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 .
Status of Claims
This Office action is in response to correspondence received February 5, 2026.
Claims 1-20 are pending and have been examined.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claim(s) recite(s)
Claims 1 and 12 which are similar in scope:
A method for making a power origin label trustworthy and verifiable in an industrial context, the method comprising: obtaining the power origin label that carries information about one or more power origins of power used for production of a product; and adding a reference to metadata to the power origin label, the metadata providing evidence for the information about the one or more power origins.
The abstract idea describes a certain method of organizing human activity: following rules or instructions; or commercial interaction, which are non-exclusive. This is following rules or instructions because this is organizing information to verify a fact. It is noted that metadata is simply data about data, examples include information about a file size or information about who last saved a file, this is all information. This is a commercial interaction because it is verifying the origin of something that is paid for, like a label attesting to the quality of something. This is also a mental process as these are steps of observation and judgment, as one could observe the information claimed and then judge that the information says that the object in question is verified. Therefore, claims 1 and 12 recite an abstract idea that is a certain method of organizing human activity or a mental process.
This judicial exception is not integrated into a practical application. Claim 1 does not recite additional elements. Claim 12 recites A computer program product comprising instructions which, when executed by a computing system, enable and/or cause the computing system to perform a method for making a power origin label trustworthy and verifiable in an industrial context, the computer program product comprising: which amounts to apply it instructions to apply the abstract idea to a computer. In combination the elements of claim 12 amount to no more than a generic computer that is performing abstract idea steps. Taking the claim as a whole the additional elements are instructions to apply the abstract idea to a computer, without more. See MPEP 2106.05(f)(2). Therefore there is not a practical application of an abstract idea.
The claim(s) does/do not include additional elements that are sufficient to amount to significantly more than the judicial exception because the reasoning from the practical application section is carried over: for the same reasons that there is not a practical application of the abstract idea, there is not significantly more than the abstract idea.
Per the dependent claims:
Claims 2-11 and 13-20 further describe the abstract idea of claims 1 and 12. Cryptographic signature could be a series of letters and numbers, or anything with cryptography applied, which is a mathematical relationship. Likewise with cryptographic hash. A ledger could be a spreadsheet (paper). Therefore, claims 2-11 and 13-20, if incorporated into the independent claims, would not overcome the 101 rejection.
Therefore claims 1-20 are rejected under 35 USC 101.
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) 1, 4, 5, 8-12, 15, and 16 is/are rejected under 35 U.S.C. 102(a)(1), anticipated by Gutermuth et al., US PGPUB 20240426879 A1 (“Gutermuth”).
Per claims 1 and 12, which are similar in scope, Gutermuth teaches A method for making a power origin label trustworthy and verifiable in an industrial context, the method comprising: obtaining the power origin label that carries information about one or more power origins of power used for production of a product; in par 14: “Optionally, the energy ratio may be used to label produced goods or to categorize goods according to their energy ratio. The first energy source may be a PV energy source. The second energy source may be an electrical grid. The energy consuming asset may be in the present example a machine tool. The energy network model may be a time-dependent network graph model. The energy network model may comprise in the present example only the aforementioned two energy sources and the one energy consuming asset. Alternatively, the energy network model may comprise a plurality of further assets.” Produced goods teaches origins, label teaches label, origin taught by source.
Gutermuth then teaches and adding a reference to metadata to the power origin label, the metadata providing evidence for the information about the one or more power origins in par 029: “The embodiments propose a virtual tracking of the share of consumed and stored green energy at each point in time. In a further step, this may allow to generate energy consumption certificates for a site and consecutively for any kind of products, indicating how green the production process was (in terms of consumed energy). In case green products would profit from taxing benefits one day, the ability to generate those certificates may be of great importance.” See also par 035: “The benefits of the proposed solution may be: For any energy consuming asset/battery energy storage, a virtual break down of the consumed power/energy into green resp. non-green shares at any point in time may be available. This may help to track how much green power was used by the site directly, stored, and reused or pushed into the grid. In more detail, this information can be used to generate energy origin certificates for any kind of products, indicating a lower bound for the share of green energy that was utilized during production. Furthermore, a virtual discharge of the single shares in the battery energy storage can be modeled, while respecting the energy balance of each share.” Metadata taught here where the information about the power “at any point in time” is available. See also par 054: “Step 4: Computation of origin label/certificate. During the production of any single good, at each point in time different devices (i.e. energy consuming assets) may be involved to produce the good. The energy network model may compute the virtual power flows for each point in time, for each device the amount of (certainly) green and (possibly) non-green power that was consumed at each point in time may be determined. Since it can be tracked the duration of the employment of all single devices for the production of the single good, altogether it can easily be computed the amount of (certainly) green and (possibly) non-green energy (sum of integrals of corresponding power flows over corresponding durations), see also Step 3b. This way, the energy ratio (e.g. of green and non-green energy that was employed for the production process) may be determined. This energy ratio then may become part of the origin label.” This is where the information that is metadata is put on the label.
