DETAILED ACTION
This Office action is in response to the applicant's filing of 09/19/2024.
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
Preliminary amendments, filed on 09/19/2024, canceled claims 20-23 and amended claims 7, 10, 12, 13, 16, 17, 19, and 24. Claims 1-19 and 24 are pending and have been examined.
Information Disclosure Statement
The information disclosure statement(s) (IDS) submitted on 09/19/2024 has/have been considered by the examiner.
Claim Interpretation
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
Use of the word “means” (or “step for”) in a claim with functional language creates a rebuttable presumption that the claim element is to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is invoked is rebutted when the function is recited with sufficient structure, material, or acts within the claim itself to entirely perform the recited function.
Absence of the word “means” (or “step for”) in a claim creates a rebuttable presumption that the claim element is not to be treated in accordance with 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph). The presumption that 35 U.S.C. 112(f) (pre-AIA 35 U.S.C. 112, sixth paragraph) is not invoked is rebutted when the claim element recites function but fails to recite sufficiently definite structure, material or acts to perform that function.
Claim elements in this application that use the word “means” (or “step for”) are presumed to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action. Similarly, claim elements that do not use the word “means” (or “step for”) are presumed not to invoke 35 U.S.C. 112(f) except as otherwise indicated in an Office action.
Independent claim 1 recites the limitation “a data capture tool configured to capture raw environmental data from a first plurality of communication channels pertaining to an organisation, and to capture a set of comparison data from a second plurality of communication channels; a data analysis module configured to compare the captured raw environmental data against the set of comparison data; and a reporting module configured to output, based on an analysis output of the data analysis module, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device,” which have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder “data capture tool / data analysis module / reporting module” configured to “capture raw environmental data from a first plurality of communication channels pertaining to an organisation, and capture a set of comparison data from a second plurality of communication channels / compare the captured raw environmental data against the set of comparison data / output, based on an analysis output of the data analysis module, an environmental action trigger, and route an electronic communication comprising the environmental action trigger” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Dependent claim 10 recites the limitation “a data processing module configured to receive the captured data from the first plurality of communication channels in a first format and from the second plurality of communication channels in a second format and to process the captured data to convert the first and second format to a common format for storage in the database,” which have been interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, because they use a generic placeholder “data processing module” configured to “receive the captured data from the first plurality of communication channels in a first format and from the second plurality of communication channels in a second format and to process the captured data to convert the first and second format to a common format for storage in the database” without reciting sufficient structure to achieve the function. Furthermore, the generic placeholder is not preceded by a structural modifier.
Since the claim limitation(s) invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, claim(s) 1 and 10 have been interpreted to cover the corresponding structure described in the specification that achieves the claimed function, and equivalents thereof.
A review of the specification shows that the following appears to be the corresponding structure described in the specification for the 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph limitation: The Examiner interprets the elements “data capture tool / data analysis module / reporting module / data processing module” configurations to be hardware components or “programmed computer” (See pages 12-17 of the Applicant's originally filed specification).
If applicant wishes to provide further explanation or dispute the examiner’s interpretation of the corresponding structure, applicant must identify the corresponding structure with reference to the specification by page and line number, and to the drawing, if any, by reference characters in response to this Office action.
If applicant does not intend to have the claim limitation(s) treated under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112 , sixth paragraph, applicant may amend the claim(s) so that it/they will clearly not invoke 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, or present a sufficient showing that the claim recites/recite sufficient structure, material, or acts for performing the claimed function to preclude application of 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph.
For more information, see MPEP § 2173 et seq. and Supplementary Examination Guidelines for Determining Compliance With 35 U.S.C. 112 and for Treatment of Related Issues in Patent Applications, 76 FR 7162, 7167 (Feb. 9, 2011).
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.
Claim 19 is rejected under 35 U.S.C. 101 because the claimed invention is not directed to statutory subject matter. Independent claim 19 references " A transitory or non-transitory computer readable media on which are stored computer-readable instructions which, when executed by a processor of a computer device cause the processor to". The United States Patent and Trademark Office (USPTO), during prosecution, applies to claims their broadest reasonable interpretation consistent with the specification. See In re Zletz, 893 F.2d 319 (Fed. Cir. 1989) (during patent examination the pending claims must be interpreted as broadly as their terms reasonably allow). According to page 7 of the Applicant’s specification, “According to a fourth aspect of the invention there is provided a transitory or non-transitory computer readable media on which are stored computer-readable instructions which, when executed by a processor of a computer device cause the processor to carry out a method according to the third aspect of the invention.” This open ended definition does not explicitly exclude carrier signals or propagating waves. Therefore the broadest reasonable interpretation of the term computer usable storage device includes "signals" and "carrier waves". Signals are not a statutory type of storage media (In re Nuitjen, 84 USPQ2d 1495 and "Subject Matter Eligibility of Computer Readable Media" at http://www.uspto.gov/patents/law/notices/101_crm_20100127.pdf).
"In an effort to assist the patent community in overcoming a rejection or potential rejection under 35 U.S.C. § 101 in this situation, the USPTO suggests the following approach. A claim drawn to such a computer usable storage device that covers both transitory and non-transitory embodiments may be amended to narrow the claim to cover only statutory embodiments to avoid a rejection under 35 U.S.C. § 101 by adding the limitation "non-transitory" to the claim. Cf. Animals - Patentability, 1077 Off. Gaz. Pat. Office 24 (April 21, 1987) Such an amendment would typically not raise the issue of new matter, even when the specification is silent because the broadest reasonable interpretation relies on the ordinary and customary meaning that includes signals per se. The limited situations in which such an amendment could raise issues of new matter occur, for example, when the specification does not support a non-transitory embodiment because a signal per se is the only viable embodiment such that the amended claim is impermissibly broadened beyond the supporting disclosure. See, e.g., Gentry Gallery, Inc. v. Berkline Corp., 134 F.3d 1473 (Fed. Cir. 1998).
Claims 1-19 and 24 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. The claims are directed to a judicial exception (i.e., a law of nature, natural phenomenon, or abstract idea) without significantly more.
Step 1: In a test for patent subject matter eligibility, claims 1-18 and 24 are found to be in accordance with Step 1 (see 2019 Revised Patent Subject Matter Eligibility), as they are related to a process, machine, manufacture, or composition of matter. Claims 1-17 and 24 recite a system, claim 18 recites a method, and claim 19 recites a computer-readable medium. When assessed under Step 2A, Prong I, they are found to be directed towards an abstract idea. The rationale for this finding is explained below:
Step 2A, Prong I: Under Step 2A, Prong I, independent claims 1, 18, and 19 are directed to an abstract idea without significantly more, as they all recite a judicial exception. Claims 1, 18, and 19 recite limitations directed to the abstract idea including “capturing raw environmental data from a first plurality of communication channels pertaining to an organisation, and a set of comparison data from a second plurality of communication channels; storing the captured raw environmental data and the set of comparison data; comparing the captured raw environmental data against the set of comparison data; and outputting, based on an analysis output, an environmental action trigger, and routing an electronic communication comprising the environmental action trigger.” These further limitations are not seen as any more than the judicial exception. Claims 1, 18, and 19 recite additional limitations including “by a data capture tool, in a data structure stored in a computer memory, by a data analysis module, by a reporting module, and to one or more user interface of one or more respective computer device.” The claims are considered to be an abstract idea under certain methods of organizing human activity because the claims are directed to commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations) and managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) such as generating an environmental action trigger by monitoring raw environmental data of an organisation. The claims are also considered to be an abstract idea under mental processes because the claims are directed to concepts performed in the human mind (including an observation, evaluation, judgment, opinion) such as receiving or capturing data (i.e. raw environmental data and comparison data); storing the captured data (i.e. raw environmental data and comparison data); comparing the captured data (i.e. raw environmental data vs comparison data); and outputting, based on an comparison analysis, data (i.e. environment action trigger) to route/send. Therefore, under Step 2A, Prong I, claims 1, 18, and 19 are directed towards an abstract idea.
