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 the Claims
The Amendment filed on 09/08/2025 has been entered. Claims 1-20 are pending in the instant patent application. Claims 1, 8 and 15 have been amended. This Final Office Action is in response to the claims filed.
Response to Claim Amendments
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §101 rejections. The rejections remain pending and are updated and addressed below in light of the amendments and per guidelines for 101 analysis (PEG 2019).
Applicant’s amendments to the claims are insufficient to overcome the 35 U.S.C. §103 rejections. The rejections remain pending and are updated and addressed below in light of the amendments.
Response to 35 U.S.C. §101 Arguments
Applicant’s arguments regarding 35 U.S.C. §101 rejection of the claims have been fully considered, but are not persuasive. The claims as presently amended still recite abstract ideas. Examiner respectfully reminds Applicant, general purpose computer elements/structure, similar to the claimed invention, used to apply a judicial exception, by use of instruction implemented on a computer, has not been found by the courts to integrate the abstract idea into a practical application; see MPEP 2106.05(f). Furthermore, the courts have found claims requiring a generic computer or nominally reciting a generic computer may still recite a mental process even though the claim limitations are not performed entirely in the human mind.
Regarding Applicant’s assertion that the present claims are subject-matter eligible under Prong 2, Examiner respectfully disagrees. The additional elements present in the claims do no integrate the abstract idea into a practical application because they do not impose any meaningful limits on practicing the abstract idea. Furthermore, the generic recitation of a neural network is also merely being used as a tool to carry out the abstract idea, there is in at least no detailed functionality regarding the neural network. Thus, the claim as presently written is directed to abstract ideas. Furthermore, as previously stated, Examiner maintains that the specification sets forth an improvement, but in a conclusory manner and furthermore the claims do not reflect the disclosed improvement or effectively demonstrate an improvement to existing technology. In addition, as previously stated, the use of the neural network, as merely recited in the claims, does not incorporate how it is being leveraged. There is no training of data, refining of data or anything of the sort done by the neural network that could perhaps recite some form of improvement to the technology or significantly more. Examiner maintains that the elements are performing generic computing functions as well as limitations that recite computer functions that the courts have recognized as well-understood, routine and conventional.
Regarding Applicant’s assertion that the claims as presently written, are similar to Example 40, Examiner respectfully disagrees. In Example 40, although each of the collecting steps analyzed individually may be viewed as mere pre- or post-solution activity, the claim as a whole is directed to a particular improvement in collecting traffic data. Specifically, the method limits collection of additional Netflow protocol data to when the initially collected data reflects an abnormal condition, which avoids excess traffic volume on the network and hindrance of network performance. The collected data can then be used to analyze the cause of the abnormal condition. This provides a specific improvement over prior systems, resulting in improved network monitoring. The claim as a whole integrates the mental process into a practical application. Thus, the claim is eligible because it is not directed to the recited judicial exception. In contrast, the claims as currently written do not provide any specific improvement to the technology or prior systems. Examiner respectfully reminds Applicant, regardless of the complexity and/or granularity of the type of data, computational data analysis without meaningful limitations within the claims that amount to significantly more is a judicial exception (i.e. abstract idea).
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.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 1, claim 1 recites to receive real-time ESG compliance information associated with a ESG compliance regulation, the ESG compliance regulation comprising a plurality of attributes, the ESG compliance information including sensor data, the sensor data including an amount of emitted greenhouse gas; perform real-time analysis of the ESG compliance regulation based on the received ESG compliance information, the analysis including comparing the sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation, the at least one attribute including an allowed amount of greenhouse gas emissions; in response to the comparison of the at least one sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation indicating a break in compliance with the ESG compliance regulation, the break in compliance including an excessive amount of greenhouse gas emissions compared to the allowed amount, automatically execute a smart contract, the smart contract causing a purchase of a carbon offset; determine and assign values to the plurality of attributes of the ESG compliance regulation based on the analysis of the ESG compliance information and the purchase of the carbon offset, wherein each ESG compliance regulation attribute is given a weighting relative to the other plurality of attributes; determine an ESG compliance rating based on the score and a mapping of score ranges to ESG compliance ratings.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the fundamental economic practices/principles taking place. Furthermore, they also fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment). Furthermore, the generic recitation of a neural network does not take the claim out of reciting abstract ideas.