Per claims 4 and 15, which are similar in scope, which are similar in scope, Gutermuth teaches the limitations of claims 1 and 12, above. Gutermuth further teaches the metadata comprise an aggregation of a plurality of data points, wherein a data point of the plurality of data points is associated with a share of the power used for the production of the product, wherein the share is in a range from 0% to 100% in par 026: “FIG. 7 shows a graph energy network 600 for the production of single good. The energy network model 600 comprises in this example as first energy source 601 a PV system, as second energy source 602 an electrical grid, a battery energy storage 604 and as energy consuming asset 603 a machine tool. By applying the method described above, the controlling of the energy flows from the energy sources 601, 602 and the battery energy system 604 is possible and allows to produce products (e.g. milling parts) with a certain energy ratio and the determining of the energy ratio.” Energy ratio teaches 0 to 100.
Per claims 5 and 16, which are similar in scope, which are similar in scope, Gutermuth teaches the limitations of claims 1 and 12, above. Gutermuth further teaches wherein the metadata comprise an aggregation of a plurality of pieces of sub-metadata, wherein a piece of sub-metadata of the plurality of pieces of sub-metadata is associated with a share of the power used for the production of the product, wherein the share is in a range from 0% to 100% in par 026: “FIG. 7 shows a graph energy network 600 for the production of single good. The energy network model 600 comprises in this example as first energy source 601 a PV system, as second energy source 602 an electrical grid, a battery energy storage 604 and as energy consuming asset 603 a machine tool. By applying the method described above, the controlling of the energy flows from the energy sources 601, 602 and the battery energy system 604 is possible and allows to produce products (e.g. milling parts) with a certain energy ratio and the determining of the energy ratio.” Energy ratio teaches 0 to 100. Under a broadest reasonable interpretation, this information teaches sub metadata as it is information about the production of the equipment, information about information, and is a part of the information (sub) of the graph energy network.
Per claim 8, Gutermuth teaches the limitations of claim 1, above. Gutermuth further teaches the metadata comprises at least one of: measurements from power management systems in a factory associated with the production of the product, inverters of a photovoltaic installation associated with the power used for the production, power consumption of one or more machines associated with the production of the product, and a weather report indicative of one or more environmental conditions associated with the production of the product in par 33: “For each power generating asset (i.e. energy source) (renewables, . . . )—except for the grid—and for the battery energy storage, two virtual power lines may be added to any possible power receiver: one referring to (certainly) green energy and one for (possibly) non-green energy, that may be produced resp. released. Here, e.g., for renewable energy sources, the ‘non-green’ part may correspond to the uncertain production due to their stochastic nature. In case information on the shares of green or non-green power that is transferred from the grid to the assets is available, the method may proceed similarly for the grid. Otherwise, for the grid only one virtual power line to each possible receiver may be added. This may allow to virtually track the way of the green and non-green power “end-to-end” through the system. In particular, the energy stored in the battery energy storage depending on its energy source (green or non-green) can also be broken down, as well as the consumed power or energy at any point in time. Furthermore, the modeling may enable to virtually choose the share of green energy that is released from the battery energy storage at any point in time, depending on the shares of stored energy (e.g., one cannot release green energy anymore if the corresponding share is zero). In an extreme scenario, this would enable two options: 1) The production of goods employing 50% green energy for all goods; and 2) The alternating production of goods employing purely green and purely non-green energy.”
Per claim 9, Gutermuth teaches the limitations of claim 1, above. Gutermuth further teaches the metadata comprises at least one of: verifiable data, non-verifiable data, and power origin labels associated with an origin of the metadata in par 35: “For any energy consuming asset/battery energy storage, a virtual break down of the consumed power/energy into green resp. non-green shares at any point in time may be available. This may help to track how much green power was used by the site directly, stored, and reused or pushed into the grid. In more detail, this information can be used to generate energy origin certificates for any kind of products, indicating a lower bound for the share of green energy that was utilized during production. Furthermore, a virtual discharge of the single shares in the battery energy storage can be modeled, while respecting the energy balance of each share. Moreover, the real battery could be spitted into two dynamic parts. One that stores the local green power for further usage and the other part helps to stabilize the grid.”
