DETAILED ACTION
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Notice for all US Patent Applications filed on or after March 16, 2013
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Status of the Claims
This communication is in response to communications received on 9/16/25. Claim(s) none is/are amended, claim(s) none is/are cancelled, claim(s) none is/are new, and applicant does not provide any information on where support for the amendments can be found in the instant specification as there are not any amendments and/or new claims. Therefore, Claims 1-11 is/are pending and have been addressed below.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 9/16/25 was/were considered by the examiner.
Priority
Acknowledgment is made of applicant's claim for foreign priority based on an application(s) JP2023-108268 filed in Japan on 6/30/23. Should applicant desire to obtain the benefit of foreign priority under 35 U.S.C. 119(a)-(d) prior to declaration of an interference, a certified English translation of the foreign application must be submitted in reply to this action. 37 CFR 41.154(b) and 41.202(e).
Failure to provide a certified translation may result in no benefit being accorded for the non-English application.
Response to Arguments
There are no arguments.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action and those claims are 1-10 based on “unit”, see instant specification [0153] for support. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claim(s) 1-11 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claim(s) does/do not fall within at least one of the four categories of patent eligible subject matter as noted below.
The limitation(s) below for representative claim(s) 1 and 11 that, under its broadest reasonable interpretation, is directed to training a large language model.
Step 1: The claim(s) as drafted, is/are a process (claim(s) 11 recites a series of steps) and system (claim(s) 1-10 recites a series of components).
Step 2A – Prong 1: The claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s) (emphasis added):
Claim 1: a project plan optimization tool that outputs information regarding an improvement proposal for a business plan of a project,
wherein the project plan optimization tool is configured by a computer including an arithmetic device that executes arithmetic processing and a storage device that is accessible by the arithmetic device,
the storage device includes a past record information accumulation unit that accumulates a record value parameter and a result evaluation value of each phase in a plurality of past projects, and
the project plan optimization method comprises:
a plan value parameter input step in which a first plan value parameter for each phase obtained by dividing the project into a plurality of phases is input;
a past record information analysis step of analyzing the record value parameters and the result evaluation values of each phase in the past projects in association with each other and generating a prediction model of the project;
a project result prediction procedure of predicting a result evaluation value from the first plan value parameter by using the prediction model; and
a plan value parameter optimization step of re-distributing the first plan value parameter between the phases and outputting an optimized second plan value parameter.
Claim(s) 1: same analysis as claim(s) 11.
Dependent claims 2-10 recite the same or similar abstract idea(s) as independent claim(s) 1 and 11 with merely a further narrowing of the abstract idea(s): .
The identified limitations of the independent and dependent claims above fall well-within the groupings of subject matter identified by the courts as being abstract concepts of:
a method of organizing human activity (commercial or legal interactions including advertising, marketing or sales activities or behaviors, or business relations) because the invention is directed to economic and/or business relationships as they are associated with project optimization.
Step 2A – Prong 2: This judicial exception is not integrated into a practical application because:
The additional elements unencompassed by the abstract idea include computer, arithmetic device, storage device (claim(s) 1, 11), unit (claim(s) 1), unit (claim(s) 2-4, 7, 10), machine learning (claim(s) 2).
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the additional elements as described above with respect to Step 2A Prong 2 fails to describe:
Improvements to the functioning of a computer, or to any other technology or technical field - see MPEP 2106.05(a)
Applying or using a judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition – see Vanda Memo
Applying the judicial exception with, or by use of, a particular machine – see MPEP 2106.05(b)
Effecting a transformation or reduction of a particular article to a different state or thing - see MPEP 2106.05(c)
Applying or using the judicial exception in some other meaningful way beyond generally linking the use of the judicial exception to a particular technological environment, such that the claim as a whole is more than a drafting effort designed to monopolize the exception - see MPEP 2106.05(e) and Vanda Memo.
Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0020, 0029]) invoked as a tool and/or general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application (MPEP 2106.05(f)&(h)).