Accordingly, the claim recites abstract ideas and dependent claims 2-7 further recite the abstract ideas.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of at least one processor, a neural network, a client computing device, at least one sensor, a memory device and determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation. The at least one processor, a neural network, a client computing device, at least one sensor, a memory device and determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation are merely generic computing devices. In addition, the “provide, on a graphical user interface...” limitation recites insignificant post solution activity.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 1 includes various elements that are not directed to the abstract idea under 2A. These elements include at least one processor, a memory device, neural network, a client computing device, at least one sensor, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and the generic computing elements described in the Applicant's specification in at least Para 0076-0107. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. Furthermore, the “receiving” limitation of Claim 1 recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed ina merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 1 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 8-14, they are directed to a method, however the claims are directed to a judicial exception without significantly more. Claims 8-14 are directed to the abstract idea of generating environmental, social and governance ratings for ESG compliance.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 8, claim 8 recites receiving
real-time ESG compliance information associated with a ESG compliance regulation, the ESG compliance regulation comprising a plurality of attributes, the ESG compliance information including sensor data, the sensor data including an amount of emitted greenhouse gas; performing real-time analysis of the ESG compliance regulation based on the received ESG compliance information, the analysis including comparing the sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation, the at least one attribute including an allowed amount of greenhouse gas emissions; in response to the comparison of the at least one sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation indicating a break in compliance with the ESG compliance regulation, the break in compliance including an excessive amount of greenhouse gas emissions compared to the allowed amount, automatically execute a smart contract, the smart contract causing a purchase of a carbon offset; determining and assign values to the plurality of attributes of the ESG compliance regulation based on the analysis of the ESG compliance information and the purchase of the carbon offset, wherein each ESG compliance regulation attribute is given a weighting relative to the other plurality of attributes; determining an ESG compliance rating based on the score and a mapping of score ranges to ESG compliance ratings.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the fundamental economic practices/principles taking place. Furthermore, they also fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment).
Furthermore, the generic recitation of a neural network does not take the claim out of reciting abstract ideas.
Accordingly, the claim recites abstract ideas and dependent claims 9-14 further recite the abstract ideas.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of a client computing device, neural network, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and at least one sensor. The client computing device, neural network, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and at least one sensor are merely generic computing devices.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 8 includes various elements that are not directed to the abstract idea under 2A. These elements include a client computing device, neural network, at least one sensor, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and the generic computing elements described in the Applicant's specification in at least Para 0076-0107. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. Furthermore, the “receiving” limitation of Claim 8 recites computer functions that the courts have recognized as well- understood, routine, and conventional functions when they are claimed ina merely generic manner (e.g., at a high level of generality) (Gee MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 8 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Regarding Claims 15-20, they are directed to a non-transitory computer readable medium, however the claims are directed to a judicial exception without significantly more. Claims 15-20 are directed to the abstract idea of generating environmental, social and governance ratings for ESG compliance.
Performing the Step 2A Prong 1 analysis while referring specifically to independent Claim 10, claim 10 recites to receive real-time ESG compliance information associated with a ESG compliance regulation, the ESG compliance regulation comprising a plurality of attributes, the ESG compliance information including sensor data, the sensor data including an amount of emitted greenhouse gas; perform real-time analysis of the ESG compliance regulation based on the received ESG compliance information, the analysis including comparing the sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation, the at least one attribute including an allowed amount of greenhouse gas emissions; in response to the comparison of the at least one sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation indicating a break in compliance with the ESG compliance regulation, the break in compliance including an excessive amount of greenhouse gas emissions compared to the allowed amount, automatically execute a smart contract, the smart contract causing a purchase of a carbon offset; determine and assign values to the plurality of attributes of the ESG compliance regulation based on the analysis of the ESG compliance information and the purchase of the carbon offset, wherein each ESG compliance regulation attribute is given a weighting relative to the other plurality of attributes; determine an ESG compliance rating based on the score and a mapping of score ranges to ESG compliance ratings.
