DETAILED ACTION
Status of Claims
The present application, filed on or after 3/16/2013, is being examined under the first inventor to file provisions of the AIA .
This action is in reply to the Application and claims filed 11/13/2024.
Claims 1-3 and 5-6 have been amended by preliminary amendment.
Claims 1-6 have been examined and are pending.
Information Disclosure Statement (IDS)
Acknowledgement is hereby made of receipt of Information Disclosure Statements filed by applicant on 11/13/2024.
(AIA ) Examiner Note
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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned at the time any inventions covered therein were effectively filed absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned at the time a later invention was effectively filed in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
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 1-6 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea (i.e. a judicial exception) without significantly more.
Per step 1 of the Subject Matter Eligibility Guidance outlined in the MPEP 2106, the claims are directed towards a process, machine, or manufacture.
Per step 2A Prong One, the claims recite specific limitations which fall within at least one of the groupings of abstract ideas enumerated in the MPEP 2106, as follows:
Per Independent claims 1, 5, 6:
construct a time-series model for predicting a future food loss coefficient using the time-series data of the food loss coefficient and the one time-series model parameter or the plurality of time-series model parameters
As noted supra, these limitations fall within at least one of the groupings of abstract ideas enumerated in MPEP 2106. Specifically, these limitations fall within the groups Mathematical Concepts (e.g. mathematical relationships; mathematical formulas or equations; mathematical calculations) and Certain Methods Of Organizing Human Activity (e.g. fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); maTakeuchiing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).
That is, the step as drafted, is a business decision to create a mathematical model (i.e. a time-series model) for a business purpose of predicting food waste in a process (i.e. food loss coefficient which represents, per Applicant’s Specification para [0019]: “the percentage of food loss (loss or discarded) in each step of the supply chain”) thus falling into Certain Methods of Organizing Human Activity. There is no technical problem being solved and no technical solution provided to solve a technical problem. Instead, time-series modeling at this high-level of generality cannot be taken as applicant’s invention but instead must be understood as generically implementing known mathematical time-series model for the business purpose of predicting food waste in a business process. There appears to be no technical improvement to any particular device or technique as claimed herein. Furthermore, the mere nominal recitation of a generic computing hardware (e.g. “hardware processor”, etc…) does not take the claim limitation out of the enumerated grouping. Thus, the claims recite an abstract idea.
Per step 2A Prong 2, the Examiner finds that the judicial exception is not integrated into a practical application. Although there are additional elements, other than those noted supra, recited in the claims, none of these additional element(s) or a combination of elements as recited in the claims apply, rely on, or use the judicial exception in a manner that imposes a meaningful limit on the judicial exception, such that it is more than a drafting effort designed to monopolize the exception. As drafted, the claims as a whole merely describe how to generally “apply” the aforementioned concepts or, link them to a field of use (i.e. in this case predicting food waste or loss) or, serve as insignificant extra-solution activity (data acquisition, collection, storage, and/or display). The claimed computer components are recited at a high level of generality and are merely invoked as tools to implement the idea but are not technical in nature. Simply implementing the abstract idea on or with generic computer components is not a practical application of the abstract idea.
These additional limitations are as follows: “A prediction device comprising: a hardware processor configured to acquire time-series data of a food loss coefficient and one time-series model parameter or a plurality of time-series model parameters;…”
However, these elements do not present a technical solution to a technical problem; i.e. Applicant’s invention is not a technique nor technical solution for “acquiring” data regardless of what the data is intended to represent. The additional elements do not recite a specific manner of performing any of the steps core to the already identified abstract idea. Instead, these features merely serve to generally “apply” the aforementioned concepts or, link them to a field of use or, are insignificant extra-solution activity to the already identified abstract idea and do not integrate the abstract idea into a practical application thereof.
Per Step 2B, the Examiner does not find that the claims provide an inventive concept, i.e., the claims do not recite additional element(s) or a combination of elements that amount to significantly more than the judicial exception recited in the claim. As discussed with respect to Step 2A Prong Two, the additional elements in the independent claims were considered as merely serving to generally “apply” the aforementioned concepts via generically described computer components (e.g. by one or more hardware processors, or a computer, or a non-transitory computer readable medium storing program for causing a computer to perform the steps noted supra) and “link” them to a field of use (i.e. prediction of food waste for business purposes), or as insignificant extra-solution activity (e.g. data collection or acquisition, etc…). For the same reason these elements are not sufficient to provide an inventive concept; i.e. the same analysis applies here in 2B. Mere instructions to apply an exception using a generic computer component and conventional data gathering cannot integrate a judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B. So, upon revaluating here in step 2B, these elements are determined to amount to no more than mere instructions to apply the exception using generic computer components (i.e. a server) and/or gather and transmit data which is well-understood, routine, conventional activity in the field; i.e. note the Symantec, TLI, and OIP Techs Court decisions cited in MPEP 2106.05(d)(ll) indicate that mere receipt or transmission of data over a network is a well-understood, routine, and conventional function when it is claimed in a merely generic manner (as it is here).