Step 2A, Prong II: Step 2A, Prong II is to determine whether any claim recites any additional element that integrate the judicial exception (abstract idea) into a practical application. Claims 1, 18, and 19 recite additional limitations including “by a data capture tool, in a data structure stored in a computer memory, by a data analysis module, by a reporting module, and to one or more user interface of one or more respective computer device.” The limitations reciting – “by a data capture tool, in a data structure stored in a computer memory, by a data analysis module, by a reporting module, and to one or more user interface of one or more respective computer device” are seen as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Accordingly, alone, and in combination, these additional elements are seen as using a computer or tool to perform an abstract idea, adding insignificant-extra-solution activity to the judicial exception. They do no more than link the judicial exception to a particular technological environment or field of use, i.e. data capture tool, data structure stored in memory, modules, or computer device, and therefore do not integrate the abstract idea into a practical application. The courts decided that although the additional elements did limit the use of the abstract idea, the court explained that this type of limitation merely confines the use of the abstract idea to a particular technological environment and this fails to add an inventive concept to the claims (See Affinity Labs of Texas v. DirecTV, LLC,). Under Step 2A, Prong II, these claims remain directed towards an abstract idea.
Step 2B: Claims 1, 18, and 19 recite additional limitations including “by a data capture tool, in a data structure stored in a computer memory, by a data analysis module, by a reporting module, and to one or more user interface of one or more respective computer device.” These limitations do not integrate the judicial exception (abstract idea) into a practical application because of the analysis provided in Step 2A, Prong II. Claims 1, 18, and 19 do not include additional elements or a combination of elements that result in the claims amount to significantly more than the judicial exception. As discussed above with respect to integration of the abstract idea into a practical application, the additional elements listed amount to no more than mere instructions to apply an exception using a generic computer component. In addition, the applicant’s specifications describe a “data processing module…or a suitably programmed computer, aided by human user”, see page 12 of Applicant’s originally filed specification, for implementing the computer, which do not amount to significantly more than the abstract idea of itself, which is not enough to transform an abstract idea into eligible subject matter. Furthermore, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. Under Step 2B in a test for patent subject matter eligibility, these claims are not patent eligible.
Dependent claims 2-17 and 24 further recite system of claim 1. Dependent claims 2-17 and 24 when analyzed as a whole are held to be patent ineligible under 35 U.S.C. 101 because the additional recited limitation fail to establish that the claims are not directed to an abstract idea:
Under Step 2A, Prong I, these additional claims only further narrow the abstract idea set forth in claims 1, 18, and 19. For example, claims 2-17 and 24 further describe the limitations for generating an environmental action trigger by monitoring raw environmental data of an organisation – which is only further narrowing the scope of the abstract idea recited in the independent claims.
Under Step 2A, Prong II, for dependent claims 2-17 and 24, there are no additional elements introduced. Thus, they do not present integration into a practical application, or amount to significantly more.
Under Step 2B, the dependent claims do not include any additional elements that are sufficient to amount to significantly more than the judicial exception. Additionally, there is no improvement in the functioning of the computer or technological field, and there is no transformation of subject matter into a different state. As discussed above with respect to integration of the abstract idea into a practical application, the additional claims do not provide any additional elements that would amount to significantly more than the judicial exception. Under Step 2B, these claims are not patent eligible.
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) 1-4, 6-8, 10-19, and 24 are rejected under 35 U.S.C. 103 as being unpatentable over U.S. Publication 2012/0316916 to Andrews in view of U.S. Publication 2022/0019624 to Gwozdz.
Claims 1, 18, and 19 are system, method, and computer-readable medium claims, respectively, with substantially indistinguishable features between each group. For purposes of compact prosecution, the Office has grouped the common method, system and non-transitory computer readable storage medium claims in applying applicable prior art.
With respect to Claim 1:
Andrews teaches:
A computer system for generating an environmental action trigger by monitoring raw environmental data of an organisation, the computer system comprising: a data capture tool configured to capture raw environmental data from a first plurality of communication channels pertaining to an organisation, and to capture a set of comparison data […] (i.e. capturing real-time news and environmental data and composite index) (Andrews: ¶ [0041] “In one implementation, with reference to FIG. 1, the present invention provides a News/Media Analytics System (NMAS) 100 adapted to automatically process and "read" news stories and content from blogs, twitter, and other social media sources, represented by news/media corpus 110, in as close to real-time as possible. Quantitative analysis, techniques or mathematics, such as green scoring/composite module 124 and sentiment processing module 125, in conjunction with computer science are processed by processor 121 of server 120 to arrive at green scores, green certification, and/or model the value of financial securities, including generating a composite environmental or green index. The NMAS 100 automatically processes news stories, filings, new/social media and other content and applies one or more models against the content to determine green scoring and/or anticipate behavior of stock price and other investment vehicles. The NMAS 100 leverages traditional and, especially, new media resources to provide a sentiment-based solution that expands the scope of conventional tools to include social media and online news.” Furthermore, as cited in ¶ [0045] “In addition, the NMAS 100 may include a green classification module 128 adapted to generate a classification system of environmentally conscious or friendly companies that serves as a classification system for green investing and that may be used to create a composite environment index. For example, companies presently assigned an RIC (Reuters Instrument Code), a ticker-like code used to identify financial instruments and indices, may be classified as "green compliant" ( e.g., achieved/maintained a green score of certain level and/or duration). In this manner the invention may be used to create a class of green-RICs for trading purposes. For example, a "Green Sentiment Index" may be generated and maintained comprised, for instance, of companies that have attained a green certification or green-RIC or the like. A green index is likely to attract investors interested in promoting environmentally responsible businesses.”);
a data structure stored in computer memory and configured to store the captured raw environmental data and the set of comparison data (i.e. storing real-time news and environmental data and composite index in databases) (Andrews: ¶ [0044] “In one manner, the NMAS 100 may be used to process traditional and new media sources of content 110 as sources of "Alpha" in the context of determining or representing "greenness" or a composite environmental index. In exemplary implementation, NMAS 100 is operated by a traditional financial services company, e.g., Thomson Reuters, wherein primary databases-internal 112 is internal textual sources, e.g., TR News and TR Feeds, and applies the data against green scoring module 124 and sentiment processing module 125 and may include predictive models to arrive at anticipated market-related behavior. For example, Thomson Reuters sources as the internal primary database may include legal sources (Westlaw), regulatory (SEC in particular, controversy data, sector specific, Etc.), social media (application of special meta-data to make it useful), and news (Thomson Reuters News) and news-like sources, including financial news and reporting. In addition, internal sources 112 may be supplemented with external sources 114, freely available or subscription-based, as additional data points considered by the predictive model. Hard facts, e.g., explosion on an oil rig results in direct financial losses (loss of revenue, damages liability, etc.) as well as negative environmental impact and resulting negative greenness score, and sentiment, e.g., quantifying the effect of fear, uncertainty, negative reputation, etc., are considered as factors that drive green scoring and/or composite environmental or green index.”);