Step 2B: The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception. Thus the additional elements as described above with respect to Step 2A Prong 2 are merely (as additionally noted by instant specification [0020, 0029]) invoked as a tool and/or a general purpose computer to apply instructions of an abstract idea in a particular technological environment, and/or mere application of an abstract idea in a particular technological environment and merely limiting the use of an abstract idea to a particular technological field do not integrate an abstract idea into a practical application and thus similarly the combination and arrangement of the above identified additional elements when analyzed under Step 2B also fails to necessitate a conclusion that the claims amount to significantly more than the abstract idea for the same reasons as set forth above (MPEP 2106.05(f)&(h)).
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
(a)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention.
Claim(s) 1-2 and 11 is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Ishikawa et al. (US 2023/0059609 A1).
Regarding claim(s) 1 and 11, Ishikawa teaches a project plan optimization method executed by a project plan optimization tool that outputs information regarding an improvement proposal for a business plan of a project,
wherein the project plan optimization tool is configured by a computer including an arithmetic device that executes arithmetic processing and a storage device that is accessible by the arithmetic device [see at least Figs. 26-27 and [0145] “the information processing device 90 includes a processor 91, a main storage device 92, an auxiliary storage device 93, an input/output interface 95, a communication interface 96, and a drive device 97. … FIG. 26 illustrates a recording medium 99 capable of recording data.”],
the storage device includes a past record information accumulation unit that accumulates a record value parameter and a result evaluation value of each phase in a plurality of past projects [see at least Figs. 15 and 17 and [0107-0111] “training data 252 with the requirement information of the past project as an explanatory variable and the check item selected in the past project as an objective variable. For example, the project information of the past project includes requirement information such as order data, contract data, estimate data, participation data, and resource data.”;
[0075] “In FIG. 8 , first, the learning unit 11 acquires project information of a past project and an item used in the past project (step S111).”;
[0098-0099, 0126] “the training data 251 with the requirement information of the past project as an explanatory variable and the work item actually used in the past project as an objective variable.
For example, the requirement information of the past project includes order data, contract data, estimate data, participation data, resource data, and the like. The order data includes information about the purpose and the orderer of the past project. The contract data includes a contract content such as a content of a defect guarantee of a past project. The estimate data includes estimate contents such as a scale and a development period of a past project. The participation data includes information such as participants, participating departments, and external participating companies of the past project. The resource data includes information such as equipment and a license of software used in past projects. Since there is a possibility that the resource data is not determined at the start of the IV&V activity, the resource data may not be included in the requirement information at the stage of the basic design process.”;
[0097, 0099, 0106; additionally 0069, 0117, 0124] further define stage(s) and phase(s) of ([0099]) “Application Example 1 can be applied not only to the start of IV&V activities but also to a development review at the beginning of each phase.”], and
the project plan optimization method comprises:
a plan value parameter input step in which a first plan value parameter for each phase obtained by dividing the project into a plurality of phases is input [see at least [0054] “The project information 100-2 including a plurality of pieces of project data 1 to j related to the target project is input to the prediction unit 12. The prediction unit 12 input the plurality of pieces of project data 1 to j to the plurality of respective prediction models 110-1 to j.”];
a past record information analysis step of analyzing the record value parameters and the result evaluation values of each phase in the past projects in association with each other and generating a prediction model of the project [see at least [0049] “FIG. 2 is a conceptual diagram illustrating an example in which the learning unit 11 generates a prediction model 110. The training data with project information of the past project as an explanatory variable and the item used in the past project as an objective variable is input to the learning unit 11. The learning unit 11 executes machine learning using the input training data and generates the prediction model 110.”];
a project result prediction procedure of predicting a result evaluation value from the first plan value parameter by using the prediction model [see at least Fig. 9 and [0078-0081, 0103, 0112, 0119, 0121] “In FIG. 9 , first, the prediction unit 12 acquires project information of the target project (step S121).
Next, the prediction unit 12 inputs the project information of the target project to the prediction model (step S122).
