DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Introduction
The following is a non-final Office Action in response to Applicant’s submission filed on 3/13/2025. Currently claims 1-13 are pending and claims 1, 10, 11 are independent.
Priority
Acknowledgment is made of applicant’s claim for foreign priority under 35 U.S.C. 119 (a)-(d). The certified copy has been filed in parent Application No. JP2024-071478, filed on 4/25/2024.
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/13/2025 appears to be in compliance with the provisions of 37 CFR 1.97. Accordingly, the IDS is being considered by the Examiner.
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.
This application includes one or more claim limitations that do not use the word “means,” but are nonetheless being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because the claim limitation(s) uses a generic placeholder that is coupled with functional language without reciting sufficient structure to perform the recited function and the generic placeholder is not preceded by a structural modifier. Such claim limitation(s) is/are: “section” in claims 1-9, 11.
Because this/these claim limitation(s) is/are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, it/they is/are being interpreted to cover the corresponding structure described in the specification as performing the claimed function, and equivalents thereof.
If applicant does not intend to have this/these limitation(s) interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, applicant may: (1) amend the claim limitation(s) to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph (e.g., by reciting sufficient structure to perform the claimed function); or (2) present a sufficient showing that the claim limitation(s) recite(s) sufficient structure to perform the claimed function so as to avoid it/them being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
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 1-13 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea), specifically an abstract idea, without significantly more. With respect to claims 1-13, following the guidance contained within MPEP 2106, the inquiry for patent eligibility follows two steps: Step 1: Does the claimed invention fall within one of the four statutory categories of invention? Step 2A (Prong 1): Is the claim “directed to” an abstract idea? Step 2A (Prong 2): Is the claim integrated into a practical application? Step 2B: Does the claim recite additional elements that amount to “significantly more” than the abstract idea?
In accordance with these steps, the Examiner finds the following:
Step 1: Claim 1 and its dependent claims (claims 2-9) are directed to a statutory category, namely a system/machine. Claim 10 is directed to a statutory category, namely a method. Claim 11 and its dependent claims (claims 12-13) are directed to a statutory category, namely a system/machine.
Step 2A (Prong 1): Claims 1, 10, and 11, which are substantially similar claims to one another, are directed to the abstract idea of “Mental Processes”, or more particularly, “Concepts performed within the human mind (including: observation, evaluation, judgment, opinion) (See MPEP 2106).” In this application that refers to using a computer system to analyze the supply chain risk within a global supply chain. To clarify this further, the Applicant’s disclosed invention is a conceptual system meant to perform the same function that a supply chain executive might perform for a global vehicle manufacturer. The abstract elements of claims 1, 10, and 11, recite in part “Obtain risk data…Obtain risk degree…Obtain risk scale…Obtain risk value…”. Dependent claims 2-9, 12, 13, add to the abstract idea the following limitations which recite in part “Obtain risk degree on a basis of a human risk…Represent Risk degree…Obtain scale…Obtain scale from user…Calculate risk value…Decrease risk vale…Increase risk value…Calculate risk value…Calculate risk according to rank…Visualize risk…”. All of these additional limitations, however, only serve to further limit the abstract idea, and hence are nonetheless directed towards fundamentally the same abstract idea as independent claims 1, 10, and 11.
Step 2A (Prong 2): Independent claims 1, 10, and 11, which are substantially similar claims to one another, do not contain additional elements, either considered individually or in combination, that effectively integrate the exception into a practical application of the exception. These claims do include the limitation that recites in part “Processors…Memory…Supply chain managing device…Visualization device…Risk data obtaining section…Risk degree calculating section…Scale calculating section… Risk estimating section…” which limits the claims to a networked/computer based environment, but this is insufficient with respect to integration into a practical application because it is merely applying the abstract idea to a general computer (See MPEP 2106.05(f)).
Additionally, dependent claims 2-9, 12, 13 do not include any additional elements to conduct a further Step 2A (Prong 2) analysis.