a data analysis module configured to compare the captured raw environmental data against the set of comparison data (i.e. comparing raw data such as news/social media data against composite index in order to determine a classification of the green score) (Andrews: ¶ [0065] “In one exemplary method of the present invention, and with reference to the flow of FIG. 3, the following processes are performed. Initially, at step 302, a user obtains information and content of interest from suitable news/social media sources (news feeds, blogs, websites etc.) from internal or external sources. At step 304 the system applies pre-processing to obtained information to identify embedded metadata or other descriptors, process text, words, phrases and attribute relevance to one or more companies. At step 306, the system applies sentiment analysis and arrive at one or more sentiment scores associated with obtained and processed information as it relates to companies of interest identified therein. At step 308, the system optionally ( as discussed elsewhere herein) may apply a risk taxonomy to arrive at a separate score or indication or a derivative score or indication related to a green score or composite index. At step 310, the system applies a predictive model using the sentiment score to arrive at a green score, e.g., to arrive at a predicted condition or price behavior associated with each company. At step 312, for a set of companies each having a green score, the system generates an expression of a composite index of the set of green scores, e.g., the index representing predicted behavior and/or a suggested action to take in light of the predicted behavior ( e.g., buy, sell or hold) of the corresponding set of stock prices.” Furthermore, as cited in ¶ [0045] “In addition, the NMAS 100 may include a green classification module 128 adapted to generate a classification system of environmentally conscious or friendly companies that serves as a classification system for green investing and that may be used to create a composite environment index. For example, companies presently assigned an RIC (Reuters Instrument Code), a ticker-like code used to identify financial instruments and indices, may be classified as "green compliant" ( e.g., achieved/maintained a green score of certain level and/or duration). In this manner the invention may be used to create a class of green-RICs for trading purposes. For example, a "Green Sentiment Index" may be generated and maintained comprised, for instance, of companies that have attained a green certification or green-RIC or the like. A green index is likely to attract investors interested in promoting environmentally responsible businesses.”); and
a reporting module configured to output, based on an analysis output of the data analysis module, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device (i.e. report and alerts pertaining to green score changes are sent to user) (Andrews: ¶ [0070] “This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC). Outputs resulting from analysis of the sourced data may take any of the following forms for delivery: a real-time stream (and historical database) of sentiment/score for a given company for a given taxonomy; a real-time stream (and historical database) of sentiment/score of more than one company representing composite a composite index; an alerting service in the shape of a electronic message indicating that an indices for a company has very more than a preset % for a given period of time; and/or an alerting service in the format of an electronic message indicating that an indices for a company has very more than a preset% by the user/system for a given period of time preset by the user/system. The recipient of the output deliverable may then further process the output as desired.” Furthermore, as cited in ¶ [0073] “Now with reference to FIG. 7, and in context of the Green Sentiment Composite Index aspect of the present invention, NMAS 100 may have as its core foundation a combination of machine learning and Artificial Intelligence (AI) capabilities that provide intelligent information for use in analyzing impact of green behavior of public and private companies. The resulting output of NMAS 100 may be in the form of a Green Sentiment Company & Composite Index, Intelligent alerts, and/or desktop client/interface and tool set. NMAS 100 may utilize a highly specialized taxonomy geared towards scoring environmental topics relevant to companies and industries. Every source will have its own nuanced taxonomy and weighting for the index calculation, e.g., by Velocity Analytics. Once operational, AI can adapt to changing market conditions and expand the taxonomy to include newly developing lingo and highlight patterns of text that are most correlated with equity price movements. In implementation, the invention may provide a classification for green investing, green alerts in the SEC may be triggered, investors may trade based on the green-RIC or classification, social media components added to overall green-investment community, and green data feeds may be delivered for further processing by investors.”).
Andrews does not explicitly disclose a data capture tool configured to capture raw environmental data from a first plurality of communication channels pertaining to an organisation, and to capture a set of comparison data from a second plurality of communication channels.
However, Gwozdz further discloses a data capture tool configured to capture raw environmental data from a first plurality of communication channels pertaining to an organisation, and to capture a set of comparison data from a second plurality of communication channels (i.e. capturing raw environmental data and comparison via different sources such as deal documents or third party sources) (Gwozdz: ¶ [0182] “FIG. 25 represents a data map, according to an embodiment of the present invention. The data map may include data from deal documents and exhibits, signals, third party sources. Data may also be identified as being out of scope. Signals repository may include governance ( e.g., complaints, etc.); social (e.g. FEMA disaster declarations, proximity to bike share, proximity to public transit, proximity to EV charging stations, social vulnerability index, social building certifications, etc.) and environmental (e.g., green building certifications, deforestation, seas rise, water intensity, physical risk, energy intensity, etc.).” Furthermore, as cited in ¶ [0162] “Client 2110 may provide an input represented by Security Masterfile 2112 or Security in-app 2114. Process may initiate at 2116 and determine whether the input is a valid identifier at 2118. This may involve checking for presence in a security master. If yes, the process may determine whether this is a duplicate identifier at 2120 and logged at 2146. If not, the process may identify corresponding documents at 2122. This may be represented by a document flag in database, such as Postgres. The process may then identify a source, at 2124, 2126, 2128 and retrieve documents at 2130, 2132, 2134, 2136 and process documents at 2138. If a source is not identified and documents are not deemed relevant, the coverage may be logged as "No Linked" at 2150. If documents are identified and deemed relevant, coverage may be logged as "Documents" at 2152. Log processing details may be captured at 2154 and metadata details at 2156.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Gwozdz’s data capture tool configured to capture raw environmental data from a first plurality of communication channels pertaining to an organisation, and to capture a set of comparison data from a second plurality of communication channels to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order “to have a system and method that could overcome the foregoing disadvantages of known systems and that could apply automated and customized analysis to analyze documents, communications, text files, websites, and other structured and unstructured input files to generate output in the form of answers to specific questions and other supporting information.” (Gwozdz: ¶ [0009]).
With respect to Claims 18 and 19:
All limitations as recited have been analyzed and rejected to claim 1. Claim 18 recites “A computer-implemented method for generating an environmental action trigger by monitoring raw environmental data of an organisation, the method comprising:” the steps of system claim 1. Claim 19 recites “A transitory or non-transitory computer readable media on which are stored computer-readable instructions which, when executed by a processor of a computer device cause the processor to:” (Andrews: ¶ [0060]) the steps of system claim 1. Claims 18 and 19 do not teach or define any new limitations beyond claim 1. Therefore they are rejected under the same rationale.
