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 .
Applicant’s Reply
Applicant's response of 10/31/25 has been entered. The examiner will address applicant's remarks at the end of this office action.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 28, is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 28, the claim depends to claim 27 that is directed to a non-transitory computer readable medium whereas the preamble of claim 28 states that the claim is directed to “The program of claim 27”. Claim 27 has been amended to delete the claimed element of a program, so there is no antecedent basis for reciting “The program” of claim 27. Also, it is not clear if the claim 28 is directed to the program itself or if the claim is directed to the non-transitory computer readable medium that is recited in claim 27. This confusion and lack of antecedent basis for “The program” renders the claim indefinite. For purposes of examination, the examiner has treated the claim as being further limiting to the non-transitory computer readable medium that is recited in claim 27. However, correction is still required.
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 16-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
The claims 16-29 recite a device and a non-transitory computer readable medium such that they are considered to pass step 1 of the eligibility analysis.
For step 2A, with respect to claims 16, 27, 29, the claim(s) recite(s) an abstract idea of estimating/calculating resource recovery for biogas materials, which is considered to be a certain method of organizing human activities type of abstract idea as will be set forth below.
Using claim 16 as a representative example that is applicable to claims 27, 29, the abstract idea is defined by the elements of:
receive input data regarding a machine learning library for biogas materials or property data related to biogas materials;
estimate an evaluation value for resource recovery of biogas materials by performing an estimation process based on the property data
wherein the property data includes at least one of the following: type, collection location, property, and quantity of biomass materials
The claimed concept reflected by the above noted claim elements is part of an overall recycling process where food waste material, such as from a restaurant, is sent to a processing facility for recycling, where the claimed invention is determining an estimated value for the recovery of biogas, such as methane. This includes determining a recovery value for biogas based on data that represents the processing plant, see claim 17 for example. This is a commercial practice/interaction that is the concept of evaluating the feasibility of processing certain waste at a given plant, so that the plant can prioritize and process certain waste and in order to provide for stable operations at the processing plant, and to maximize biogas yield. This is considered to be a commercial practice that is waste recycling, and is a fundamental economic concept. Recycling of waste to convert the waste to a usable energy source is an economic practice that is involved in the management of recycling and the operation of processing facilities that process waste to obtain biogas. This represents a certain method of organizing human activities.
Also, the claimed evaluation, absent the recitation to machine learning, can be done by a person mentally. The mental process category for abstract ideas includes making evaluations or judgements or observations or opinions. The claimed invention is reciting the use of data to make an estimate of the amount of biogas that can be recovered, and is fully capable of being performed by a person mentally. The claims are additionally taken to be a mental process that can be performed by a person who is analyzing waste data to calculate recovery values. For the above reasons, the claims 16, 27, are found to be reciting an abstract idea at step 2A.
For claims 16, the additional elements are the recitation to an input device, a machine learning device executing a machine learning model to receive the machine learning library or property data from the input device, a learning process execution device configured to learn setting values…., and the evaluation value calculation execution device. For claim 27, a non-transitory computer readable medium storing instructions that execute the claimed steps, and the at least one computing device with a hardware processor that stores instructions as is recited in claim 29. These elements are claiming the use of “device(s)” to perform the steps that define the abstract idea in combination with the use of a device that executes a machine learning model.
This judicial exception is not integrated into a practical application (2nd prong of eligibility test for step 2A) because the additional elements of the claim when considered individually and in combination with the claim as a whole, amount to the use of a computing device(s) that are being used as a tool to execute the abstract idea (see MPEP 2106.05(f)) in combination with the use of a machine learning model, that is recited at a high level of generality. With respect to the claimed devices that are recited as performing the steps/functions that define the abstract idea, this is taken as an instruction for one to use a computer to perform the steps that define the abstract idea. The claimed use of the machine learning model is also taken as a general link to use of the computers and machine learning, which is a particular technological environment, see MPEP 2106.05(h). Machine learning by definition requires the model to be able to learn and that is done by using the machine learning library that contains the data that the model is trained on. A machine model learning (being trained) is inherent to machine learning and does not amount to more than a general link to the use of machine learning in general. See MPEP 2106.05(h) with respect to a link to a particular technological environment.
Therefore, the above is indicative of the fact that the claim has not integrated the abstract idea into a practical application and therefore the claim is found to be directed to the abstract idea identified by the examiner.
For step 2B, the claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception when considered individually and in combination with the claim as a whole because they do not amount to more than simply instructing one to practice the abstract idea by using a generically recited computing device(s) and a device that uses machine learning to perform steps that define the abstract idea. This does not render the claims as being eligible. See MPEP 2106.05(f), (h). The rationale set forth for the 2nd prong of the eligibility test above is also applicable to step 2B in this regard so no further comments are necessary. This is consistent with the PEG found in the MPEP 2106 where the considerations at the 2nd prong and step 2B overlap when the issue at hand in a claim is mere implementation by a computer.
