailable for each task:
transmit a message to re-generate the workflow schedule, wherein the message includes one or more constraints causing a resource to be unavailable for a task, the retrieved one or more workflow parameters, and one or more location parameters;
re-generate the workflow schedule, wherein the re-generated workflow schedule includes only uncompleted tasks, reducing used network bandwidth;
transmit one of the generated workflow schedule and the re-generated workflow schedule to a downstream system;
automatically, by the downstream system, alter operation of the downstream system based on the re-generated workflow schedule;
perform further training of the ML algorithm as results become known from processing generated workflow schedules such that the ML algorithm adapts itself to changing conditions
in order to provide greater scheduling flexibility so that users may be given a reasonable amount of time to perform tasks and to rest in between tasks while meeting task completion due dates (as suggested in the abstract of Guha).
[Claim 2] Seki discloses wherein the workflow schedule includes a date of execution for each task (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”).
[Claim 3] Seki discloses wherein the request includes a release date associated with the at least one product (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”).
[Claim 4] Seki discloses wherein the two or more location parameters are linked to a location and define one or more location-specific rules, and a date of execution for at least one task is based on the one or more location-specific rules (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”; p. 4 – “Further, in the present embodiment, recommended filing dates may be calculated for each of a plurality of countries where filing is planned. Specifically, the input reception unit 121 acquires information on a plurality of countries where applications are planned from the terminal 30 of the user. For example, as shown in FIG. 6, the user inputs a plurality of planned filing countries in the second input area I2. The required number of days specifying unit 122 refers to the main database and specifies the required number of days in each of the plurality of countries in which the application is scheduled to be filed obtained by the input receiving unit 121 The recommended filing date calculation unit 123 calculates the recommended filing date based on the planned product publication date of the design and the required number of days in each of the plurality of planned filing countries.” As seen at the bottom of page 3, the planned product publication date is entered as January 10, 2022 and the recommended filing date in Japan is calculated based on the January 10, 2022 date. The top of page 5 describes a multi-country example. Using the same planned product publication date of January 10, 2022, a recommended filing date is also recommended for a Chinese application. In other words, two or more locations may be identified as two or more countries and a schedule is established for each respective location/country in light of a same final date, the final date being the planned product publication date.).
[Claim 5] Seki discloses wherein the executable task is at least one of: researching regulations, defining requirements, executing a pricing engine, and filing the product with an approval organization (p. 2 – “The input reception unit 121 acquires information on the planned product release date of the design and the planned application country of the design. The scheduled product release date of the design is the scheduled date on which the product to which the design is applied will be disclosed to unspecified persons through advertising or sales. For example, the user uses the input device 31 of the terminal 30 to specify the planned product release date of the design and the planned application country of the design. The input reception unit 121 acquires information on the planned product publication date of the design and the planned application country of the design from the user's terminal 30 via the network 20.”; p. 3 – “The recommended filing date calculation unit 123 calculates the recommended filing date for the design in the country in which the application is scheduled based on the planned product release date of the design acquired by the input receiving unit 121 and the required number of days specified by the required number of days specifying unit 122.”).
[Claim 6] Seki does not explicitly disclose wherein the workflow is associated with one of: an update of the product and an extension of the product. Iida discloses that a product release date may be updated and then used to update filing schedules accordingly (p. 19 – “Next, the management server 14 (control unit 50) updates the product release date (scheduled release date) recorded in the schedule recording unit 65 to the new scheduled release date (step S38). This makes it possible to notify the client 12 used by patent personnel, inventors, design personnel, or design creators of the advanced schedule. In this case, if the filing schedules for patent applications and design registration applications are brought forward, it is preferable that the filing schedules in the schedule recording section 65 are updated when the filing procedures are completed.”). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki wherein the workflow is associated with one of: an update of the product and an extension of the product so that appropriate filing date recommendations may be made and the appropriate users are notified when changes to a product release are warranted (as suggested on p. 19 of Iida), thereby making Seki’s invention more flexible in handling updates and changes.
[Claim 7] Seki does not explicitly disclose wherein the product is at least one of an automobile product and a home product. Iida discloses that the product may be tires (i.e., an automobile product). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki wherein the product is at least one of an automobile product and a home product so that Seki’s invention may be adaptable to all sorts of products, thereby making it more marketable to a wider range of customers. Additionally, automobile products and home products have long been so ubiquitous in the realm of developed products that the substitution of at least one of an automobile product and a home product for Seki’s products would have been well within the technical capability of those skilled in the art prior to Applicant’s invention and such a product substitution would have yielded predictable and expected results.
[Claim 8] Seki discloses wherein the location is at least one state of the United States of America (p. 9 – “For example, on the output screen OS, the predicted application publication dates for the first country of filing (Japan) and the second scheduled filing country (China, the United States) are each displayed as a bar graph extending from the filing date or scheduled filing date to the predicted application publication date.” The United States includes all, i.e., at least one, state of the United States of America.).