With respect to Claim 2:
Andrews teaches:
The system of claim 1, wherein the set of comparison data comprises a set of benchmark data, wherein the analysis module is configured to determine, based on the benchmark data, a threshold standard of environmental practice, and wherein the environmental action trigger comprises an alert, the alert indicating that the captured raw environmental data does not comply with the threshold standard of environmental practice (i.e. utilizing benchmark data in order to alert indicating that an indices for a company has very more than a preset% for a given period of time, wherein the indices associated with the benchmark are used as a threshold) (Andrews: ¶ [0049] “The NMAS 100 may be powered by natural language processing with linguistics technology in processing news/media data and content delivered to it. The NMAS 100 analyzes company-related news/media commentary to track "green" sentiment over time. The quantitative "green" strategies provided by the NMAS 100 may be used in market making, in portfolio management to improve asset allocation decisions by benchmarking portfolio sentiment and calculating sector weightings, in fundamental analysis to forecast stock, sector, and market outlooks, in risk management to better understand abnormal risks to portfolios and to develop potential sentiment hedges, and to track and benchmark public perception and media coverage as well as that of competitors.” Furthermore, as cited in ¶ [0065] “At step 308, the system optionally ( as discussed elsewhere herein) may apply a risk taxonomy to arrive at a separate score or indication or a derivative score or indication related to a green score or composite index. At step 310, the system applies a predictive model using the sentiment score to arrive at a green score, e.g., to arrive at a predicted condition or price behavior associated with each company. At step 312, for a set of companies each having a green score, the system generates an expression of a composite index of the set of green scores, e.g., the index representing predicted behavior and/or a suggested action to take in light of the predicted behavior ( e.g., buy, sell or hold) of the corresponding set of stock prices.” Furthermore, as cited in ¶ [0070] “This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC). Outputs resulting from analysis of the sourced data may take any of the following forms for delivery: a real-time stream (and historical database) of sentiment/score for a given company for a given taxonomy; a real-time stream (and historical database) of sentiment/score of more than one company representing composite a composite index; an alerting service in the shape of a electronic message indicating that an indices for a company has very more than a preset % for a given period of time; and/or an alerting service in the format of an electronic message indicating that an indices for a company has very more than a preset% by the user/system for a given period of time preset by the user/system. The recipient of the output deliverable may then further process the output as desired.”).
With respect to Claim 3:
Andrews teaches:
The system of claim 1, wherein the set of comparison data comprises a set of external communication data issued by the organisation, wherein the data analysis module is configured to determine, based on the external communication data, a reported standard of environmental practice by the organisation, and wherein the output environmental action trigger is output based on a comparison of the reported standard of environmental practice and an actual standard of practice based on the raw environmental data (i.e. index of green classification reads on reported standard, wherein the alert is outputted based on a comparison of the companies’ green score to the composite index) (Andrews: ¶ [0027] “The composite environmental index is determined based at least in part on a set of green scores associated with the set of companies and the green score may be generated in real time and/or be arrived at based on one or more of the following positive criteria: product or manufacturing environmental related compliance or certification; energy efficiency; corporate practices that promote environmental stewardship, consumer protection, human rights, and diversity, business/products involved in green technology, energy efficient technologies, alternative fuel technologies, renewable resource technology and/or the following negative criteria: businesses involved in alcohol, tobacco, gambling, weapons, and/or the military, and businesses not environmental standard compliant.” Furthermore, as cited in ¶ [0065] “At step 308, the system optionally ( as discussed elsewhere herein) may apply a risk taxonomy to arrive at a separate score or indication or a derivative score or indication related to a green score or composite index. At step 310, the system applies a predictive model using the sentiment score to arrive at a green score, e.g., to arrive at a predicted condition or price behavior associated with each company. At step 312, for a set of companies each having a green score, the system generates an expression of a composite index of the set of green scores, e.g., the index representing predicted behavior and/or a suggested action to take in light of the predicted behavior ( e.g., buy, sell or hold) of the corresponding set of stock prices.” Furthermore, as cited in ¶ [0070] “This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC). Outputs resulting from analysis of the sourced data may take any of the following forms for delivery: a real-time stream (and historical database) of sentiment/score for a given company for a given taxonomy; a real-time stream (and historical database) of sentiment/score of more than one company representing composite a composite index; an alerting service in the shape of a electronic message indicating that an indices for a company has very more than a preset % for a given period of time; and/or an alerting service in the format of an electronic message indicating that an indices for a company has very more than a preset% by the user/system for a given period of time preset by the user/system. The recipient of the output deliverable may then further process the output as desired.”).
With respect to Claim 4:
Andrews teaches:
The system of claim 3, wherein the electronic communication comprising the environmental action trigger further comprises a visual indication of a comparison index between the reported standard of environmental practice and the actual standard of practice based on the raw environmental data (i.e. providing an output that compares the green sentiment score for company and the composite index, wherein the composite index represents the environmental standard) (Andrews: Elements I-III in Fig. 5 and ¶ [0070] “FIG. 5 is a flow chart that represents steps in an exemplary method for producing a sentiment for use in green scoring, for example for greenness benchmarking of public and private companies using social media and news content. The exemplary sources of data for processing by NMAS 100 includes: New Agency Wire sources ( e.g., AFP, AP, TR, Reuters, Bloomberg), Social Media (blogs, twitter, RSS, Gigaom, NWCleanTech, ClimateWire), and Internet/Web-based sources (e.g., CNN.com, WSJ.com, lesoir.be). In today's environment, social media often provides more timely sources of information than traditional news outlets. For example, a blogger may post a comment about "Company A", which comment and further commentary are picked up on social media sources before finally being mentioned in syndicated and traditional news stories/sources. This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC). Outputs resulting from analysis of the sourced data may take any of the following forms for delivery: a real-time stream (and historical database) of sentiment/score for a given company for a given taxonomy; a real-time stream (and historical database) of sentiment/score of more than one company representing composite a composite index; an alerting service in the shape of a electronic message indicating that an indices for a company has very more than a preset % for a given period of time; and/or an alerting service in the format of an electronic message indicating that an indices for a company has very more than a preset% by the user/system for a given period of time preset by the user/system. The recipient of the output deliverable may then further process the output as desired.” Furthermore, as cited in ¶ [0027] “The composite environmental index is determined based at least in part on a set of green scores associated with the set of companies and the green score may be generated in real time and/or be arrived at based on one or more of the following positive criteria: product or manufacturing environmental related compliance or certification; energy efficiency; corporate practices that promote environmental stewardship, consumer protection, human rights, and diversity, business/products involved in green technology, energy efficient technologies, alternative fuel technologies, renewable resource technology and/or the following negative criteria: businesses involved in alcohol, tobacco, gambling, weapons, and/or the military, and businesses not environmental standard compliant.”).