Next, the prediction unit 12 outputs assistance information including at least one piece of assistance data predicted by the prediction model (step S123).”]; and
a plan value parameter optimization step of re-distributing the first plan value parameter between the phases and outputting an optimized second plan value parameter [see at least Fig. 24 and [0129-0130] “FIG. 24 is a conceptual diagram illustrating an example in which the correction information 224 and information based on a category into which at least one piece of correction data included in the correction information 224 is classified are output as the assistance information. In the example of FIG. 24 , the assistance information generation device 20 generates correction information 224 including the correction data for the failure using the first prediction model 214, inputs the generated correction information 224 to the second prediction model 215, and classifies the correction information into categories. In the example of FIG. 24 , the requirement information of the target project includes order data, contract data, estimate data, participation data, resource data, progress data, correction data, and the like.”;
[0116-0117] “Next, Application Example 3 in which an analysis item lacking in the previous phase is added as assistance information based on project information in the past project and the target project will be described. In Application Example 3, for example, an analysis item having a large contribution degree to an explanatory variable is added as a check item to be added. The examination results of the analysis item to be added are shown in a table (analysis metrics) in which verification targets of the failure such as a design document, a test method procedure (also referred to as a test procedure manual), and a program (source codes) are associated with analysis items for these verification targets.
… In the third Application Example, at the start of analysis in each phase, failure analysis metrics for developing an accurate analysis policy are generated as assistance information in consideration of the occurrence of a failure and the current state of a response.”].
Regarding claim(s) 2, Ishikawa teaches the project plan optimization tool according to claim 1,
wherein the past record information analysis unit
analyzes the record value parameter and the result evaluation value accumulated in the past record information accumulation unit by machine learning for each phase, and
generates the prediction model that has learned a causal relationship between the record value parameter and the result evaluation value [for the limitations above, see at least Figs. 15 and 17 and [0107-0111] “training data 252 with the requirement information of the past project as an explanatory variable and the check item selected in the past project as an objective variable. For example, the project information of the past project includes requirement information such as order data, contract data, estimate data, participation data, and resource data.”;
[0075] “In FIG. 8 , first, the learning unit 11 acquires project information of a past project and an item used in the past project (step S111).”;
[0098-0099, 0126] “the training data 251 with the requirement information of the past project as an explanatory variable and the work item actually used in the past project as an objective variable.
For example, the requirement information of the past project includes order data, contract data, estimate data, participation data, resource data, and the like. The order data includes information about the purpose and the orderer of the past project. The contract data includes a contract content such as a content of a defect guarantee of a past project. The estimate data includes estimate contents such as a scale and a development period of a past project. The participation data includes information such as participants, participating departments, and external participating companies of the past project. The resource data includes information such as equipment and a license of software used in past projects. Since there is a possibility that the resource data is not determined at the start of the IV&V activity, the resource data may not be included in the requirement information at the stage of the basic design process.”;
[0097, 0099, 0106; additionally 0069, 0117, 0124] further define stage(s) and phase(s) of ([0099]) “Application Example 1 can be applied not only to the start of IV&V activities but also to a development review at the beginning of each phase.”].
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.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
It has been held that a prior art reference must either be in the field of applicant’s endeavor or, if not, then be reasonably pertinent to the particular problem with which the applicant was concerned, in order to be relied upon as a basis for rejection of the claimed invention. See In re Oetiker, 977 F.2d 1443, 24 USPQ2d 1443 (Fed. Cir. 1992).
Claim(s) 3-8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa et al. (US 2023/0059609 A1) in view of Crabtree et al. (US 2021/0209505 A1).