These claim limitations fall within the Certain Methods of Organizing Human Activity grouping of abstract ideas due to the fundamental economic practices/principles taking place. Furthermore, they also fall within the Mental Processes grouping of abstract ideas for they are concepts that can be performed in the human mind and/or with pen/paper (including an observation, evaluation, judgment).
Furthermore, the generic recitation of a neural network does not take the claim out of reciting abstract ideas.
Accordingly, the claim recites abstract ideas and dependent claims 16-20 further recite the abstract ideas.
Regarding Step 2A Prong 2 analysis, the judicial exception is not integrated into a practical application. In particular the claim recites the elements of at least one processor, neural network, a client computing device, at least one sensor, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and a memory device. The at least one processor, neural network, client computing device, at least one sensor, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and a memory device are merely generic computing devices. In addition, the “provide, on a graphical user interface...” limitation recites insignificant post solution activity.
With respect to 2B, the claims do not include additional elements amounting to significantly more than the abstract idea. Claim 15 includes various elements that are not directed to the abstract idea under 2A. These elements include at least one processor, a memory device, a client computing device, neural network, at least one sensor, determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation and the generic computing elements described in the Applicant's specification in at least Para 0076- 0107. These elements do not amount to more than the abstract idea because it is a generic computer performing generic functions. Furthermore, the “receiving” limitation of Claim 15 recites computer functions that the courts have recognized as well-understood, routine, and conventional functions when they are claimed in a merely generic manner (e.g., at a high level of generality) (See MPEP 2106.05(d)(ii)...at least, Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information).
Therefore, Claim 15 is not drawn to eligible subject matter as it is directed to abstract ideas without significantly more.
Response to 35 U.S.C. §103 Arguments
Applicant’s arguments regarding 35 U.S.C. §103 rejection of the claims have been fully considered, but are not persuasive. Furthermore, Applicant’s arguments are moot in light of newly amended language.
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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) 1-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Yan et al. (US 2022/0343433 A1) in view of Lancaster et al. (US 2023/0401508 A1) in view of Abbott et al. (US 2024/0161126 A1) further in view of Abramowitz (US 2015/0254766 A1).
Regarding Claim 1, Yan teaches the limitations of Claim 1 which state,
receive from a client computing device, ESG compliance information associated with a ESG compliance regulation (Yan: Para 0049, 0080, 0146 via the ESG rankings dataset leverages Dun & Bradstreet data, which is real data collected on and from companies. Other data sources, such as news and company reports, are triangulated with additional data collected by Dun & Bradstreet in order to confirm their veracity. The variable, GHG emissions, which is infrequently disclosed is modeled fora subset of companies using numerous firm -specific variables...Operation 215 receives data from data sources 305, which include data sources 310, 315, 335 and 340...Data is first sourced through internal Dun & Bradstreet databases using analytical tools. This data was complemented with data from government sources (e.g., U.S. Environmental Protection Agency (EPA) compliance and environmental pollutant data), public sources (e.g., company reports and filings), news (e.g., processed through D&B Hoovers), and some third - party licensed data (e.g., aggregation of sustainability reports, GHG emissions from CDP));
perform analysis of the ESG compliance regulation based on the received ESG compliance information (Yan: Para 0058 via The ESG rankings dataset's topic architecture was created by referencing several of the leading ESG standards. Data is sourced, collected, and quality-checked through various processes. In preparation for analytical modeling and calculations, the data is further normalized, processed, and weighted. The outputs are various ESG-related rankings as well as overall scores. The ESG outputs are calculated to create data that is normally distributed between 1, indicating low risk or best performance, and 5, indicating high risk or worst performance).
However, Yan does not explicitly disclose the limitations of Claim 1 which state receiving real-time ESG compliance information, the ESG compliance regulation comprising a plurality of attributes, the ESG compliance information including sensor data captured by at least one sensor, the sensor data including an amount of emitted greenhouse gas; performing real-time analysis of the ESG compliance regulation, the analysis including comparing the sensor data captured by the at least one sensor to at least one attribute of the plurality of attributes of the ESG compliance regulation, the at least one attribute.