With respect to Claim 6:
Andrews teaches:
The system of claim 3, wherein the external communications data comprises one or more of: social media publications, web pages, media articles, press releases, annual reports, sustainability reports, policy documents, ESG reports, ESG performance data sheets, spreadsheets, or other investor related communications (i.e. raw/real-time data includes social media data, web data/articles, news articles, reports, and ESG data and other investor-related data) (Andrews: ¶¶ [0047] [0048] “Preferably, a green score of a company or index is calculated in near real time ( e.g., about 150 ms) and is used, for example, to develop alpha strategies for investments, monitor a company's green reputation, and identify changing risk profiles at the company and industry level. Unlike other approaches that rely on periodic research processed by analysts, the present invention receives and continuously processes media feeds, e.g., WWW web and social media feeds, in addition to traditional sources. In one manner, the invention produces a stream of information and data that captures daily trends along with the added value of intelligent alerts and a portal allowing users, e.g., customers, to access a chain of content, e.g., from related and unrelated products, e.g., other Thomson Reuters products. As green or environment related news and social media content increases, media services companies may leverage products and services across a broad platform of offerings, e.g., Thomson Reuters Markets. The present invention enables companies to connect offerings across divisions and accelerate market share penetration of the green analytics space…For example, green score criteria applied by the green scoring module 124 of the NMAS 100 may include: product or manufacturing environmental related compliance or certification; energy efficiency; corporate practices that promote environmental stewardship, consumer protection, human rights, and diversity. Green score criteria applied by the NMAS 100 may further include: positive attributes or scores for business/products involved in green technology, energy efficient technologies, alternative fuel technologies, renewable resource technology, and negative attributes or scores for businesses involved in alcohol, tobacco, gambling, weapons, and/or the military. The areas of concern recognized by the SRI industry can be summarized as environment, social justice, and corporate governance (ESG). Although described in terms of greenness and environmental compliance, the present invention may be applied in terms of creating a healthful, lifestyle, or other classification for scoring companies based on societal goals and pursuits.”).
With respect to Claim 7:
Andrews teaches:
The system of claim 3, wherein the analysis module is configured to automatically classify features of the captured data by environmental semantic theme (i.e. classify environmental data in order to classify the environmentally data into different levels of green compliancy) (Andrews: ¶ [0045] “In addition, the NMAS 100 may include a green classification module 128 adapted to generate a classification system of environmentally conscious or friendly companies that serves as a classification system for green investing and that may be used to create a composite environment index. For example, companies presently assigned an RIC (Reuters Instrument Code), a ticker-like code used to identify financial instruments and indices, may be classified as "green compliant" ( e.g., achieved/maintained a green score of certain level and/or duration). In this manner the invention may be used to create a class of green-RICs for trading purposes. For example, a "Green Sentiment Index" may be generated and maintained comprised, for instance, of companies that have attained a green certification or green-RIC or the like. A green index is likely to attract investors interested in promoting environmentally responsible businesses.” Furthermore, as cited in ¶ [0068] “More particularly, semantic analysis interprets text to discern expressions of affect or opinion and may be used to generate results having semantic awareness. Such systems may be based on ontologies, e.g., a human emotion ontology (HEO), and linguistic resources, e.g., WordNet-Affect (WNA). By extending the use of systems beyond traditional news sources, NMAS can employ the techniques to interpret and process opinions and sentiments expressed in non-traditional outlets/sources, e.g., blogs, wikis, online fora, message boards, chat rooms, social media networks, etc., to determine a green sentiment and green score. With all media sources, but particularly "new media" sources lacking the historic verification internal processes, the system may also assign some level of verification as to the accuracy (actual or perceived (short-term)) of the message. In addition, the system may be configured to identify "false" news and to anticipate short-term effect of such "news" in predicting stock price behavior.”).
With respect to Claim 8:
Andrews teaches:
The system of claim 7, wherein the analysis module comprises a machine learning model, the machine learning model trained on a training data set in which environmental semantic themes are labelled in text of a plurality of training documents (i.e. utilizing AI in order to train data for topology classification with respect to environmental data) (Andrews: ¶ [0073] “Now with reference to FIG. 7, and in context of the Green Sentiment Composite Index aspect of the present invention, NMAS 100 may have as its core foundation a combination of machine learning and Artificial Intelligence (AI) capabilities that provide intelligent information for use in analyzing impact of green behavior of public and private companies. The resulting output of NMAS 100 may be in the form of a Green Sentiment Company & Composite Index, Intelligent alerts, and/or desktop client/interface and tool set. NMAS 100 may utilize a highly specialized taxonomy geared towards scoring environmental topics relevant to companies and industries. Every source will have its own nuanced taxonomy and weighting for the index calculation, e.g., by Velocity Analytics. Once operational, AI can adapt to changing market conditions and expand the taxonomy to include newly developing lingo and highlight patterns of text that are most correlated with equity price movements. In implementation, the invention may provide a classification for green investing, green alerts in the SEC may be triggered, investors may trade based on the green-RIC or classification, social media components added to overall green-investment community, and green data feeds may be delivered for further processing by investors.”).
With respect to Claim 10:
Andrews does not explicitly disclose the system of claim 1, further comprising a data processing module configured to receive the captured data from the first plurality of communication channels in a first format and from the second plurality of communication channels in a second format and to process the captured data to convert the first and second format to a common format for storage in the database.
However, Gwozdz further discloses a data processing module configured to receive the captured data from the first plurality of communication channels in a first format and from the second plurality of communication channels in a second format and to process the captured data to convert the first and second format to a common format for storage in the database (i.e. receive captured data in different formats such as text, video, and/or audio and converting the data to a common format for storage in the database) (Gwozdz: ¶¶ [0051] [0052] “Referring to FIG. 2, the architecture of the System is depicted according to an exemplary embodiment of the invention. As mentioned previously, the System can support information extraction and data analysis on structured and unstructured data. The input data 210 may take the form of various files or information of different types and formats such as documents, text, video, audio, tables, and databases. As shown in FIG. 2, the data to be analyzed can be input to a core document management system 220…According to a preferred embodiment of the invention, the input data 210 is transformed into a common data format 230, referred to in FIG. 2 as "Lume." Lume may preferably be the common format for all components and data storage. As shown in FIG. 2, the core document management system includes a document conversion system 240 (to convert documents to a Lume format 230) and a document and corpus repository 220. The document conversion system provides a utility for extracting document data and metadata and storing it in a format 240 used to perform natural language processing.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Gwozdz’s receive the captured data from the first plurality of communication channels in a first format and from the second plurality of communication channels in a second format and to process the captured data to convert the first and second format to a common format for storage in the database to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order “to have a system and method that could overcome the foregoing disadvantages of known systems and that could apply automated and customized analysis to analyze documents, communications, text files, websites, and other structured and unstructured input files to generate output in the form of answers to specific questions and other supporting information.” (Gwozdz: ¶ [0009]).