[Claim 9] Seki does not explicitly disclose instructions that cause the back-end computer server to:
receive a modification to a first task in the workflow of the request;
re-generate the workflow schedule in response to the received modification; and
import the re-generated workflow schedule to the resource management tool.
Iida discloses that a product release date may be updated and then used to update filing schedules accordingly (p. 19 – “Next, the management server 14 (control unit 50) updates the product release date (scheduled release date) recorded in the schedule recording unit 65 to the new scheduled release date (step S38). This makes it possible to notify the client 12 used by patent personnel, inventors, design personnel, or design creators of the advanced schedule. In this case, if the filing schedules for patent applications and design registration applications are brought forward, it is preferable that the filing schedules in the schedule recording section 65 are updated when the filing procedures are completed.”). The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki to incorporate instructions that cause the back-end computer server to:
receive a modification to a first task in the workflow of the request; and
re-generate the workflow schedule in response to the received modification
so that appropriate filing date recommendations may be made and the appropriate users are notified when changes to a product release are warranted (as suggested on p. 19 of Iida), thereby making Seki’s invention more flexible in handling updates and changes.
Regarding the ability to import the re-generated workflow schedule to the resource management tool, Bissonette discloses the use of a software-based product development portfolio management system and method that may be implemented using software as a service (SaaS) (Bissonette: abstract) and Bissonette allows for multiple project activities to be planned and managed separately and then rolled up as sub-schedules to be integrated into a master schedule for validation of task status (Bissonette: ¶¶ 82, 97). Seki seeks to manage product releases and the filing of corresponding applications in multiple countries based on each country’s respective guidelines (Seki: pp. 2-3). The coordination of the product releases and filing of applications in each of multiple countries is an example of a workflow of scheduled tasks. Seki does not explicitly import a generated workflow schedule to a project plan, like Bissonette does. While not explicitly stated, Seki describes procedures that are related to seeking intellectual property rights. Iida shows that the type of tasks scheduled by Seki may be part of a larger product development management function that manages multiple product development schedules, intellectual property procedure schedules, etc. For example, Iida explains:
The schedule management function is a function for managing product development schedules, intellectual property procedure schedules, and the like. For example, the date and time when the product development plan was submitted (registered), the date and time when the prototype was planned to be developed, the date and time when the prototype was actually created, the date and time when the prototype was scheduled to be released, etc. In other words, the scheduled work date and time for each process The date and time when each step is actually completed is stored in the schedule recording section 65 of the information recording section 56 and managed. In addition, for example, the date of meeting with a patent attorney for patent application and design registration application, the date of receipt of the application manuscript, the date of patent application, the date of publication of the patent application, the date of application for design registration, and the scheduled date of publication of the registration gazette It is saved in the recording unit 65 and managed. (Iida: p. 9)
Iida demonstrates that Seki’s planned workflow tasks can easily and conveniently fit into a larger master plan and corresponding master schedule that manages an entire coordinated product development and intellectual property procedure. Bissonette shows how a master schedule may import tasks from sub-schedules to better coordinate and monitor overall task performance and Bissonette’s teachings may easily be applied to the coordinated types of tasks disclosed in Seki and in Iida. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki to incorporate instructions that cause the back-end computer server to import the re-generated workflow schedule to the resource management tool to more easily and effectively track tasks that should be timed properly (such as timing product releases and application filings in multiple countries as well as any updates thereto) while enabling users to more conveniently “create and update valid project plans using integrated management tools and techniques, view near-real-time project data and metrics; enable lean project management; send messages to other users via system alerts and/or e-mails and receive messages/alerts from other SPM System users; input data; establish and change organizational governance guidelines; and approve, conditionally approve or reject decisions” (as suggested in Bissonette: abstract).
[Claim 10] Seki does not explicitly disclose instructions that cause the back-end application computer to:
identify, via the resource management tool, one or more resources adapted to execute the workflow schedule.