Lancaster though, with the teachings of Yan, teaches of
receiving real-time ESG compliance information (Lancaster: Para 0023, 0025, 0028, 0036 via the first crew and second crew may each be registered with the platform in a manner that may allow for real-time generation of the dynamic compliance profile… Using the platform of the present disclosure, team member tracking, safety and job step compliance, and emissions monitoring is captured in real-time… Additionally or alternatively, the hazards may correspond to atmospheric or other sensors that are configured to relay real-time information regarding field conditions to a central platform… The onsite dashboard 304b may be configured to receive one or more inputs from users on the jobsite (including inputs form sensors) in order to generate the various compliance metrics described herein, including those compliance metrics associated with the team profile, the work profile, and/or the emissions profile. In the example of FIG. 3B, the onsite dashboard 304b is shown associated with function 308b, at which a user can obtain site-specific information, such as from an owner or operator of the jobsite. The onsite dashboard 304b is further shown associated with function 312b, at which a user can obtain information associated with hazards specific to the jobsite, including real-time alerts regarding the risks present on the jobsite).
the ESG compliance regulation comprising a plurality of attributes, the ESG compliance information including sensor data captured by at least one sensor, the sensor data including an amount of emitted greenhouse gas (Lancaster: Para 0033 via The system 200 of FIG. 2 is further shown as including a dynamic compliance profile 236 generated as a result of the operations of the platform 200 described herein. Broadly, the dynamic compliance profile may be generated by receiving a plurality of jobsite inputs at the jobsite associated with one or more of the team profile 208, the work profile 216, and/or the emissions profile 228. For example, with reference to the team profile 208, the platform 204 may be configured to generate one or more team members compliance metrics in response to an input associated with a physical presence of a team member on the jobsite...With reference to the emissions profile 228, the platform 204 may be configured to generate one or more emissions compliance metrics in using data from the sensor array 232. In some cases, the emission compliance metric may be calculated by comparing the data from the sensor array 232 toa base or target value and determining whether the data meets or exceed certain ESG criteria).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yan, with the teachings of Lancaster in order to have receiving real-time ESG compliance information, the ESG compliance regulation comprising a plurality of attributes, the ESG compliance information including sensor data captured by at least one sensor, the sensor data including an amount of emitted greenhouse gas; performing real-time analysis of the ESG compliance regulation, the analysis including comparing the sensor data captured by the at least one sensor to at least one attribute of the plurality of attributes of the ESG compliance regulation, the at least one attribute. The motivations behind this being using attributes at a job site and sensors to determine whether there is ESG compliance or non-compliance. In addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Furthermore, Yan does not explicitly disclose the limitations of Claim 1 which state in response to the comparison of the at least one sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation indicating a break in compliance with the ESG compliance regulation, automatically execute a smart contract, the smart contract causing a purchase of a carbon offset.
Abbott though, with the teachings of Yan/Lancaster, teaches of
in response to the comparison of the at least one sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation indicating a break in compliance with the ESG compliance regulation, automatically execute a smart contract, the smart contract causing a purchase of a carbon offset (Abbott: Para 0013, 0047, 0050 via the disclosed systems and methods may implement smart contracts. Smart contracts are programs stored on a blockchain that run when predetermined conditions are met. They typically are used to automate the execution of an agreement so that all participants can be immediately certain of the outcome, without any intermediary's involvement or time loss. In some embodiments, a smart contract tied to the DLT automatically authorizes payments to the client for the purchase of carbon credits or offsets created based on the determination of buyers and sellers of such credits either through direct interactions or system auctions held by the sellers of such carbon credits or offsets. The value of the carbon credits or offsets will be based on the energy saved or generated based on the reporting tied to the credits and other benefits associated with the energy usage... The data 216 is then run through predictive analytics 220 to compare the energy usage against the calculations of the energy usage utilizing the previous installed or replaced equipment or other data from third party sources, the specifications of which may be stored also in DLT 218 in the cloud-based environment. The difference calculated between the actual energy savings or usage and the predicted energy savings or usage is then also verified and validated. This data 216 may be collected continuously through a defined term (e.g., one month), at which point the total usage or savings for such period will be determined through a smart contract 113 tied to the DLT 218, based on contract parameters. The smart contract 113 can generate and publish the data and demonstrate validation through the DLT... The DLT 218 is utilized to store the data needed to facilitate creation, trading and redemption of tokens that represent the actual energy usage or savings versus the predicted energy usage or savings. The smart contracts automatically process payments to the carbon credit or offset buyer based on the value of the credit calculated and the relevant parameters agreed and incorporated in the smart contract).