Per claim 10, Gutermuth teaches the limitations of claim 1, above. Gutermuth further teaches empowering an auditor to verify an accuracy and an authenticity of data that went into a generating of the power origin label and/or to verify a labelling process that was performed for the generating of the power origin label, wherein the empowering is based on enabling the auditor to access one or more pieces of the metadata that is associated with the power origin label based on the added reference in par 54: “Step 4: Computation of origin label/certificate. During the production of any single good, at each point in time different devices (i.e. energy consuming assets) may be involved to produce the good. The energy network model may compute the virtual power flows for each point in time, for each device the amount of (certainly) green and (possibly) non-green power that was consumed at each point in time may be determined. Since it can be tracked the duration of the employment of all single devices for the production of the single good, altogether it can easily be computed the amount of (certainly) green and (possibly) non-green energy (sum of integrals of corresponding power flows over corresponding durations), see also Step 3b. This way, the energy ratio (e.g. of green and non-green energy that was employed for the production process) may be determined. This energy ratio then may become part of the origin label.”
Per claim 11, Gutermuth teaches the limitations of claim 1, above. Gutermuth further teaches the adding the reference to the metadata further comprises adding the reference to one or more pieces of the metadata, wherein the one or more pieces of the metadata are a share of the metadata that is associated with the power origin label in par 55: “Further benefits include that by adding further constraints to the model, the described mechanism can not only be used for the generation of a single good and track its individual energy contributions, but also in a more advanced setup, where two (or more) goods may be produced, but all green energy may be used for product 1, and all possibly non-green energy for product 2. This way, different markets could be addressed. Remark, that the solution may use green energy from own PV production as well as from the grid, where the device the code may be running (resp., the solution may be computed) on may use an API to read the current green energy share and may calculate as above the certainly green share and the none-green share. Hence, it may allow for virtual separation of the energy resp. power origin.”
Therefore, claims 1, 4, 5, 8-12, 15, and 16 are rejected under 35 USC 102.
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.
Claim(s) 2, 3, 13, and 14 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gutermuth et al., US PGPUB 20240426879 A1 (“Gutermuth”) in view of Miller et al., US PGPUB 20210142426 A1 (“Miller”)
Per claims 2 and 13, which are similar in scope, Gutermuth teaches the limitations of claims 1 and 12, above. Gutermuth does not teach adding, to the power origin label, a cryptographic signature, and/or committing the power origin label and the metadata to a ledger-based evidence collection platform.
Miller teaches generating energy blocks on a blockchain corresponding to generation, transmission, and consumption of predetermined quanta of energy represented by corresponding records. See abstract.
Miller teaches adding, to the power origin label, a cryptographic signature, and/or committing the power origin label and the metadata to a ledger-based evidence collection platform in par 36: “In various embodiments, once an individual energy data record reaches a top of a queue in transaction store broker 190, the broker 190 transmits the currently queued-up energy data record to transaction store service 200 in Merkle engine 188. The transaction store service 200 may be implemented, by way of example and not limitation, as a cloud container instance in some embodiments. The transaction store service 200 is configured to receive an energy data record (representing a specific unit of energy) transmitted from the broker 190, identify the source of the energy data record (e.g., whether it originated from a specific energy generation facility 101A, a specific consumption facility 101B), and perform validation operations on the energy data record. The transaction store service 200 is also configured to hash the record (e.g., using one or more secure hash algorithm (SHA), message digest (MD, such as MD5), BLAKE (such as BLAKE3), and other appropriate hashing and hash functions) to create a hash “signature” of the record. In various embodiments, the transaction store service 200 may function as a deduplication engine to deduplicate messages (e.g., messages received from the same source for an identical time and energy value).” See also par 37.
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the energy source labeling teaching of Gutermuth with the using a ledger and cryptographic signature teaching of Miller because Miller teaches in par 08 that: “For example, some embodiments may advantageously provide a technological solution to the problem in the industry of accurately and reliably tracking physical movement of energy from generation through transmission to consumption across one or more physical power distribution networks and associated data communication, processing, and control networks. Various embodiments may advantageously generate secure, immutable, verifiable digital energy data assets such as, by way of example and not limitation, blockchain tokens.” These solutions as taught would motivate one ordinarily skilled to combine the arts because one would want to track energy better though various networks including “processing” networks (like those that produce articles). For these reasons one would be motivated to modify Gutermuth with Miller.