Regarding claim(s) 3, Ishikawa teaches the project plan optimization tool according to claim 1, wherein
(original vs citation) the project result prediction unit calculates a plurality of result evaluation values by simulation of a plurality of projects with a plurality of plan value parameters [see at least [0006, 0008] “a system for predicting future outcomes of dynamic and complex systems using simulation results driven by a parametric and blended analytic and modeling approach. A model engine and simulation engine in combination with a visualization engine using such an approach has been developed to produce geospatial and temporal context aware system models for use in generating predictive results which may be used to recommend future outcomes from continuously competing models derived from ingesting large amounts of varied but related data.”], and
the plan value parameter optimization unit selects a result evaluation value close to a goal definition set along a target of the project, and outputs a second plan value parameter corresponding to the selected result evaluation value [see at least [0121] “In the failure analysis metrics 223-1, the likelihood of a failure is indicated by three types of symbols such as ∘, Δ, and x. ∘ indicates that the possibility of occurrence of a failure is low. Δ indicates that caution is required for the failure. x indicates that the possibility of occurrence of a failure is extremely high. The likelihood of failure may be expressed by numerical values instead of symbols. For example, the numerical values may be changed in ascending or descending order according to the likelihood of occurrence of the failure.”;
Fig. 24 and [0129-0130] “FIG. 24 is a conceptual diagram illustrating an example in which the correction information 224 and information based on a category into which at least one piece of correction data included in the correction information 224 is classified are output as the assistance information. In the example of FIG. 24 , the assistance information generation device 20 generates correction information 224 including the correction data for the failure using the first prediction model 214, inputs the generated correction information 224 to the second prediction model 215, and classifies the correction information into categories. In the example of FIG. 24 , the requirement information of the target project includes order data, contract data, estimate data, participation data, resource data, progress data, correction data, and the like.”;
[0116-0117] “Next, Application Example 3 in which an analysis item lacking in the previous phase is added as assistance information based on project information in the past project and the target project will be described. In Application Example 3, for example, an analysis item having a large contribution degree to an explanatory variable is added as a check item to be added. The examination results of the analysis item to be added are shown in a table (analysis metrics) in which verification targets of the failure such as a design document, a test method procedure (also referred to as a test procedure manual), and a program (source codes) are associated with analysis items for these verification targets.
… In the third Application Example, at the start of analysis in each phase, failure analysis metrics for developing an accurate analysis policy are generated as assistance information in consideration of the occurrence of a failure and the current state of a response.”].
Ishikawa doesn’t/don’t explicitly teach however Crabtree discloses
the project result prediction unit calculates a plurality of result evaluation values by simulation of a plurality of projects with a plurality of plan value parameters [see at least [0006, 0008] “a system for predicting future outcomes of dynamic and complex systems using simulation results driven by a parametric and blended analytic and modeling approach. A model engine and simulation engine in combination with a visualization engine using such an approach has been developed to produce geospatial and temporal context aware system models for use in generating predictive results which may be used to recommend future outcomes from continuously competing models derived from ingesting large amounts of varied but related data.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ishikawa with Crabtree to include the limitation(s) above as disclosed by Crabtree. Doing so would improve Ishikawa’s (Ishikawa) project assessment by explicating defining what one or more results are [see at least Crabtree [0003-0006] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Ishikawa and b) Crabtree and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim(s) 4, modified Ishikawa teaches the project plan optimization tool according to claim 3,
and Ishikawa teaches wherein the plan value parameter optimization unit determines that optimization has been completed by the goal definition and selection of a result evaluation value within an error range set in advance, and outputs the second plan value parameter corresponding to the selected result evaluation value [see at least [0125] “For example, in Application Example 4, in the target project, the closest modification item, a modification item within a predetermined range, a modification item classified into the same category, and a representative modification item of the same category (a median value, an average value, or the like) among the modification items of the past project are added to the modification item.”;
[0130] “For example, the second prediction model 215 may output correction data such as a correction history closest to the correction data T1 or a correction history within a predetermined range around the correction data T1.”].
Regarding claim(s) 5, modified Ishikawa teaches the project plan optimization tool according to claim 3,
and Ishikawa teaches wherein the goal definition is defined by a combination of a plurality of conditions, and priorities are designated for the plurality of conditions [see at least [0116-0117] “In Application Example 3, for example, an analysis item having a large contribution degree to an explanatory variable is added as a check item to be added. The examination results of the analysis item to be added are shown in a table (analysis metrics) in which verification targets of the failure such as a design document, a test method procedure (also referred to as a test procedure manual), and a program (source codes) are associated with analysis items for these verification targets.