For claims 17, 28, the following elements are a further embellishment of the same abstract idea that was found for claim 16:
data regarding the operational status of a biogas plant is configured to be input
wherein the data regarding the operational status of the biogas plant is further processed
estimate an evaluation value regarding resource recovery of biogas materials by performing an estimation process based on both the property data and the data, regarding the operational status of the biogas plant
wherein the data regarding the operational status of the biogas plant includes at least one of the following:
the type of biogas material used at the plant;
the fermentation method;
the presence and amount of additives, such as coagulants;
the VS volume load per cubic meter of a methane fermentation tank;
the organic decomposition rate in the methane fermentation tank;
the methane gas generation amount in a gas holder; and
the measured methane yield (BMP)
The above elements are simply reciting more about the abstract idea of claim 16 and further define how the estimation of the value for the resource recovery is being performed. This is part of the abstract idea. The claimed use of the input device, the value calculation execution device and the machine learning are additional elements and have been treated in the same manner as set forth for claim 16 to which applicant is referred. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B; therefore the claims are not considered to be eligible.
For claims 18, 19, 21, 22, the claimed evaluation values including a value regarding mixed biogas material as claimed and determining a combination of gas is unsuitable, and the claimed conditions of claim 22, are elements that are a further recitation to the same abstract idea of claim 16. All that is claimed is part of the abstract idea. People can do what is claimed using pen and paper or by performing the claimed step mentally. There are no further additional elements claimed. The calculation execution device has been treated in the same manner as set forth for claim 16, to which applicant is referred. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B; therefore the claims are not considered to be eligible.
For claim 20, the claimed determining of the compatibility of mixed fermentation for mixed biogas as claimed, is a further recitation to the same abstract idea as set forth in claim 16. No further additional element has been claimed. The calculation execution unit has been treated in the same manner as set forth for claim 16, to which applicant is referred. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B; therefore the claims are not considered to be eligible.
For claim 23, the claimed predicting of and determining appropriate types of quantities, etc., is an element that is part of the abstract idea of claim 16. The claimed prediction and determination is part of the abstract idea and is something that can be done by a person, including mentally. The claim does not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B; therefore the claims are not considered to be eligible.
For claim 24, the claimed estimation that is recited is part of the abstract idea. Estimating the claimed variables can be done by people and is part of the abstract idea as far as the data processing that is occurring in the claims. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B; therefore the claims are not considered to be eligible.
For claim 25, reciting that the machine learning library includes at least one of the following examples for data is an element that is part of the abstract idea and is reciting information per se. The data that is part of the training library is an element that is part of the abstract idea. The claims do not recite any additional elements that provide for integration at the 2nd prong or that provide significantly more at step 2B; therefore the claims are not considered to be eligible.
For claim 26, the claim recites an additional element of the communication device configured to communicated with a network and a data transmission that is sending the evaluation value estimation via the network. This is claiming the use of a computer connected to a network for data transmission, and is akin to reciting “apply it” with a computer and a network such as the Internet. This does not define more than a general link to the use of computers connected by a network , which does not provide for integration or significantly more at step 2B. The claim is not found to be eligible.
Therefore, for the above reasons, claims 16-29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to 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.
Claim(s) 16-17, 25, 27, 28, 29, is/are rejected under 35 U.S.C. 102(a)(1) as being anticipated by NPL article “Tree based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste”, from 2021, hereafter referred to as Wang.
For claims 16, 25, 27, 29, Wang teaches that machine learning can be used to predict and estimate waste material biogas yield as a function of the feedstock type. The reference teaches that an automated machine learning algorithm is used to predict biogas yield, see the abstract. Disclosed on page 12992 is that a training data set was used that comprised 8 years of operating data with 31 different waste streams. Disclosed is that using a large data set provides for a more accurate model and predictive capability. This data set satisfies the claimed learning library for the biogas materials, and that is used by the claimed machine learning model where the data is “learned” so that the model is trained. The machine learning model of Wang is trained on the data set, where the data is received as claimed, and a learning process occurs as claimed. This is the training of the model that is inherent to and part of machine learning by definition, and which is taught by Wang. Page 12994 discloses the prediction of the biogas yield, and satisfies the claimed biogas recovery value that is estimated by performing an estimation process. This is satisfied by the use of machine learning to predict (estimate) the amount of biogas that can be recovered in Wang. Wang teaches that the type of waste material is used to estimate the value for recovery of biogas. This anticipates what is claimed.