Per claims 3 and 14, which are similar in scope, Gutermuth teaches the limitations of claims 1 and 12, above. Gutermuth does not teach the adding the reference comprises constructing the reference as a cryptographic hash that is computed over the metadata; and adding, to the power origin label, the cryptographic hash.
Miller teaches the adding the reference comprises constructing the reference as a cryptographic hash that is computed over the metadata; and adding, to the power origin label, the cryptographic hash in par 58: “The energy block generated in step 435 is subsequently enriched by associating 440 scheduling, delivery, and consumption data. By way of example and not limitation, the associated consumption data, for example, may represent energy consumption data record(s) (ECDR(s)). ECDRs may, similar to EGDRs as described in relation to FIG. 4, be transformed according to a predetermined data structure, hashed, stored in a Merkle trie (which may, by way of example and not limitation, be the same Merkle trie as the EGDRs are stored on or a separate Merkle trie), and used to generated digital energy asset tokens. The resultant consumption-related tokens may be associated, for example, with generation data (e.g., EGDRs or energy generation block(s) generated therefrom). Consumption-related tokens may, for example, be associated with correlated energy generation block(s) by an identifying hash or other identifier with the energy block. Similarly, transmission (e.g., scheduling and delivery data) data may be similarly hashed and stored, or may be associated (e.g., as metadata) with corresponding energy blocks.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the energy source labeling teaching of Gutermuth with the using a ledger and cryptographic signature teaching of Miller because Miller teaches in par 08 that: “For example, some embodiments may advantageously provide a technological solution to the problem in the industry of accurately and reliably tracking physical movement of energy from generation through transmission to consumption across one or more physical power distribution networks and associated data communication, processing, and control networks. Various embodiments may advantageously generate secure, immutable, verifiable digital energy data assets such as, by way of example and not limitation, blockchain tokens.” These solutions as taught would motivate one ordinarily skilled to combine the arts because one would want to track energy better though various networks including “processing” networks (like those that produce articles). For these reasons one would be motivated to modify Gutermuth with Miller.
Claim(s) 6, 7, 17, and 18 is/are rejected under 35 U.S.C. 103 as being unpatentable over Gutermuth et al., US PGPUB 20240426879 A1 (“Gutermuth”) in view of Fokue et al., US PGPUB 20120066167 A1 (“Fokue”).
Per claims 6 and 17, which are similar in scope, Gutermuth teaches the limitations of claims 5 and 16, above. Gutermuth does not teach wherein data points of the plurality of data points have different trustworthiness, and wherein pieces of sub-metadata of the plurality of pieces of sub-metadata have different trustworthiness.
Fokue teaches techniques for assessing trust in information. See abstract.
Fokue teaches wherein data points of the plurality of data points have different trustworthiness, and wherein pieces of sub-metadata of the plurality of pieces of sub-metadata have different trustworthiness in par 20: “] Accordingly, in one or more embodiments of the invention, the trust computation model works as follows. First, a probability measure is computed for each justification as the sum of the probabilities associated with possible worlds in which the justification is present (namely, all the axioms in the justification are present). Second, the degree of inconsistency is partitioned across all justifications. For instance, if a justification J.sub.i is present in 80% of the possible worlds, then it is assigned four times the blame as a justification J.sub.2 that is present in 20% of the possible worlds. Third, the penalty associated with a justification is partitioned across all axioms or sources in the justification using a biased (on prior beliefs in trust assessment) or an unbiased partitioning scheme. Note that there may be alternate approaches to derive trust scores from inconsistency measures and justifications, and the techniques detailed herein are flexible and extensible to such trust computation models.”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the energy source labeling teaching of Gutermuth with the data trustworthiness teaching of Fokue because Fokue teaches in par 04 that : “In such scenarios, a decision maker (a human or a software agent alike) is faced with the challenge of examining large volumes of information originating from heterogeneous sources with the goal of ascertaining trust in various pieces of information. A common data model subsumed by several trust computation models is the ability of an entity (for example, an information source) to assign a numeric trust score to another entity. In existing approaches, such pair-wise numeric ratings contribute to a (dis)similarity score (for example, based on L.sub.1 norm, L.sub.2 norm, cosine distance, etc.) which can be used to compute personalized trust scores or recursively propagated throughout the network to compute global trust scores.” As a decisionmaker including an automated decisionmaker has to deal with different sources of information, one would be motivated by this teaching to combine the references so that appropriate weights could be applied to the different sources. This would make the data ultimately more reliable as it would convey how trustworthy the data was. For these reasons one would be motivated to modify Gutermuth with Fokue.