With respect to Claim 11:
Andrews teaches:
The system of claim 10, wherein the data structure is a relational database and each data entry in the relational database is stored in association with a unique identifier that specifies a respective source communication channel of the data entry (i.e. classification codes or topic codes are used as identifiers in servers/databases for information retrieval) (Andrews: ¶ [0051] “Additional such techniques may be used to identify textual terms of potential heightened relevance, for example, score text across the following exemplary, primary dimensions: "Author sentiment"-metrics for how positive, negative or neutral the tone of the item is, specific to each company in the article; "Relevance"-how relevant or substantive the story is for a particular item; "Volume analysis" -how much news is happening on a particular company; "Uniqueness"-how new or repetitive the item is over various time periods; and Headline analysis----denotes special features such as broker actions, pricing commentary, interviews, exclusives, and wrap-ups, among many others. The NMAS uses rich metadata, for example: company identifiers; topic codes-identifying subject matter; stage of the story-alert, article, update, etc.; and business sector and geographic classification codes; index references to similar articles. The metadata across multiple fields provides differentiated content for use by quantitative analysts and sophisticated algorithmic engines.” Furthermore, as cited in ¶ [0056] “Databases 110, which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, include or are otherwise associated with respective indices (not shown). Each of the indices includes terms and phrases in association with corresponding document addresses, identifiers, and other conventional information. Databases 110 are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server 120.”).
With respect to Claim 12:
Andrews teaches:
The system of claim 1, wherein the one or more computer device, to which the electronic communication comprising the environmental action trigger is routed, comprises a computer device associated with the organization (i.e. alerts are sent to computing device associated with company/user) (Andrews: ¶ [0070] “This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC). Outputs resulting from analysis of the sourced data may take any of the following forms for delivery: a real-time stream (and historical database) of sentiment/score for a given company for a given taxonomy; a real-time stream (and historical database) of sentiment/score of more than one company representing composite a composite index; an alerting service in the shape of a electronic message indicating that an indices for a company has very more than a preset % for a given period of time; and/or an alerting service in the format of an electronic message indicating that an indices for a company has very more than a preset% by the user/system for a given period of time preset by the user/system. The recipient of the output deliverable may then further process the output as desired.” Furthermore, as cited in ¶ [0080] “The computing device may also be used to alert users through a computer interface (not shown) of risks, including but not limited to imminent risks, i.e., risks that are likely to occur including, but not limited to, likely to occur in the near future or a defined time period. Typically, the users are alerted via a computing device (not shown). The present invention, however, is not so limited, and any device having a visual display or even a voice communication may suitably be used.”).
With respect to Claim 13:
Andrews teaches:
The system of claim 1, wherein the one or more computer device, to which the electronic communication comprising the environmental action trigger is routed, comprises a computer device associated with a second organisation, the second organisation being an environmental regulatory organization (i.e. alerts/changes are monitored by EPA) (Andrews: ¶ [0072] “The computing device may also be used to alert users through a computer interface (not shown) of risks, including but not limited to imminent risks, i.e., risks that are likely to occur including, but not limited to, likely to occur in the near future or a defined time period. Typically, the users are alerted via a computing device (not shown). The present invention, however, is not so limited, and any device having a visual display or even a voice communication may suitably be used.”).
With respect to Claim 14:
Andrews does not explicitly disclose the system of claim 10, wherein the captured data from the first and second plurality of communication channels in the respective first and second formats is stored in the data structure in association with a timestamp that indicates a time at which the data is captured.
However, Gwozdz further discloses wherein the captured data from the first and second plurality of communication channels in the respective first and second formats is stored in the data structure in association with a timestamp that indicates a time at which the data is captured (i.e. time stamp associated with each event log corresponding to document are stored) (Gwozdz: ¶ [0132] “An embodiment of the present invention is directed to providing an Event Log. An exemplary Event Log details actions and when such actions occurred. Actions may include a number of assets added to a portfolio; a number of assets ready for purchase; assets removed from the portfolio, a LIBOR category change, etc. A corresponding date-time stamp may be provided along with asset level details. For example, a user may interact with an alert icon that provides portfolio events such as added/removed securities; changes to data categories; changes to answers, etc.” Furthermore, as cited in ¶ [0048] “FIG. 1 is a functional block diagram of a system for automated analysis of structured and unstructured data according to an exemplary embodiment of the invention. As shown in FIG. 1, the System integrates a variety of data sources, domain know ledge, and human interaction, in addition to the algorithms that ingest and structure the content. The System includes a scanning component 10 to ingest a plurality of documents 5 such as contracts, loan documents, and/or text files, and to extract related data 6. During the ingestion process, the System may incorporate OCR technology to convert an image (e.g., PDF image) into searchable characters and may incorporate NLP pre-processing to convert the scanned images into raw documents 11 and essential content 12. In addition, the appropriate ingestion approach will be used to convert and preserve document metadata and formatting information. In many instances, the input unstructured data will reside in a multitude of documents which together form a corpus 15 of documents that is stored in a dataset.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Gwozdz’s captured data from the first and second plurality of communication channels in the respective first and second formats is stored in the data structure in association with a timestamp that indicates a time at which the data is captured to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order “to have a system and method that could overcome the foregoing disadvantages of known systems and that could apply automated and customized analysis to analyze documents, communications, text files, websites, and other structured and unstructured input files to generate output in the form of answers to specific questions and other supporting information.” (Gwozdz: ¶ [0009]).
With respect to Claim 15:
Andrews does not explicitly disclose the system of claim 10, wherein the first and/ or second format is in the group comprising: text data, HTML, PDF, image, JSON files, XML files, and data tables.
However, Gwozdz further discloses wherein the first and/ or second format is in the group comprising: text data, HTML, PDF, image, JSON files, XML files, and data tables (Gwozdz: ¶ [0069] “FIG. 4A illustrates an example of the Lume structure and the initial conversion of different types of files into Lumes. As shown in FIG. 4A, dataset 410 refers to a body of different types of files or documents. These documents may initially be in different formats, e.g., such as Adobe portable document format (PDF), unstructured text files, Microsoft Word files, and HTML files.” Furthermore, as cited in ¶ [0075] “The documents are then converted in step 712 to Lume format as described above. An OCR process may be used in step 714 to convert an image file to characters. In step 716, the documents are collected in a Dataset. In step 718, the System identifies and annotates structural Lume Elements (e.g., see FIG. 6).” Furthermore, as cited in ¶ [0065] “According to one embodiment, Lume is implemented in Python and has computer-object representations as Python objects and is serialized as JavaScript Object Notation ("JSON") for inter-process communication. Lume may be designed for use with web-based specifications, such as JSON, Swagger (YAML), RESTful and will interface with the Python ecosystem, but it can also be implemented in, and support components written in Java and other languages.” Furthermore, as cited in ¶ [0072] “This is performed by document specific components that ingest the specific format. In particular, during ingestion, (i) the original file is opened, (ii) the DOCX format is decompressed into an XML file, and then (iii) the XML file is read into a data structure for parsing. The parsing separates the data in the document from the metadata, and then stores the data in the "data" field of the Lume, and the metadata into Lume Elements. This will then be output as a LumeText. Examples of metadata stored are author, page, paragraph, and font information.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Gwozdz’s first and/ or second format is in the group comprising: text data, HTML, PDF, image, JSON files, XML files, and data tables to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order “to have a system and method that could overcome the foregoing disadvantages of known systems and that could apply automated and customized analysis to analyze documents, communications, text files, websites, and other structured and unstructured input files to generate output in the form of answers to specific questions and other supporting information.” (Gwozdz: ¶ [0009]).