Accordingly, alone and in combination, these elements do not integrate the abstract idea into a practical application, as found supra, nor provide an inventive concept, and thus the claims are not patent eligible.
As for the dependent claims, the dependent claims do recite a combination of additional elements. However, these claims as a whole, considered either independently or in combination with the parent claims, do not integrate the identified abstract idea into a practical application thereof nor do they provide an inventive concept.
For example, dependent claim 4 recites the following: “wherein the time-series model is a nonlinear time-series model.” However, applicant does not appear to have invented any particular time-series model and non-linear time-series models were well-known before the effective filing date of the claimed invention. Therefore, this further description of an off-the-shelf model which may be used to predict a value which the business desired to predict from collected business data is not significantly more than the already recited abstract idea.
Therefore, the Examiner does not find that these additional claim limitations integrate the abstract idea into a practical application nor provide an inventive concept. Instead, these limitations, as a whole and in combination with the already recited claim elements of the parent claims, are not significantly more than the already identified abstract idea. A similar finding is found for the remaining dependent claims.
For these reasons, the claims are not found to include additional elements that are sufficient to amount to significantly more than the judicial exception and therefore the claims are not found to be patent eligible.
Please see the MPEP 2106 and the 2019 Revised Patent Subject Matter Eligibility Guidance published in the Federal Register (84 FR 50) on January 7, 2019 (found at http://www.uspto.gov/patent/laws-and-regulations/examination-policy/examination-guidance-and-training-materials).
Claim Rejections - 35 USC § 103 (AIA )
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 of this title, 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 non-obviousness.
Claims 1-6 are rejected under 35 U.S.C. 103 as obvious over Takeuchi et al. (U.S. 2004/0254760 A1; hereinafter, "Takeuchi") in view of Non Patent Literature (C. Caldeira, V. De Laurentiis, S. Corrado, F. van Holsteijn, and S. Sala, "Quantification of food waste per product group along the food supply chain in the European Union: a mass flow analysis," Resources, Conservation and Recycling, vol. 149, pp. 479 to 488, Oct. 2019; hereinafter “NPL”).
Claims 1, 5, 6:
Pertaining to claims 1, 5, 6 exemplified in the limitations of method claim 1, Takeuchi as shown teaches the following:
A prediction device comprising: a hardware processor configured to:
acquire time-series data […] and one time-series model parameter or a plurality of time-series model parameters (Takeuchi, see at least Figs. 2-4 and [0024]-[0040], e.g.: “…FIG. 4 is a flowchart illustrating the operation of the time-series model learning unit 11 shown in FIG. 3. The time-series model learning unit 11 of FIG. 3 operates as follows: While sequentially reading in a data sequence [acquired time series data], the time-series model learning unit 11 updates parameters sequentially based upon the data read. We write Theta(t) for a parameter value obtained [acquired time-series model parameter] as a result of learning using data from x1 to xt. When the AR model is adopted as the time-series model, the parameter value Theta (t) can be updated. This will be outlined in the below…”; note Theta is a parameter vector as noted per [0033].); and
construct a time-series model for predicting […] using the time-series data […] and the one time-series model parameter or the plurality of time-series model parameters (Takeuchi, see citations noted supra, e.g. again per at least [0028]-[0034], the time-series model which is constructed may be an auto-regression model referred to as an “AR” model, etc…;
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Further note the model is used to predict change points, e.g. per [0062]-[0064], etc…
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The only difference between the claim features and the teaching of the prior art is in the intended meaning of the time series data which are collected, i.e. of a food loss coefficient and the intended use of the time-series model which is constructed, i.e. for predicting a future food loss coefficient. However, the intended use statements do not place a further limit on the time-series data acquisition step nor of the model construction. Stated another way, data acquisition would be done the same regardless of the meaning of the data which is collected and there does not appear to be any functional limitation placed upon the model construction step due to its intended field of use; these steps are agnostic as to the field of endeavor to which they are being employed. Nonetheless, Takeuchi in view of NPL teaches these features as follows:
[…time series data of] food loss coefficient (NPL, teaches a scheme for quantifying food loss; i.e. there is a mass flow analysis (MFA) techniqu. In the MFA technique, analysis is performed based on a food waste coefficient [food loss coefficient] which is a ratio of an amount of loss to an amount of food flowing in each step of a supply chain. Examiner notes that the change of such ratio from time “t1” to “tn” is time series data. See NPL e.g. at least Section 2.3 – 2.4: “…Food waste coefficients (i.e. the percentage of a flow entering a certain stage of the FSC that is wasted) taken from the literature were used to quantify the amount of waste generated at the steps of the FSC where there was a lack of statistical data sources to calculate the waste flows with mass balances. This was the case at PP, D&R and at consumption stages. The process followed to select the food waste coefficients builds on the literature review developed by Xue et al. (2017)….” And per at least Fig. 1 which is an accounting approach and main sources of data used to calculate food waste flows:
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In view of these teachings, Examiner finds that time-series data of a food waste coefficient [food loss coefficient] is a known type of data to be collected as demonstrated by NPL upon which the known method/system of Takeuchi may operate (which is directed towards collecting such time series data to construct a time-series model to predict future such data). Therefore, it would be obvious to a person of ordinary skill in the art, motivated by the combined teachings of NPL and Takeuchi, to collect the food loss coefficient time series data of NPL and construct the time series model of Takeuchi to predict future food loss coefficients, especially motivated per at least Takeuchi’s teachings at [0002] and [0062]-[0064] that his invention is applicable to detecting change points in time-series data, e.g. detecting a change point from when a ratio of an amount of food loss to an amount of food flowing changes, e.g. as represented by a food loss coefficient of NPL because per MPEP 2143(I) (G) Some teaching, suggestion, or motivation in the prior art that would have led one of ordinary skill to modify the prior art reference or to combine prior art reference teachings to arrive at the claimed invention is obvious. The motivation to combine may be implicit and may be found in the knowledge of one of ordinary skill in the art, or, in some cases, from the nature of the problem to be solved. Id. at 1366, 80 USPQ2d at 1649. Furthermore, KSR forecloses the argument that a specific teaching, suggestion, or motivation is require to support a finding of obviousness. See the Board decision Ex parte Smith, -- USPQ2d --, slip op. at 20, (Bd. Pat. App. & Interf. June 25, 2007) (citing KSR, 82 USPQ2d at 1396). And Examiner notes that "Section 103 forbids issuance of a patent when 'the differences between the subject matter sought to be patented and the prior art are such that the subject matter as a whole would have been obvious at the time the invention was made to a person having ordinary skill in the art to which said subject matter pertains.'" KSR Int'l Co. v. Teleflexlnc., 127 S.Ct. 1727, 1734 (2007).
Claim 2
Takeuchi / NPL teaches the limitations upon which this claim depends. Furthermore, as shown, Takeuchi teaches the following: The prediction device according to claim 1, further wherein the hardware processor is further configured to predict time-series data of the future food loss coefficient by inputting time-series data of a past food loss coefficient and one past time-series model parameter or a plurality of past time-series model parameters to the constructed time-series model (Takeuchi, see at least [0074]-[0075] regarding input of time-series data and updated model parameters to the time-series model. )
Claim 3
Takeuchi teaches the limitations upon which this claim depends. Furthermore, as shown, Takeuchi teaches the following: The prediction device according to claim 2, wherein the hardware processor is further configured to predict future food loss using the predicted time-series data of the food loss coefficient (Takeuchi, see again citations noted supra, e.g.:
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Claim 4
Takeuchi teaches the limitations upon which this claim depends. Furthermore, as shown, Takeuchi teaches the following:
The prediction device according to claim 1, wherein the time-series model is a nonlinear time-series model (Takeuchi, see citations noted supra, e.g. again per equations 1-6, the “AR” time-series model is non-linear; e.g. note equation (4).
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Conclusion
The following prior art is made of record although not relied upon as it is considered pertinent to applicant's disclosure:
"Impact of food wastage on water resources and GHG emissions in Korea: A trend-based prediction modeling study - ScienceDirect." https://www.sciencedirect.com/science/article/abs/pii/S0959652620326093
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MICHAEL J SITTNER whose telephone number is (571)270-3984. The examiner can normally be reached M-F; ~9:30-6:30. 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, Waseem Ashraf can be reached on (571) 270-3948. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and maTakeuchie patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/Michael J Sittner/
Primary Examiner, Art Unit 3621
Prosecution Insights