The failure analysis metrics are a list of the likelihood of a failure that can occur in the design document, the test method procedure, the program, and the like, for each item. For the failure analysis metrics, a value related to the possibility of occurrence of a failure is set.”].
Regarding claim(s) 6, modified Ishikawa teaches the project plan optimization tool according to claim 3, .
Modified Ishikawa doesn’t/don’t explicitly teach however Crabtree discloses
wherein the goal definition is transition of an evaluation value according to a position of each phase on a time axis in the project [see at least [0044] “Structured plan analysis result data … can be used to prioritize both human and machine-oriented tasks to maximize reward functions over finite time horizons 217or through the graph-based data store 145, depending on the specifics of the analysis in complexity and time run.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Ishikawa with Crabtree to include the limitation(s) above as disclosed by Crabtree. Doing so would improve modified Ishikawa’s (Ishikawa) project assessment by explicating defining what one or more results are [see at least Crabtree [0003-0006] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Ishikawa and b) Crabtree and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim(s) 7, Ishikawa teaches the project plan optimization tool according to claim 1,
wherein the plan value parameter optimization unit
(original vs citation) searches for a combination of movements of the plan value parameter between phases, the plan value parameter being for deriving an appropriate result evaluation value, based on a movement rule set in advance for each type of plan value parameter, and
outputs a second plan value parameter based on the searched combination [for the limitations above, see at least Fig. 24 and [0129-0130] “FIG. 24 is a conceptual diagram illustrating an example in which the correction information 224 and information based on a category into which at least one piece of correction data included in the correction information 224 is classified are output as the assistance information. In the example of FIG. 24 , the assistance information generation device 20 generates correction information 224 including the correction data for the failure using the first prediction model 214, inputs the generated correction information 224 to the second prediction model 215, and classifies the correction information into categories. In the example of FIG. 24 , the requirement information of the target project includes order data, contract data, estimate data, participation data, resource data, progress data, correction data, and the like.
… For example, the second prediction model 215 may output correction data such as a correction history closest to the correction data T1 or a correction history within a predetermined range around the correction data T1.”;
[0116-0117] “Next, Application Example 3 in which an analysis item lacking in the previous phase is added as assistance information based on project information in the past project and the target project will be described. In Application Example 3, for example, an analysis item having a large contribution degree to an explanatory variable is added as a check item to be added. The examination results of the analysis item to be added are shown in a table (analysis metrics) in which verification targets of the failure such as a design document, a test method procedure (also referred to as a test procedure manual), and a program (source codes) are associated with analysis items for these verification targets.
… In the third Application Example, at the start of analysis in each phase, failure analysis metrics for developing an accurate analysis policy are generated as assistance information in consideration of the occurrence of a failure and the current state of a response.”].
Ishikawa doesn’t/don’t explicitly teach however Crabtree discloses
(original vs citation) searches for a combination of movements of the plan value parameter between phases, the plan value parameter being for deriving an appropriate result evaluation value, based on a movement rule set in advance for each type of plan value parameter, and
(original vs citation) outputs a second plan value parameter based on the searched combination [for the limitations above, see at least [0044] “Structured plan analysis result data … can be used to prioritize both human and machine-oriented tasks to maximize reward functions over finite time horizons 217or through the graph-based data store 145, depending on the specifics of the analysis in complexity and time run.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Ishikawa with Crabtree to include the limitation(s) above as disclosed by Crabtree. Doing so would improve Ishikawa’s (Ishikawa) project assessment by explicating defining what one or more results are [see at least Crabtree [0003-0006] ].