Step 2B: Independent claims 1, 10, and 11, which are substantially similar claims to one another, include additional elements, when considered both individually and as an ordered combination, which are insufficient to amount to significantly more than the judicial exception. The additional elements of these claims recite in part “Processors…Memory…Supply chain managing device…Visualization device…Risk data obtaining section…Risk degree calculating section…Scale calculating section… Risk estimating section…”. These items are not significantly more because these are merely the software and/or hardware components used to implement the abstract idea (analyze the supply chain risk within a global supply chain) on a general purpose computer (See MPEP 2106.05(f)). This is exemplified in the Applicant’s specification in [0020] – “The adapter 10, the DB server 20, and the AP server 30 are each a computer device, and are each, for example, a server computer. However, there is no limitation to this, and the adapter 10, the DB server 20, and the AP server 30 may each be a personal computer (PC), a mobile computer, a smart phone, a tablet, or the like.”
Additionally, dependent claims 2-9, 12, 13 do not include any additional elements to conduct a further 2B analysis.
Accordingly, whether taken individually or as an ordered combination claims 1-13 are rejected under 35 USC § 101 because the claimed invention is directed to a judicial exception, an abstract idea, without significantly more.
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.
Claims 1-5, 10-13 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Vashistha (US 20170193411 A1).
Regarding claims 1, 10, and 11, Vashistha discloses a supply chain managing device (Vashistha ABS - Machines and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located) comprising: a risk data obtaining section configured to obtain risk data including a relation between a spatial position and a risk value, as data related to a risk affecting a supply chain (Vashistha ¶44 - The present invention may also be used to transform data into graphical and quantitative risk evaluation metrics reflecting the potential risk associated with a geographic location at which one or more suppliers are located. The geographic location may be, for example, a city or a country); a risk degree calculating section configured to obtain a risk degree corresponding to a type of the risk on a basis of the risk data (Vashistha ¶30 - At Step 204, a risk score is assigned to each parameter (101) based on the parameter value entered for the parameter (101)); a scale calculating section configured to obtain a scale as a spatial position suitable for representing the risk, on a basis of the risk degree (Vashistha ¶31 - The risk score assigned to the bands may be a number chosen from a predetermined range such as one to ten. The number assigned to the risk parameter (101) reflects the relative level of risk reflected by the parameter value. For example, a risk score in the range of 1.0 to 2.0 may correspond to a negligible risk, a risk score in the range of 2.1 to 4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0 may correspond to a moderate risk, a risk score in the range of 6.1 to 8.0 may correspond to a high risk, and a risk score in the range of 8.1 to 10.0 may correspond to an extreme risk); and a risk estimating section configured to obtain the risk value at a specified spatial position, the risk value corresponding to the scale (Vashistha Fig. 5 – Fig. 6).
Regarding claim 2, Vashistha discloses the risk degree calculating section obtains the risk degree on a basis of a human risk degree as a risk degree with regard to a human and a supply chain risk degree as a risk degree with regard to the supply chain (Vashistha Fig. 3 – Fig. 6).
Regarding claim 3, Vashistha discloses the risk degree calculating section obtains the risk degree by representing the human risk degree and the supply chain risk degree by numerical values and calculating a weighted average of the respective numerical values of the human risk degree and the supply chain risk degree (Vashistha Fig. 3 – Fig. 6).
Regarding claim 4, Vashistha discloses the scale calculating section obtains the scale according to magnitude of the risk degree (Vashistha ¶31 - The risk score assigned to the bands may be a number chosen from a predetermined range such as one to ten. The number assigned to the risk parameter (101) reflects the relative level of risk reflected by the parameter value. For example, a risk score in the range of 1.0 to 2.0 may correspond to a negligible risk, a risk score in the range of 2.1 to 4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0 may correspond to a moderate risk, a risk score in the range of 6.1 to 8.0 may correspond to a high risk, and a risk score in the range of 8.1 to 10.0 may correspond to an extreme risk).
Regarding claim 5, Vashistha discloses the scale calculating section obtains the scale on a basis of information concerning a scale input from a user (Vashistha ¶29 - A graphical user interface may be used to facilitate entry of the parameter values), in addition to the magnitude of the risk degree (Vashistha ¶31 - The risk score assigned to the bands may be a number chosen from a predetermined range such as one to ten. The number assigned to the risk parameter (101) reflects the relative level of risk reflected by the parameter value. For example, a risk score in the range of 1.0 to 2.0 may correspond to a negligible risk, a risk score in the range of 2.1 to 4.0 may correspond to a low risk, a risk score in the range of 4.1-6.0 may correspond to a moderate risk, a risk score in the range of 6.1 to 8.0 may correspond to a high risk, and a risk score in the range of 8.1 to 10.0 may correspond to an extreme risk).