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yan/Lancaster with the teachings of Abbott, in order to have in response to the comparison of the at least one sensor data to at least one attribute of the plurality of attributes of the ESG compliance regulation indicating a break in compliance with the ESG compliance regulation, automatically execute a smart contract, the smart contract causing a purchase of a carbon offset. The motivations behind this being to incorporate the teachings of verifying, validating, tracking, and trading carbon credits utilizing smart meter technology tied into a distributed ledger systems and methods. In addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
The combination of Yan/Lancaster/Abbott further teaches the limitations of Claim 1 which state
the break in compliance including an excessive amount of greenhouse gas emissions compared to the allowed amount (Lancaster: Para 0033 via the emission compliance metric may be calculated by comparing the data from the sensor array 232 to a base or target value and determining whether the data meets or exceed certain ESG criteria).
In addition, Yan does not explicitly disclose the limitations of Claim 1 which state determine and assign values to a plurality of attributes of the ESG compliance regulation based on the analysis of the ESG compliance information and the purchase of the carbon offset, wherein each ESG compliance regulation attribute is given a weighting relative to the other plurality of attributes; determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation; determine an ESG compliance rating based on the score and a mapping of score ranges to ESG compliance ratings and provide, on a graphical user interface configured to display, the ESG compliance rating.
Abramowitz though, with the teachings taught by Yan/Lancaster/Abbott, teaches of
determine and assign values to a plurality of attributes of the ESG compliance regulation based on the analysis of the ESG compliance information and the purchase of the carbon offset, wherein each ESG compliance regulation attribute is given a weighting relative to the other plurality of attributes (Abramowitz: Para 0020-0021 via the algorithm received weights for each bond attribute. For example, the list of attributes fed to the algorithm may include cash flows and may also include a weight of 25% for the cash flow attribute. The weight specifies the level of important of the weighted attribute. For example, a weight of 25 out of 100 possible means that the attribute given that weight has an importance of 25% compared to the remaining attributes. As another example, cash flows are assigned a weight of 25, a profitability attribute is assigned a weight of 25, and a corporate structure attribute is assigned a weight Thus, in this example, the corporate structure attribute is 100 percent more important than either the cash flows or profitability attributes. Also, in a similar fashion, the weights are user-configurable as are specifying the attributes. That is, a user can enter the amount of weight for each specified attribute or can select from a list of available weight values... the attributes and weights are configurable so that the algorithm captures the factors which the user believes can drive a bond to get upgraded or downgraded);
determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation (Abramowitz: Para 0022, 0027 via The algorithm incorporates a neural network or other machine learning model that, based on in part but not limited to a comparison of input bond data that includes bond credit ratings with past or predicted bond data that includes bond credit ratings, adjust the weight parameters as necessary to improve the accuracy of the credit rating computation...The algorithm generates a score based on the values of the weighted attributes);
determine an ESG compliance rating based on the score and a mapping of score ranges to ESG compliance ratings (Abramowitz: Para 0034 via In another embodiment, the bond attribute values or weights can be entered. For example, an bond analyst can decide to enter a particular value for an attribute or weight. The weighted attributes are used by the algorithm to generate a score and the score is used to determine a bond rating. In an embodiment, because the level of granularity of the attributes, their values, the weights, their values, and any intermediary values are important, slight changes in bond values can produce Slightly different scores. However, a user or the algorithm may determine that certain differences are negligible or otherwise unimportant and should not be counted. Thus, an embodiment provides a mapping of ranges of scores to credit ratings. For example, in FIG. 5, although Bond 1 and Bond 2 have different scores, namely, 37 and 39, respectively, Bond 1 and Bond 2 have the same credit rating, namely, 7. Thus, in this example, both 37 and 39 get mapped to credit rating 7. In an embodiment, the score values, ranges, credit rating values, and mapping of score ranges to credit ratings are configurable);