With respect to Claim 16:
Andrews teaches:
The system of claim 10, wherein the processing module is further configured to extract text content from the data captured from the first and second plurality of communication channels, and to define an entry in the relational database for each paragraph of text that is extracted (i.e. extract text from a plurality of channels in order to define metadata for content retrieval in a database) (Andrews: ¶ [0070] “The exemplary sources of data for processing by NMAS 100 includes: New Agency Wire sources (e.g., AFP, AP, TR, Reuters, Bloomberg), Social Media (blogs, twitter, RSS, Gigaom, NWCleanTech, ClimateWire), and Internet/Web-based sources (e.g., CNN.com, WSJ.com, lesoir.be). In today's environment, social media often provides more timely sources of information than traditional news outlets. For example, a blogger may post a comment about "Company A", which comment and further commentary are picked up on social media sources before finally being mentioned in syndicated and traditional news stories/sources. This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC).” Furthermore, as cited in ¶ [0056] “Databases 110, which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, include or are otherwise associated with respective indices (not shown). Each of the indices includes terms and phrases in association with corresponding document addresses, identifiers, and other conventional information. Databases 110 are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server 120.”).
With respect to Claim 17:
Andrews teaches:
The system of claim 9, wherein the processing module is further configured to extract text content from the data captured from the first and second plurality of communication channels, and to define an entry in the relational database for each paragraph of text that is extracted, and wherein the processing module is further configured to store, in association with each data entry associated with a paragraph of text, an indication of an environmental theme to which the paragraph is semantically directed (i.e. extract text from a plurality of channels in order to define metadata for content retrieval in a database) (Andrews: ¶ [0070] “The exemplary sources of data for processing by NMAS 100 includes: New Agency Wire sources (e.g., AFP, AP, TR, Reuters, Bloomberg), Social Media (blogs, twitter, RSS, Gigaom, NWCleanTech, ClimateWire), and Internet/Web-based sources (e.g., CNN.com, WSJ.com, lesoir.be). In today's environment, social media often provides more timely sources of information than traditional news outlets. For example, a blogger may post a comment about "Company A", which comment and further commentary are picked up on social media sources before finally being mentioned in syndicated and traditional news stories/sources. This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC).” Furthermore, as cited in ¶ [0051] “Additional such techniques may be used to identify textual terms of potential heightened relevance, for example, score text across the following exemplary, primary dimensions: "Author sentiment"-metrics for how positive, negative or neutral the tone of the item is, specific to each company in the article; "Relevance"-how relevant or substantive the story is for a particular item; "Volume analysis" -how much news is happening on a particular company; "Uniqueness"-how new or repetitive the item is over various time periods; and Headline analysis----denotes special features such as broker actions, pricing commentary, interviews, exclusives, and wrap-ups, among many others. The NMAS uses rich metadata, for example: company identifiers; topic codes-identifying subject matter; stage of the story-alert, article, update, etc.; and business sector and geographic classification codes; index references to similar articles. The metadata across multiple fields provides differentiated content for use by quantitative analysts and sophisticated algorithmic engines.” Furthermore, as cited in ¶ [0056] “Databases 110, which take the exemplary form of one or more electronic, magnetic, or optical data-storage devices, include or are otherwise associated with respective indices (not shown). Each of the indices includes terms and phrases in association with corresponding document addresses, identifiers, and other conventional information. Databases 110 are coupled or couplable via a wireless or wireline communications network, such as a local-, wide-, private-, or virtual-private network, to server 120.”).
With respect to Claim 24:
Andrews does not explicitly disclose the system of claim 3, wherein the comparison of the captured raw environmental data against the set of comparison data comprises determining that a first environmental factor is addressed in only one of the first and second plurality of communication channels.
However, Gwozdz further discloses wherein the comparison of the captured raw environmental data against the set of comparison data comprises determining that a first environmental factor is addressed in only one of the first and second plurality of communication channels (i.e. certain documents have environmental factors disclosed only in either a first or second type of communication channel or source) (Gwozdz: ¶¶ [0104] [0105] “Data sources may include documents in various formats including PDF, Tiff, Public, Private, HTML, conversion, etc. In addition, an embodiment of the present invention may be extended to consider other documents and sources. For example, a user may identify private documents such as private loans, credit agreements, etc. An embodiment of the present invention may blend these private documents with the corpus of public information. Accordingly, a user may connect and/or combine the user's own data sources, perform processing in the pipeline of the Securities Analyzer and then view and/or access results…An embodiment of the present invention may leverage third party data sources and other external data. This may be relevant for securities related to environmental social governance (ESG). For example, an embodiment of the present invention may connect to a certification database or energy ratings source to determine analytics relating to a particular security, such as a commercial mortgage backed security. The analytics may support a green score determination.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Gwozdz’s comparison of the captured raw environmental data against the set of comparison data comprises determining that a first environmental factor is addressed in only one of the first and second plurality of communication channels to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order “to have a system and method that could overcome the foregoing disadvantages of known systems and that could apply automated and customized analysis to analyze documents, communications, text files, websites, and other structured and unstructured input files to generate output in the form of answers to specific questions and other supporting information.” (Gwozdz: ¶ [0009]).
Claim(s) 5 is rejected under 35 U.S.C. 103 as being unpatentable over Andrews and Gwozdz in view of U.S. Publication 2019/0331655 to Jahns.