Per claims 7 and 18, which are similar in scope, Gutermuth teaches the limitations of claims 6 and 17, above. Gutermuth does not teach aggregated data points in the aggregation of the plurality of data points have heterogenous trust levels, and wherein aggregated pieces of sub-metadata in the aggregation of the plurality of pieces of sub-metadata have heterogenous trust levels.
Fokue teaches aggregated data points in the aggregation of the plurality of data points have heterogenous trust levels, and wherein aggregated pieces of sub-metadata in the aggregation of the plurality of pieces of sub-metadata have heterogenous trust levels in par 72: “As detailed herein, one or more embodiments of the invention include a trust computation model. The problem of assessing trust in a set IS that includes n information sources can be formalized as follows. The trust value assumed or known prior to any statement made by an information source i is specified by a probability distribution PrTV(i) over the domain [0, 1]. For example, a uniform distribution is often assumed for new information source for which there is no prior knowledge. Statements made by each information source i is specified in the form of a probabilistic knowledge base K.sub.i=(T.sup.i, A.sup.i, BN.sup.i). The knowledge function C maps an information source i to the probabilistic knowledge base K, capturing all of its statements. The trust update problem is a triple (IS, PrTV, C) where IS includes information sources, PrTV is a prior trust value function over IS which maps a source i to a probability distribution PrTV(i) over the domain [0, 1], and C is a knowledge function over IS. A solution to a trust update problem is given by the posterior trust value function PoTV. PoTV maps an information source i to a probability distribution over the domain [0, 1], which represents a new belief in the trustworthiness of i after processing statements in .orgate..sub.j.di-elect cons.ISC(j).”
It would have been obvious to one ordinarily skilled in the art before the effective filing date of the claimed invention to modify the energy source labeling teaching of Gutermuth with the data trustworthiness teaching of Fokue because Fokue teaches in par 04 that : “In such scenarios, a decision maker (a human or a software agent alike) is faced with the challenge of examining large volumes of information originating from heterogeneous sources with the goal of ascertaining trust in various pieces of information. A common data model subsumed by several trust computation models is the ability of an entity (for example, an information source) to assign a numeric trust score to another entity. In existing approaches, such pair-wise numeric ratings contribute to a (dis)similarity score (for example, based on L.sub.1 norm, L.sub.2 norm, cosine distance, etc.) which can be used to compute personalized trust scores or recursively propagated throughout the network to compute global trust scores.” As a decisionmaker including an automated decisionmaker has to deal with different sources of information, one would be motivated by this teaching to combine the references so that appropriate weights could be applied to the different sources. This would make the data ultimately more reliable as it would convey how trustworthy the data was. For these reasons one would be motivated to modify Gutermuth with Fokue.
Therefore, claims 2, 3, 6, 7, 13, 14, and 17-20 are rejected under 35 USC 103.
Prior Art Made of Record
The following prior art is considered relevant to Applicant’s disclosure but is not relied on in the above rejection:
Palanchian et al., US PGPUB 20090125436 A1, teaches a calculator to determine the amount of green energy used in a practice see par 53. Further teaches the creation of renewable energy certificates see par 60 that represents a specific amount of green energy used in a process. This teaches and adding a reference to metadata to the power origin label, the metadata providing evidence for the information about the one or more power origins, in claims 1 and 12, because the certificate is data about the data of the process.
EKOenergy ecolabel, [online], available at: < https://web.archive.org/web/20241114190134/https://www.ekoenergy.org/ecolabel/ >
Archived on November 14, 2024.
Teaches on page 1: “the origin of the energy is reliably tracked and double counting of environmental attributes is excluded (i.e. we are absolutely sure that only 1 person or company can claim a specific MWh of renewable energy). Therefore, we only accept instruments that fulfil the quality criteria of the Greenhouse Gas Protocol Scope 2 Guidance. In Europe, energy is tracked with Guarantees of Origin, as regulated by European legislation. In Northern America, the origin of EKOenergy is proven with RECs (Renewable Energy Certificates). Elsewhere we accept other reliable methods of energy tracking, for example, national Energy Attribute Certificate schemes or I-RECs. The production and consumption of energy have to take place within the same market boundaries.” This teaches a label for origin of energy per claims 1 and 12.
Conclusion
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/RICHARD W. CRANDALL/ Primary Examiner, Art Unit 3619
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