Furthermore, all of the claimed elements were known in the prior arts of a) Ishikawa and b) Crabtree and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim(s) 8, modified Ishikawa teaches the project plan optimization tool according to claim 7,
and Ishikawa teaches wherein the movement rule is allowed to designate at least one of availability of movement of the plan value parameter, a movable amount of the plan value parameter, a range of a movement destination phase of the plan value parameter, and a numerical range after movement of the plan value parameter [see at least [0125] “For example, in Application Example 4, in the target project, the closest modification item, a modification item within a predetermined range, a modification item classified into the same category, and a representative modification item of the same category (a median value, an average value, or the like) among the modification items of the past project are added to the modification item.”;
[0130] “For example, the second prediction model 215 may output correction data such as a correction history closest to the correction data T1 or a correction history within a predetermined range around the correction data T1.”].
Claim(s) 9-10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Ishikawa in view of Crabtree as applied to claim(s) 7 above and further in view of Handa et al. (KR 2020-0019741 A1).
Regarding claim(s) 9, modified Ishikawa teaches the project plan optimization tool according to claim 7, .
Modified Ishikawa doesn’t/don’t explicitly teach however Handa discloses
wherein the movement rule includes a rule for maintaining a relation between a plurality of plan value parameters having a correlation relationship [see at least [0013] “In this method, the method comprises a first step of preparing a data table to be analyzed, including a plurality of data tables, in a memory device; a second step of generating a data relationship model that indicates the relationship between the plurality of data tables; a third step of interpreting the data table to be analyzed and extracting a plurality of correlation rules that indicate the correlation of attributes included in the data table; and a fourth step of generating a combination of attributes of the preamble and conclusion of each correlation rule, calculating the distance between the attributes in the data relationship model for each combination, and calculating the degree of deviation based on the distance.”;
[0040-0041] ([0040]) “The unexpectedness calculation unit (108) calculates the unexpectedness for each correlation rule extracted by the correlation rule extraction unit (107) by comparing the event included in the preamble and conclusion of the correlation rule with the data relationship model generated by the data relationship model generation unit (105), and stores it in the correlation rule memory unit (103).
The calculated outlier is stored in the outlier column (Fig. 4) of the correlation rule storage table (1030) of the correlation rule memory unit (103).
([0041]) The rule recommendation unit (109) receives a request from the analysis implementer to narrow the range of correlation rules and four total threshold values for support, confidence, lift, and surprise defined by the analysis implementer, and narrows the range of correlation rules by performing threshold processing on all correlation rules stored in the correlation rule memory unit (103), and returns the result of narrowing the range to the user terminal (111).
Threshold processing is to keep rules that have a value higher than the threshold set for each metric and remove rules that have a value lower than the threshold.
A rule is left that has a value higher than the threshold for any of the four indicators: the sum of support, confidence, lift, and surprise.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Ishikawa with Handa to include the limitation(s) above as disclosed by Handa. Doing so would improve modified Ishikawa’s (Ishikawa) project assessment by explicating defining what one or more results are [see at least Handa [0011] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Ishikawa and b) Handa and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Regarding claim(s) 10, modified Ishikawa teaches the project plan optimization tool according to claim 7,
and Ishikawa teaches wherein the plan value parameter optimization unit
(original vs citation) searches for a combination of re-distribution of plan value parameters in a range deviating from the movement rule, and
(original vs citation) derives the second plan value parameter to which a movement rule deviation condition is attached [see at least Fig. 24 and [0129-0130] “FIG. 24 is a conceptual diagram illustrating an example in which the correction information 224 and information based on a category into which at least one piece of correction data included in the correction information 224 is classified are output as the assistance information. In the example of FIG. 24 , the assistance information generation device 20 generates correction information 224 including the correction data for the failure using the first prediction model 214, inputs the generated correction information 224 to the second prediction model 215, and classifies the correction information into categories. In the example of FIG. 24 , the requirement information of the target project includes order data, contract data, estimate data, participation data, resource data, progress data, correction data, and the like.”;
[0116-0117] “Next, Application Example 3 in which an analysis item lacking in the previous phase is added as assistance information based on project information in the past project and the target project will be described. In Application Example 3, for example, an analysis item having a large contribution degree to an explanatory variable is added as a check item to be added. The examination results of the analysis item to be added are shown in a table (analysis metrics) in which verification targets of the failure such as a design document, a test method procedure (also referred to as a test procedure manual), and a program (source codes) are associated with analysis items for these verification targets.