Bissonette discloses the use of a software-based product development portfolio management system and method that may be implemented using software as a service (SaaS) (Bissonette: abstract) and Bissonette allows for multiple project activities to be planned and managed separately and then rolled up as sub-schedules to be integrated into a master schedule for validation of task status (Bissonette: ¶¶ 82, 97). Seki seeks to manage product releases and the filing of corresponding applications in multiple countries based on each country’s respective guidelines (Seki: pp. 2-3). The coordination of the product releases and filing of applications in each of multiple countries is an example of a workflow of scheduled tasks. Seki does not explicitly import a generated workflow schedule to a project plan, like Bissonette does. While not explicitly stated, Seki describes procedures that are related to seeking intellectual property rights. Iida shows that the type of tasks scheduled by Seki may be part of a larger product development management function that manages multiple product development schedules, intellectual property procedure schedules, etc. For example, Iida explains:
The schedule management function is a function for managing product development schedules, intellectual property procedure schedules, and the like. For example, the date and time when the product development plan was submitted (registered), the date and time when the prototype was planned to be developed, the date and time when the prototype was actually created, the date and time when the prototype was scheduled to be released, etc. In other words, the scheduled work date and time for each process The date and time when each step is actually completed is stored in the schedule recording section 65 of the information recording section 56 and managed. In addition, for example, the date of meeting with a patent attorney for patent application and design registration application, the date of receipt of the application manuscript, the date of patent application, the date of publication of the patent application, the date of application for design registration, and the scheduled date of publication of the registration gazette It is saved in the recording unit 65 and managed. (Iida: p. 9)
Iida demonstrates that Seki’s planned workflow tasks can easily and conveniently fit into a larger master plan and corresponding master schedule that manages an entire coordinated product development and intellectual property procedure. Iida also notifies the appropriate person in a charge of related operations of any changes affecting their respective operations, as seen in the following excerpt:
In addition, when a product design change is input, or when the product design is updated, the intellectual property management apparatus 10 preferably notifies relevant departments and persons in charge. In this way, when a design change is detected, a notification is sent, so that even if the operator forgets to enter the business consultation, or if there is an omission of communication, the relevant parties can quickly change the design. In line with the change, it is possible to change the production plan, change the plan of the intellectual property acquisition work, for example, increase the number of patent applications, etc., and respond more quickly. Specifically, by notifying the person in charge, the person in charge can notice the design change, compare the product with the design change and the prior art, and the product with the design change can be identified. It is possible to quickly determine whether or not a product infringes another's patent right. As a result, it is possible to suppress the occurrence of a delay in response. In addition, by notifying the design change, the person in charge of intellectual property rights can consider the patent application and design registration application for the technology related to the design change. As a result, even if a developer forgets to file an application, the person in charge of intellectual property rights can perform work related to the application work and provide business consultation, thereby preventing the developer from forgetting to file the application. (Iida: p. 13)
Bissonette shows how a master schedule may import tasks from sub-schedules to better coordinate and monitor overall task performance and Bissonette’s teachings may easily be applied to the coordinated types of tasks disclosed in Seki and in Iida. The Examiner submits that it would have been obvious to one of ordinary skill in the art before the effective filing date of Applicant’s invention to modify Seki to incorporate instructions that cause the back-end application computer to:
identify, via the resource management tool, one or more resources adapted to execute the workflow schedule
to more easily and effectively track tasks that should be timed properly (such as timing product releases and application filings in multiple countries as well as any updates thereto) while enabling users to more conveniently “create and update valid project plans using integrated management tools and techniques, view near-real-time project data and metrics; enable lean project management; send messages to other users via system alerts and/or e-mails and receive messages/alerts from other SPM System users; input data; establish and change organizational governance guidelines; and approve, conditionally approve or reject decisions” (as suggested in Bissonette: abstract).
[Claims 11-18] Claims 11-18 recite limitations already addressed by the rejections of claims 1-7 and 9-10 above; therefore, the same rejections apply. (It is noted that the phrase “in a case it is determined by the resource management tool that one or more resources are unavailable for each task” is a condition that must be met for the subsequent transmitting and re-generating steps to be performed in independent method claim 11 and its dependent claims. A method is defined by positively recited steps. Unless this condition is met, the corresponding steps are not necessarily performed within the scope of the method claims.)
[Claims 19-20] Claims 19-20 recite limitations already addressed by the rejections of claims 1-4 above; therefore, the same rejections apply.
Furthermore, Seki discloses a non-transitory, computer-readable medium storing instructions that, when executed by a processor, cause the processor to perform a method implemented via a back-end application computer server of an enterprise, the method comprising the disclosed functionality (Seki: p. 3 – “An example of the flow of processing executed by the control unit 12 of the recommended filing date providing system 10 configured as described above will be described with reference to FIG.”; p. 3 – “The control unit 12 can be realized by a processor operating in cooperation with a memory. Examples of processors include CPU, MPU, and GPU. Examples of memory include RAM and ROM. The processor designates at least a part of the computer-readable instructions stored in the ROM, develops them on the RAM, and cooperates with the RAM to execute a predetermined process, whereby the input reception unit 121 and the required number of days identification unit 122 , recommended filing date calculation unit 123, and other functions. ROM is an example of nontransitory storage means for storing computer readable instructions by a processor.”; p. 10 – “In the first embodiment described above, the recommended filing date providing system 10 is connected to the user's terminal 30 via the network 20.” While Seki does not explicitly disclose that the back-end application computer server is “of an enterprise,” this limitation does not impart significant patentable weight on the claims as a whole and does not serve to patentably distinguish the claims over the prior art since it is limited to the preamble and does not breathe life into the body of the claims. Additionally, the phrase “of the enterprise” does not affect any limiting structural or functional elements of the claims.).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Smirnov et al. (WO 00/38091) – Uses adaptive workflow scheduling to update uncompleted tasks (claim 7).
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SUSANNA M DIAZ whose telephone number is (571)272-6733. The examiner can normally be reached M-F, 8 am-4:30 pm.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Brian Epstein can be reached at (571) 270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SUSANNA M. DIAZ/
Primary Examiner
Art Unit 3625A
Prosecution Insights