Regarding claim 12, Vashistha discloses the visualizing device visualizes the risk value corresponding to the scale (Vashistha Fig. 3 – Fig. 6).
Regarding claim 13, Vashistha discloses for the type of the risk, the visualizing device associates the scale and the risk value corresponding to the scale with each other, and visualizes the risk value (Vashistha Fig. 3 – Fig. 6).
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 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.
Claims 6-7 are rejected under 35 U.S.C. 103 as being unpatentable over Vashistha (US 20200265357 A1) in view of Urban et al. (US 20190188616 A1)
Regarding claim 6, Vashistha discloses a supply chain managing device (Vashistha ABS - Machines and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located).
Vashistha lacks the risk estimating section calculates the risk value corresponding to the scale on a basis of a magnitude relation between the spatial position included in the risk data and the scale calculated by the scale calculating section.
Urban, from the same field of endeavor, teaches the risk estimating section calculates the risk value corresponding to the scale on a basis of a magnitude relation between the spatial position included in the risk data and the scale calculated by the scale calculating section (Urban ¶45 - Using the various data metrics 322 having varying units, a sigmoid function based logistic regression analysis may be used. The sigmoid distribution is used to derive a scaled score between 0-100 for each data metric that may be averaged up to derive a scaled score for the risk factor and then weight-averaged to derive a score for the baseline risk score for the country or geographic region… Urban ¶41 - Kilometers of Rail Lines/Land Area unit of measure for a geographic region 318a may be used as one data input to assess transportation infrastructure for a geographic region. It should be understood that other data metrics and units of measure that impact risk factors or statistical measure(s) of a geographic region may be used {i.e. population}),
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the geographic risk methodology/system of Vashistha by including the risk simulation techniques of Urban because Urban discloses a “system to assist a manufacturer in detecting anomalies in risk levels, thereby helping to avoid disruption in a supply chain (Urban ¶76)”. Additionally, Vashistha further details “Machines and methods to quantify risk associated with suppliers or geographic locations (Vashistha ABS)” so it would be obvious to consider including the additional risk simulation techniques that Urban discloses because it would improve the risk analysis techniques of Vashistha by better detecting anomalies.
Regarding claim 7, Vashistha in view of Urban discloses a supply chain managing device (Vashistha ABS - Machines and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located).
Urban further teaches the risk estimating section calculates the risk value corresponding to the scale by decreasing, according to the scale, the risk value included in the risk data, when the scale represents a smaller area than the spatial position included in the risk data, and calculates the risk value corresponding to the scale by increasing, according to the scale, the risk value included in the risk data, when the scale represents a larger area than the spatial position included in the risk data (Urban ¶45 - Using the various data metrics 322 having varying units, a sigmoid function based logistic regression analysis may be used. The sigmoid distribution is used to derive a scaled score between 0-100 for each data metric that may be averaged up to derive a scaled score for the risk factor and then weight-averaged to derive a score for the baseline risk score for the country or geographic region… Urban ¶41 - Kilometers of Rail Lines/Land Area unit of measure for a geographic region 318a may be used as one data input to assess transportation infrastructure for a geographic region. It should be understood that other data metrics and units of measure that impact risk factors or statistical measure(s) of a geographic region may be used {i.e. population}).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the geographic risk methodology/system of Vashistha by including the risk simulation techniques of Urban because Urban discloses a “system to assist a manufacturer in detecting anomalies in risk levels, thereby helping to avoid disruption in a supply chain (Urban ¶76)”. Additionally, Vashistha further details “Machines and methods to quantify risk associated with suppliers or geographic locations (Vashistha ABS)” so it would be obvious to consider including the additional risk simulation techniques that Urban discloses because it would improve the risk analysis techniques of Vashistha by better detecting anomalies.
Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Vashistha (US 20200265357 A1) in view of Dunbar et al. (US 20210398050 A1)
Regarding claim 8, Vashistha discloses a supply chain managing device (Vashistha ABS - Machines and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located).
Vashistha lacks the risk estimating section calculates the risk value corresponding to the scale on a basis of a rank set for a user.