For claims 17, 28, Wang teaches that the data used to estimate the biogas recovery value (yield) includes data about the operating conditions that the waste material is subjected to. Data about the processing facility (the biogas plant) is used to make the biogas recovery estimation, see page 12993 under the data collection heading. Discloses is that operating conditions of the biogas facility are monitored and are part of the data that is used by the machine learning model. The model of Wang uses all of the feedstock data for the waste materials and operating information of the biogas plant, to make the claimed estimation of the recovery value. See the last paragraph of page 12993 to the top of page 12994 where this is disclosed. This satisfies what is claimed. The data includes the type of material as claimed (only one of the claimed data types of required in the claim scope).
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.
Claim(s) 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL article “Tree based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste”, herein referred to as Wang in view of Kent et al. (20200074307).
For claim 18, not disclosed is that the C/N ratio is part of the evaluation. The claimed C/N ratio is the ratio of carbon concentration to nitrogen, as is well understood by those of ordinary skill in the art of biogas production.
Kent discloses a system and method for determining an overall BMP for feedstock to determine a cumulative methane yield, and is a reference that is concerned with biogas production. In paragraph 104 Kent makes disclosure to a C/N ratio for a mixture of solids, which is a ratio of carbon to nitrogen in a material. This is something that is well known to those of ordinary skill in the art. This variable is used in the determination of maximizing methane production as is set forth in paragraph 103.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to provide a C/N ratio as claimed for materials when mixed so that the ratio of carbon to nitrogen is known. This is just providing a value to one of ordinary skill in the art that is already something well known to those of ordinary skill in the art in relation to biogas production. This yields predictable results of allowing for the C/N ratio to be known.
Claim(s) 19, 21, 22, is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL article “Tree based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste”, herein referred to as Wang in view of Kent et al. (20200074307) and further in view of NPL reference to Choi (Influence of carbon type and carbon to nitrogen ratio on the biochemical methane potential, pH, and ammonia nitrogen in anaerobic digestion).
For claims 19, 22, the applicant recites a C/N ratio of 25 or higher, or being 25-27. This is claiming a value that depends on the materials being evaluated as far as the feedstock is concerned.
Choi teaches that for optimal biogas recovery, the optimal C/N ratio is found to be 25-30:1, see page 5.
It would been obvious to one of ordinary skill in the art that one can have a C/N ratio of higher than 25 or that is 26, and that this is within the optimal range for a desirable C/N ratio. The claim is reciting that the evaluation results in a finding that the C/N ratio is 25, which is in the middle of the recognized optimal range for a C/N ratio of carbon to nitrogen, as taught by Choi.
For claim 21, not disclosed is that when the methane yield is less than 100ml/g-Vs, the materials are unsuitable for fermentation. One of ordinary skill in the art that is estimating how much biogas can be recovered from the processing of waste materials would have found it obvious that if the recovery value in terms of yield is very low, such as approaching zero (within the claim scope of yield being less than 100ml/g-Vs), then the materials is/are not suitable for fermentation because there would be no yield of biogas to recover. One would not process materials to recover biogas such as a methane yield when the estimation value indicates that the materials would yield low values close to zero for the yield, thereby not making it worthwhile to process the materials via fermentation. This determination and assessment of the situation would have been obvious to one of ordinary skill in the art.
Claim(s) 26 is/are rejected under 35 U.S.C. 103 as being unpatentable over NPL article “Tree based automated machine learning to predict biogas production for anaerobic co-digestion of organic waste”, herein referred to as Wang.
For claim 26, not disclosed by Wang is that the evaluation value is sent is transmitted via a communication device. This is claiming that a computer is sending the result of the analysis via a communication device to another computer, such as by using a network. One of ordinary skill in the art who is using the system of Want to estimate a recovery value would clearly want to know the result and because machine learning is something that requires a computer, it would have been obvious to one of ordinary skill in the art to transmit the estimated recovery value (the biogas yield), such as by sending the result to another interested party by the Internet.
Response to arguments
The 35 USC 112 rejections for claim 23 has been rendered moot based on the amendment to the claims. The rejection of claim 28 is being maintained because the claim still recites “The program of claim 27” where claim 27 has been amended to recite a non-transitory computer readable medium. The applicant is referred to the 35 USC 112 rejection of record where this is addressed.