With respect to Claim 5:
Andrews teaches:
The system of claim 2, wherein the benchmark data comprises one or more of: environmental regulatory data indicating a regulatory standard of environmental practice (i.e. composite index includes benchmark standard green score for regulatory compliant companies) (Andrews: ¶ [0012] “The "green analytics" space is substantial and rapidly growing with investment firms and managers driving much of the growth and having the highest projected demand for green analytics. Existing products within the green analytics space generally fall under three categories: ESG Risk Solutions, Thematic Indices and Benchmarks, and Reputation Monitoring. One provider in the space is RiskMetrics/KLD, which specializes in web-based research services and thematic indices and carbon analytics. Financial services companies offer ESG products through indices and web-based research platforms. Societe Generale, for example, offers thematic indices covering a variety of issues from human rights to CSR. Other participants, such as FTSE, Dow Jones, and Calvert Investments, provide an environmental index that investors can use for benchmarking and portfolio construction. In the reputation monitoring space, companies such as RepRisk and Factiva Insight offer tools deployed through the web, which may be broad-based intelligence or focused, e.g., brand risk as it relates to environmental issues.” Furthermore, as cited in ¶ [0024] “In addition, the present invention may be used to generate a classification system of environmentally conscious or friendly companies that serves as a classification system for green investing. The present invention may be used to classify or certify a company as "green compliant" and to create a "Green Sentiment Index" comprised of companies that have attained a green certification. A green index is likely to attract investors interested in promoting environmentally responsible businesses.”), and
competitor environmental data indicating an [[average]] reported standard of environmental practice of a plurality of competitor organisations (i.e. providing an output that compares the green sentiment score for company and the composite index, wherein the composite index represents the environmental standard) (Andrews: Elements I-III in Fig. 5 and ¶ [0070] “FIG. 5 is a flow chart that represents steps in an exemplary method for producing a sentiment for use in green scoring, for example for greenness benchmarking of public and private companies using social media and news content. The exemplary sources of data for processing by NMAS 100 includes: New Agency Wire sources ( e.g., AFP, AP, TR, Reuters, Bloomberg), Social Media (blogs, twitter, RSS, Gigaom, NWCleanTech, ClimateWire), and Internet/Web-based sources (e.g., CNN.com, WSJ.com, lesoir.be). In today's environment, social media often provides more timely sources of information than traditional news outlets. For example, a blogger may post a comment about "Company A", which comment and further commentary are picked up on social media sources before finally being mentioned in syndicated and traditional news stories/sources. This seems to be particularly true in the case of "green" issues and content. By examining social media-based sentiment the present invention is more responsive to predicting behavior of companies and stock prices in respect to green issues. In the example of FIG. 5, the following analysis is performed: Entity extraction (e.g., subject, company, location, etc.), Source, Author, Volume of the news, Relate to a specific taxonomy/theme (e.g. green), Fact extraction, Topic code assignment, Classification assignment, Analyze the tone, Assign a sentiment ( + or - ), Instrument code assignment (e.g., RIC, green-RIC). Outputs resulting from analysis of the sourced data may take any of the following forms for delivery: a real-time stream (and historical database) of sentiment/score for a given company for a given taxonomy; a real-time stream (and historical database) of sentiment/score of more than one company representing composite a composite index; an alerting service in the shape of a electronic message indicating that an indices for a company has very more than a preset % for a given period of time; and/or an alerting service in the format of an electronic message indicating that an indices for a company has very more than a preset% by the user/system for a given period of time preset by the user/system. The recipient of the output deliverable may then further process the output as desired.” Furthermore, as cited in ¶ [0027] “The composite environmental index is determined based at least in part on a set of green scores associated with the set of companies and the green score may be generated in real time and/or be arrived at based on one or more of the following positive criteria: product or manufacturing environmental related compliance or certification; energy efficiency; corporate practices that promote environmental stewardship, consumer protection, human rights, and diversity, business/products involved in green technology, energy efficient technologies, alternative fuel technologies, renewable resource technology and/or the following negative criteria: businesses involved in alcohol, tobacco, gambling, weapons, and/or the military, and businesses not environmental standard compliant.”).
Andrews and Gwozdz do not explicitly disclose competitor environmental data indicating an average reported standard of environmental practice of a plurality of competitor organisations.
However, Jahns further discloses competitor environmental data indicating an average reported standard of environmental practice of a plurality of competitor organisations (i.e. standard processing factors or standard environmental practices is based on an average of the industry or competitor organizations) (Jahns: Table 4 and ¶ [0057] “The following 'standard processing factors' are used (Table 4). These standard factors are based on average indicators of the industry and internal assumptions. If further information is available such data will be used preferably.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Jahns’ competitor environmental data indicating an average reported standard of environmental practice of a plurality of competitor organisations to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order for “providing a scoring model, called "Product Sustainability Scorecard" to increase transparency of the environmental impact of fragrances and related raw materials to facilitate product development.” (Jahns: ¶ [0003]).
Claim(s) 9 is rejected under 35 U.S.C. 103 as being unpatentable over Andrews and Gwozdz in view of U.S. Publication 2022/0171906 to Suto.
With respect to Claim 9:
Andrews and Gwozdz do not explicitly disclose the system of claim 8, wherein the machine learning model is one of: a support vector machine, an XGBoost model, a long short-term memory model, and convolutional neural network.
However, Suto further discloses wherein the machine learning model is one of: a support vector machine, an XGBoost model, a long short-term memory model, and convolutional neural network (i.e. learning model may be support vector machine, CNN or long/short term modelling) (Suto: ¶ [0043] “The machine learning model may utilize a convolutional neural network (CNN). The machine learning model may utilize the CNN to perform a time series analysis. The machine learning model may utilize supervised learning models, such as, but not limited to support-vector machines, k-nearest neighbor algorithm, amongst others, for classification and regression of at least the comprehensive database and building information.” Furthermore, as cited in ¶¶ [0062] [0064] [0066] “If the user's purpose is short term, the environmental mapping program 110 may provide one or more recommendations the user can implement immediately, such as, but not limited to, temperature and lighting adjustments. The environmental mapping program 110 may provide corresponding green score improvements with the one or more recommendations…If the user's purpose is long term, the environmental mapping program 110 may provide one or more recommendations the user can implement over a period of time, such as, but not limited to, energy efficient utilities, solar panels, insulation, the addition of one or more IoT devices. The environmental program 110 may provide corresponding green score improvements with the one or more recommendations, as well as estimated cost and estimated savings over time…The environmental mapping program 110 may receive feedback from the user based on the one or more provided recommendations. For example, the user may provide the actual cost of installation and savings for a recommended energy efficient appliance. The environmental mapping program 110 may utilize feedback from the user in training the machine learning model.”).
Therefore, it would have been obvious to one of ordinary skill in the art, at the time the invention was made, to add Suto’s machine learning model is one of: a support vector machine, an XGBoost model, a long short-term memory model, and convolutional neural network to Andrews’ reporting module configured to output, based on a comparison between raw environmental data and comparison data, an environmental action trigger, and to route an electronic communication comprising the environmental action trigger to one or more user interface of one or more respective computer device. One of ordinary skill in the art would have been motivated to do so in order for “higher efficiency, more safety and comfort, and lower cost operation of the facility” and “Utilizing the comprehensive information on building performance of smart buildings may allow a user to assess the resource consumption and environmental impact of other buildings.” (Suto: ¶¶ [0002] [0003]).
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. The following references are cited to further show the state of the art:
U.S. Publication 2008/0275815 to Musier for disclosing methods and systems for facilitating exchange of rights associated with environmentally relevant items are provided. The methods and systems may include identifying a carbon reduction credit as an environmentally relevant item recognized by a jurisdiction associated with a first type of environmentally relevant action, identifying a renewable energy credit as an environmentally relevant item recognized by a jurisdiction associated with a different type of environmentally relevant action, identifying an energy efficiency credit as an environmentally relevant item recognized by a jurisdiction associated with a different type of environmentally relevant action, identifying a pollution reduction credit as an environmentally relevant item recognized by a jurisdiction associated with a first type of environmentally relevant action and providing a cross-environmentally relevant item complexity manager module by which a user can manage actions relevant to all of the types of environmentally relevant items.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to Azam Ansari, whose telephone number is (571) 272-7047. The examiner can normally be reached from Monday to Friday between 8 AM and 4:30 PM.
If any attempt to reach the examiner by telephone is unsuccessful, the examiner's supervisor, Waseem Ashraf, can be reached at (571) 270-3948.
Another resource that is available to applicants is the Patent Application Information Retrieval (PAIR). Information regarding the status of an application can be obtained from the (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAX. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pairdirect.uspto.gov. Should you have questions on access to the Private PAIR system, please feel free to contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free).
Applicants are invited to contact the Office to schedule either an in-person or a telephonic interview to discuss and resolve the issues set forth in this Office Action. Although an interview is not required, the Office believes that an interview can be of use to resolve any issues related to a patent application in an efficient and prompt manner.
Sincerely,
/AZAM A ANSARI/
Primary Examiner, Art Unit 3621
November 3, 2025
Prosecution Insights