provide, on a graphical user interface configured to display, the ESG compliance rating (Abramowitz: Para 0064, 0069 via An embodiment can be understood with reference to FIG. 9A-9D, tables of bond portfolios and corresponding bond attributes used as input into a dynamic bond rating service and the respective output tables. For Table 9A, a user interface of a dynamic bond credit rating system such as that in FIG. 1 is provided for customers to input bond portfolio information for a corresponding bond portfolio. The bond portfolio information can include but is not limited to bond attribute values for the bonds such as price. Then, responsive to receiving the bond portfolio information, the system applies the received bond portfolio information to a dynamic bond credit rating algorithm that dynamically generates the bond credit ratings for each bond of the portfolio. Then the credit ratings are output, e.g. in a table such as that shown in FIG. 9B... An embodiment can be understood with reference to FIG. 10, a schematic diagram showing the percent change in the dynamic credit rating for Bond A. In this embodiment, a user interface is provided to allow a user to configure input prediction parameters for one or more bonds. In the example in FIG. 10, the user chose time intervals of 3 months, 6 months, and 1 year for seeing any change in the attribute, dynamic credit rating. In the embodiment, a graph is generated which plots the percentage change in the dynamic credit rating over the specified time intervals. Thus, at a glance, the user can see an upward trend or increase in the credit rating for Bond A. It should be appreciated that when using the granular credit rating system described herein, the graph shows change that might not be detected when plotting the bond credit ratings as computed by the standard credit rating agencies. Thus, the granularity of the dynamic bond credit rating system allows a user to make a more informed decision regarding his or her held bonds).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to modify Yan/Lancaster/Abbott with the teachings of Abramowitz in order to have determine and assign values to a plurality of attributes of the ESG compliance regulation based on the analysis of the ESG compliance information and the purchase of the carbon offset, wherein each ESG compliance regulation attribute is given a weighting relative to the other plurality of attributes; determine, in real-time by a scoring algorithm using a neural network, a score for the ESG compliance regulation, based on the ESG compliance regulation weighted attribute values, wherein the neural network adjusts at least one weight attribute value based on an ESG compliance regulation; determine an ESG compliance rating based on the score and a mapping of score ranges to ESG compliance ratings and provide, on a graphical user interface configured to display, the ESG compliance rating. The motivations behind this being to incorporate the capabilities to compute ratings. Also, it would be obvious to combine the references in at least in part due to the substituting of equivalents for the same purpose. In this instance, simple substitution of one known element (bond information) for another (ESG information) to obtain predictable results. In addition to being in the same CPC class, the teachings, suggestions, and motivations in this prior art would have led one of ordinary skill to modify the prior art reference or combine prior art reference teachings to arrive at the claimed invention.
Regarding Claim 2, Yan/Lancaster/Abbott/Abramowitz, teaches the limitations of Claim 2 which state
wherein when the ESG compliance regulation is issued by an entity, the ESG compliance regulation attributes include carbon dioxide emission rights (Yan: Para 0081-0083, 0141, 0146 via Data sources 310 include the world's leading commercial data company's clouds and 3.sup.rd party data sources. Examples include Green List, Global Diversity List, spend data, inquiry data, Global Archive, comprehensive global database of business information, small business risk insights, Country Risk, Risk scores (SSI/SER), and GHG Emission...Data sources 315 are public data sources, which may include data in various format pictures, e.g., PDF. Data sources 315 include (a) public data 320, (b) company websites 325, and (c) company reports 330...Public data 320 includes data from government, ée.g., SEC, and United Nations sources, and includes Form 10-K, proxy statements, annual reports, EPA, OSHA, EPLS and OFAC...The ESG rankings dataset's topic architecture was created by referencing several of the leading ESG standards, including the SASB, the Global Reporting Initiative (GRI), the Task Force on Climate-related Financial Disclosures (TCFD), the CDP (formerly the Carbon Disclosure Project), the UN SDGs, and other notable sustainability reporting frameworks...Data is first sourced through internal Dun & Bradstreet databases using analytical tools. This data was complemented with data from government sources (e.g., U.S. Environmental Protection Agency (EPA) compliance and environmental pollutant data), public sources (e.g., company reports and filings), news (e.g., processed through D&B Hoovers), and some third -party licensed data (e.g., aggregation of sustainability reports, GHG emissions from CDP).