Dunbar, from the same field of endeavor, teaches the risk estimating section calculates the risk value corresponding to the scale on a basis of a rank set for a user (Dunbar ¶114 - The steps are identical with those of FIG. 1, with the exception of block 304), which occurs after 114. At block 304, the various scenarios are filtered, by keeping those scenarios that meet a user-defined threshold, and discarding those scenarios that do not meet the user-defined threshold. The filtered scenarios are then ranked at 114. At block 104, an exception in an incoming supply chain data is detected by a processor. At block 106, the processor analyzes the exception. At block 302, the processor triggers a scenario generator. At block 110, the scenario generator generates one or more resolution scenarios for the exception. At block 112, a digital supply chain simulator, evaluates each scenario based on the impact of each scenario on the supply chain. Finally, at 114, a recommender ranks the one or more scenarios based on the impact and a set of target Key Performance Indicators (KPIs) in order to generate resolution scenarios based on rules defined by the user at block 110).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the geographic risk methodology/system of Vashistha by including the issue detection techniques of Dunbar because Dunbar discloses a “system to assist a manufacturer in detecting anomalies in risk levels, thereby helping to avoid disruption in a supply chain (Dunbar ¶76)”. Additionally, Vashistha further details “Machines and methods to quantify risk associated with suppliers or geographic locations (Vashistha ABS)” so it would be obvious to consider including the additional issue detection techniques that Dunbar discloses because it would improve the risk analysis techniques of Vashistha by better detecting supply chain anomalies.
Regarding claim 9, Vashistha in view of Dunbar discloses a supply chain managing device (Vashistha ABS - Machines and methods to quantify risk associated with suppliers or geographic locations at which suppliers or global internal delivery centers are located).
Dunbar further teaches according to a magnitude relation between a numerical value indicating the rank and a predetermined threshold value, the risk estimating section determines whether to calculate the risk value corresponding to the scale by decreasing, according to the scale, the risk value included in the risk data, or calculate the risk value corresponding to the scale by increasing, according to the scale, the risk value included in the risk data (Dunbar ¶114 - The steps are identical with those of FIG. 1, with the exception of block 304), which occurs after 114. At block 304, the various scenarios are filtered, by keeping those scenarios that meet a user-defined threshold, and discarding those scenarios that do not meet the user-defined threshold. The filtered scenarios are then ranked at 114. At block 104, an exception in an incoming supply chain data is detected by a processor. At block 106, the processor analyzes the exception. At block 302, the processor triggers a scenario generator. At block 110, the scenario generator generates one or more resolution scenarios for the exception. At block 112, a digital supply chain simulator, evaluates each scenario based on the impact of each scenario on the supply chain. Finally, at 114, a recommender ranks the one or more scenarios based on the impact and a set of target Key Performance Indicators (KPIs) in order to generate resolution scenarios based on rules defined by the user at block 110).
It would be obvious for one of ordinary skill in the art before the effective filing date of the Applicant’s claimed invention to modify the geographic risk methodology/system of Vashistha by including the issue detection techniques of Dunbar because Dunbar discloses a “system to assist a manufacturer in detecting anomalies in risk levels, thereby helping to avoid disruption in a supply chain (Dunbar ¶76)”. Additionally, Vashistha further details “Machines and methods to quantify risk associated with suppliers or geographic locations (Vashistha ABS)” so it would be obvious to consider including the additional issue detection techniques that Dunbar discloses because it would improve the risk analysis techniques of Vashistha by better detecting supply chain anomalies.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
McNamara et al. (US 20150046363 A1)
Leidner et al. (WO 2017017533 A1)
Vashistha (US-20200265357-A1)
Nagao et al. (JP 2010108308 A)
and
R. S. M. Goh et al., "RiskVis: Supply chain visualization with risk management and real-time monitoring," 2013 IEEE International Conference on Automation Science and Engineering (CASE), Madison, WI, USA, 2013, pp. 207-212 [online], [retrieved on 2026-06-17]. Retrieved from the Internet <https://ieeexplore.ieee.org/document/6653910?source=IQplus>
These pieces of prior art are cited because they all disclose variations on visualizing supply chain risks associated with different geographic locations and issues present at those location.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Michael R Koester whose telephone number is (313)446-4837. The examiner can normally be reached Monday thru Friday 8:00AM-5:00 PM EST.
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, Jerry O'Connor can be reached at (571) 272-6787. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MICHAEL R KOESTER/Examiner, Art Unit 3624
/Jerry O'Connor/Supervisory Patent Examiner,Group Art Unit 3624
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