… In the third Application Example, at the start of analysis in each phase, failure analysis metrics for developing an accurate analysis policy are generated as assistance information in consideration of the occurrence of a failure and the current state of a response.”].
Modified Ishikawa doesn’t/don’t explicitly teach however Crabtree discloses
(original vs citation) searches for a combination of re-distribution of plan value parameters in a range deviating from the movement rule [see at least [0044] “Structured plan analysis result data … can be used to prioritize both human and machine-oriented tasks to maximize reward functions over finite time horizons 217or through the graph-based data store 145, depending on the specifics of the analysis in complexity and time run.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Ishikawa with Crabtree to include the limitation(s) above as disclosed by Crabtree. Doing so would improve modified Ishikawa’s (Ishikawa) project assessment by explicating defining what one or more results are [see at least Crabtree [0003-0006] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Ishikawa and b) Crabtree and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Modified Ishikawa doesn’t/don’t explicitly teach however Handa discloses
(original vs citation) searches for a combination of re-distribution of plan value parameters in a range deviating from the movement rule, and
(original vs citation) derives the second plan value parameter to which a movement rule deviation condition is attached [for the limitations above, see at least [0013] “In this method, the method comprises a first step of preparing a data table to be analyzed, including a plurality of data tables, in a memory device; a second step of generating a data relationship model that indicates the relationship between the plurality of data tables; a third step of interpreting the data table to be analyzed and extracting a plurality of correlation rules that indicate the correlation of attributes included in the data table; and a fourth step of generating a combination of attributes of the preamble and conclusion of each correlation rule, calculating the distance between the attributes in the data relationship model for each combination, and calculating the degree of deviation based on the distance.”;
[0040-0041] ([0040]) “The unexpectedness calculation unit (108) calculates the unexpectedness for each correlation rule extracted by the correlation rule extraction unit (107) by comparing the event included in the preamble and conclusion of the correlation rule with the data relationship model generated by the data relationship model generation unit (105), and stores it in the correlation rule memory unit (103).
The calculated outlier is stored in the outlier column (Fig. 4) of the correlation rule storage table (1030) of the correlation rule memory unit (103).
([0041]) The rule recommendation unit (109) receives a request from the analysis implementer to narrow the range of correlation rules and four total threshold values for support, confidence, lift, and surprise defined by the analysis implementer, and narrows the range of correlation rules by performing threshold processing on all correlation rules stored in the correlation rule memory unit (103), and returns the result of narrowing the range to the user terminal (111).
Threshold processing is to keep rules that have a value higher than the threshold set for each metric and remove rules that have a value lower than the threshold.
A rule is left that has a value higher than the threshold for any of the four indicators: the sum of support, confidence, lift, and surprise.”].
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify modified Ishikawa with Handa to include the limitation(s) above as disclosed by Handa. Doing so would improve modified Ishikawa’s (Ishikawa) project assessment by explicating defining what one or more results are [see at least Handa [0011] ].
Furthermore, all of the claimed elements were known in the prior arts of a) modified Ishikawa and b) Handa and c) one skilled in the art could have combined the elements as claimed by known methods with no change in their respective functions, and the combination would have yielded predictable results to one of ordinary skill in the art before the effective filing date of the claimed invention.
Conclusion
When responding to the office action, any new claims and/or limitations should be accompanied by a reference as to where the new claims and/or limitations are supported in the original disclosure.
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Ishikawa et al. – WO 2021/171388 A1 (relevant because it teaches same as US 2023/0059609 A1)
Stanciu et al. – Towards an adaptable large scale project execution monitoring (relevant because it teaches project improvement)
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/JAMES WEBB/Examiner, Art Unit 3624
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