Regarding Claim 3, Yan/Lancaster/Abbott/Abramowitz, teaches the limitations of Claim 3 which state
wherein when the ESG compliance regulation is issued by an entity, the ESG compliance regulation attributes include at least one category selected from the following: water treatment; renewable energy consumption; and emissions (Yan: Para 0141, 0163 and Table 1 via Under each of the environmental (E), social (S), and governance (G) dimensions, specific themes were described, as well as another layer of specific topics that relate to each general theme. Once this framework was established, each of the ESG themes could then be populated with hundreds of variables sourced from various datasets. The ESG rankings dataset uses the SASB Sustainable Industry Classification System@@® taxonomy for sector classifications. According to SASB, this taxonomy categorizes companies into sectors and industries in accordance with a fundamental view of their business model, their resource intensity, their sustainability impacts, and their sustainability innovation potential... Table 1, provides examples of ESG-related data per ESG topic... See Table 1).
Regarding Claim 4, Yan/Lancaster/Abbott/Abramowitz, teaches the limitations of Claim 4 which state
receiving the weighting scheme as input for the ESG compliance regulation attributes (Abramowitz: Para 0020 via Also, in a similar fashion, the weights are user-configurable as are specifying the attributes. That is, a user can enter the amount of weight for each specified attribute or can select from a list of available weight values. In an embodiment, the weights can be provided to the algorithm as an input file, either on a one-off basis or as part of an automated procedure).
Regarding Claim 5, Yan/Lancaster/Abbott/Abramowitz, teaches the limitations of Claim 5 which state
receiving the ESG compliance regulation attributes as input (Abramowitz: Para 0019 via in an embodiment, the specified attributes can be provided to the algorithm as an input file. For example, the system hosting the algorithm may include an automated process which feeds the list of specified attributed to the algorithm as input).
Regarding Claim 6, Yan/Lancaster/Abbott/Abramowitz, teaches the limitations of Claim 6 which state
wherein the scoring algorithm is configured to set a desired level of granularity (Abramowitz: Para 0023 via the level of granularity of the ultimately computed credit rating is important, because it is an object of the invention for the credit rating to be sensitive to and to reflect significant changes in the credit risk of the underlying issuer or bond itself. That is, it is important for even slight changes as well as large changes to any of the bond attributes to be detected and reflected in the credit rating. These slight changes as well as large changes are captured in the level of granularity as specified in, but not limited to, the attributes and the respective weights. For example, it is contemplated that a user can enter as many types of attributes as is needed for capturing an important change in the credit rating of the given bond. It further is contemplated that a user can specify the level of accuracy, e.g. to the decimal place, of any particular attribute value).
Regarding Claim 7, Yan/Lancaster/Abbott/Abramowitz, teaches the limitations of Claim 7 which state
wherein the mapping of score ranges to ESG compliance ratings is configurable (Abramowitz: Para 0034 via Thus, an embodiment provides a mapping of ranges of scores to credit ratings. For example, in FIG. 5, although Bond 1 and Bond 2 have different scores, namely, 37 and 39, respectively, Bond 1 and Bond 2 have the same credit rating, namely, 7. Thus, in this example, both 37 and 39 get mapped to credit rating 7. In an embodiment, the score values, ranges, credit rating values, and mapping of score ranges to credit ratings are configurable).
Regarding Claims 8-14, they are analogous to Claims 1-7 respectively and are rejected for the same reasons.
Regarding Claims 15-20, they are analogous to Claims 1-6 respectively and are rejected for the same reasons. In addition, Yan teaches of a non-transitory computer-readable medium, processor, and device (Yan: Para 0063-0065, 0068 via system descriptions).
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.
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/T.E.S./ Examiner, Art Unit 3623 /RUTAO WU/Supervisory Patent Examiner, Art Unit 3623