The traversal of the 35 USC 101 rejection is not persuasive. On page 12 the applicant argues that the claims do not recite a certain method of organizing human activity. The applicant argues that the claims do not recite a fundamental economic practice, a commercial or legal interaction, or managing personal behavior or relationships. The applicant argues that the claims do not recite hedging, insurance, or mitigating risk. This is not persuasive. The claimed invention is determining a value for recovery of biogas, such as methane, including determining a recovery value for biogas based on data that represents the processing plant. This is a commercial practice/interaction that is reciting the concept of evaluating the feasibility of processing certain waste at a given plant, so that the plant can prioritize and process certain waste and in order to provide for stable operations at the processing plant. This is considered to be a commercial practice that is waste recycling, and is a fundamental economic concept. Recycling of waste to convert the waste to a usable energy source is an economic practice that is involved in the management of recycling waste materials and the operation of processing facilities that process waste to obtain biogas. Evaluating the feasibility of processing certain waste so that it can be estimated how much biogas can be recovered, is an economic concept that is forecasting/predicting whether or not to undertake the processing of the waste, so that money is not spent on processing waste that does not yield any meaningful amount of biogas. This is an economic concept that is a commercial practice in the field of waste recycling.
With respect to the mental process category argument, it is not persuasive. The claimed steps that define the abstract idea can be practically performed by a person mentally. The applicant argues that the human mind cannot perform the claimed invention. This is not persuasive for two reasons, one is that the applicant is arguing the additional elements as not being performed mentally, where the examiner did not allege that the devices claimed and the use of machine learning was part of the mental process. Absent the recitation to the additional elements identified in the rejection, the claimed elements that define the abstract idea can be performed mentally. A human being can receive data regarding a machine learning library of data, can estimate a value for the recovery of biogas by performing an estimation process (broadly recited to be done in any manner). The claims do not recite any particular way that the estimation of the biogas value is being estimated, so there is nothing claimed but the end result to be achieved. A person can look at data and make an estimate of how much biogas they can recover based on the data they are considering and based on their past experience. The argument that a human cannot perform the abstract idea mentally is not persuasive.
The applicant argues that the claims include the use of a machine learning model where a device is used to learn setting values for the ML model based on the library and that this renders the claims eligible. The applicant argues that the machine learning is not claimed in a generic sense and makes reference to the figures 14 and 15 of the specification. This is not persuasive for two reasons. The first reason is that the figures are not claimed so that aspect of the argument is not commensurate with the claim scope. Second, the use of machine learning model is not considered to be part of the abstract idea by the examiner. The examiner has not taken the position that the machine learning model and the training of the model is a mental process. The use of the machine learning is a general link to a particular technological environment that is not sufficient to provide integration or significantly more as is set forth in the rejection of record.
On page 16-19 the applicant argues that the claims are integrated into a practical application. The applicant argues that the claims provide a technical solution to a technical problem, which is the estimation of a biogas recovery value. The estimation of the amount of biogas one can recover from waste material is not a technical problem where the claims serve to improve technology. The applicant argues that the vast amount of data to produce an accurate and efficient evaluation value cannot be done without a machine learning library. With respect to the claims, all that is required for the data is input data relating to a learning library for biogas materials or that is for property data relating to the biogas materials. The claim does not require a vast amount of data that can only be analyzed using technology via a technical solution because no specific data is claimed. The claimed estimation of the biogas recovery value is not improving technology in any manner because the claimed technology is not changed or made to operate more efficiently or in some improved manner. The result of the claim is a value for biogas recovery that only has meaning to a human being who is assessing the feasibility as to whether or not a given waste material will yield sufficient values of biogas to justify the processing of the material. The estimated biogas recovery value is not used to improve technology. The argument is not persuasive.
On page 19 the applicant argues that the claims provide for an inventive concept by estimating the evaluation value (biogas recovery) by using a trained machine learning model. This is not persuasive because as already been addressed, the use of machine learning is considered to be an additional element that amounts to an instruction for one to use a computer and to use machine learning as a tool to execute the steps that define the abstract idea. This is reciting a link to a particular technological environment, which is the use of machine learning by a computing device. See MPEP 2106.05(h). This does not result in the claims reciting significantly more. The use of machine learning that is being recited in a very general manner and at a high level of generality, is the equivalent to instructing one to use a computer and machine learning to accomplish the estimation of the biogas recover value. This does not render the claims eligible in view of MPEP 2106.05(f), and (h).
With respect to prior art, the applicant has changed the scope of the claims by deleting the claimed elements that were invoking 112f, and that required an interpretation of the claimed invention commensurate with the guidance from the specification, as was set forth by the examiner in the last office action. The currently pending claims as amended do not recite 112f language and have been treated under the broadest reasonable interpretation standard. The amendment materially changed the scope of the claims from requiring a 112f interpretation in light of the specification and the structure taught therein, to being interpreted under BRI. This necessitates the new ground of rejection applied to the claims.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to DENNIS WILLIAM RUHL whose telephone number is (571)272-6808. The examiner can normally be reached M-F 7am-3:30pm.
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If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Jessica Lemieux can be reached at 5712703445. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/DENNIS W RUHL/ Primary Examiner, Art Unit 3626