Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Status of Claims
This communication is a First Office Action on the merits in reply to application number 18/673,378 filed on 05/24/2024.
Claims 1-23 are currently pending and have been examined.
Information Disclosure Statement
The information disclosure statement (IDS) filed on 05/24/2024 has been considered.
Priority
Applicant’s claim for priority under 35 U.S.C. 119 and/or 35 U.S.C. 120 is acknowledged.
Claim Objections
Claim 14 is/are objected to because of the following informalities: A grammatical error of the typographical kind.
Claim 14 recites: The computer system of claim 10. For the purpose of compact prosecution, Examiner is interpreting this as a typographical error, as the computer system of the claimed invention is recited by claim 9, not claim 10. Therefore, Examiner is examining claim 14 as being dependent on claim 9, not claim 10.
Claims 21-23 recite: The non-transitory computer-readable storage medium of claim 17. For the purpose of compact prosecution, Examiner is interpreting this as a typographical error, as the non-transitory computer-readable storage medium of the claimed invention is recited by claim 20, not claim 17. Therefore, Examiner is examining claims 21-23 as being dependent on claim 20, not claim 17.
Appropriate correction is required.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 1-23 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claims 1, 9, 20, and 10 are rejected under 35 USC 112(b) because the bounds of the claimed invention are unclear. In particular, the claims recite:
From claims 1/9/20: “determining reliability of the available data sources and prioritizing the available data sources according to relevance of the available data sources for the research objective, the available data sources comprising non-agent sources such as web scraping, Application Programming Interface (API) endpoints, web browsing, and uploaded files, as well as agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection;”.
From claim 10: “…and self-improvement techniques such as meta-learning, transfer learning, and agent self-analyses, …”.
It is unclear what the following are: “non-agent sources”, “agent-based sources”, and “self-improvement techniques”. The drafting of the claims (i.e., the use of “such as” to determine the subsequent elements) renders it ambiguous which, if any, of the subsequent elements of the claim limitations are required by the claim, i.e., they cause ambiguity as to claim scope. One ordinary skill in the art at the time of the invention’s filing would not readily recognize the meaning of claim limitations “non-agent sources”, “agent-based sources”, and “self-improvement techniques”. Therefore, the bounds of the claim are ambiguous. For the purpose of compact prosecution, Examiner is interpreting, under BRI, “such as” to being equivalent to “any of”, i.e., just one of the subsequent elements provided for the “non-agent sources”, “agent-based sources”, and “self-improvement techniques” as being required by the claim.
Claims 2-8, and 10-12 depend from claim 1 and fail to cure the issues noted above. Claims 13-19 depend from claim 9 and fail to cure the issues noted above. Claims 21-23 depend from claim 20 and fail to cure the issues noted above. Therefore, claims 2-8, 10-19, and 21-23 are indefinite based on their inheritance of the deficiencies of their respective parent claim.
Accordingly, claims 1-23 are rejected under 35 USC 112(b).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-23 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-patentable subject matter. The claims are directed to an abstract idea without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than the judicial exception as further set forth in MPEP 2106.
Step 1: The claimed invention is analyzed to determine if it falls outside one of the four statutory categories of invention. See MPEP 2106.03
Claims 1-8, 10-12, and 14 are directed to a method (i.e., Process), claims 9, 13, 15-19, and 21-23 are directed to a system (i.e., Machine), and claim 20 is directed to a non-transitory computer-readable storage medium (i.e., Manufacture). Therefore, claims 1-23 are directed to patent eligible categories of invention. Accordingly, claims 1-23 satisfy Step 1 of the eligibility inquiry.
Step 2A, Prong 1: In prong one of step 2A, the claim(s) is/are analyzed to evaluate whether they recite a judicial exception. See MPEP 2106.04
Independent claims 1, 9, and 17 recite a computing system, a method, and a machine-readable medium for enterprise resource planning. As drafted, the limitations recited by the independent claims fall under the “Mental Processes” abstract idea group by setting forth activities that could be performed mentally by a human (including an observation, evaluation, judgment, opinion) (see MPEP § 2106.04(a)(2), subsection III), and “Certain Methods of Organizing Human Activity” directed to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) (see MPEP § 2106.04(a)(2), subsection II).
receiving a query from a user within a Market Research System (MRS), wherein the query comprises a standard report, a research objective and/or one or more questions; (But for the recitation of additional elements (underlined), the step for “receiving a query“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
collecting context relevant to the query based on availability of internal information retrieval by querying the user with one or more clarification questions using one or more Artificial Intelligence (AI) agents within the MRS; (But for the recitation of additional elements (underlined), the step for “collecting context“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering. Furthermore, the step limitation “by querying the user” falls under the Certain Methods of Organizing Human Activity abstract idea grouping directed to managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions).);
forming a strategy to retrieve data from available data sources based on the context, wherein the strategy formation comprising: (The step for “forming a strategy“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
determining reliability of the available data sources and prioritizing the available data sources according to relevance of the available data sources for the research objective, the available data sources comprising non-agent sources such as web scraping, Application Programming Interface (API) endpoints, web browsing, and uploaded files, as well as agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection; (But for the recitation of additional elements (underlined), the steps for “determining reliability of the available data sources“ and “prioritizing the available data sources” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
retrieving data from the available data sources based on the strategy; (The step for “retrieving data“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
and analysing the data to determine if the retrieved data is sufficient to complete the research objective; (The step for “analyzing the data“ could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
requesting modification or additional information in the data based on the analysis using the one or more AI agents within the MRS, responsive to the data not being determined as sufficient to complete the research objective; (But for the recitation of additional elements (underlined), the step for “requesting modification or additional information” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper. Additionally, even if considered as an additional element, this step amounts to insignificant extra-solution activity as mere data gathering.);
determining market research parameters based on the analysed data, the market research parameters comprising dynamic reports with visualizations and/or structured reports based on predefined templates; (The step for “determining market research parameters” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.);
and iteratively optimizing market research parameters by activating the one or more AI agents within the MRS based on incoming data and feedback. (But for the recitation of additional elements (underlined), the step for “iteratively optimizing market research parameters” could be accomplished mentally, such as by human observation, evaluation, judgement, or with the help of pen and paper.).
The additional elements beyond the abstract ideas for consideration under Step 2A, Prong 2, and Step 2B recited by independent claim 1 are: one or more Artificial Intelligence (AI) agents, web scraping, Application Programming Interface (API) endpoints, web browsing, uploaded files, agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection. Independent claims 9 and 20 recite limitations that are substantially similar to the limitations recited by claim 1, therefore the same analysis applies to claims 9 and 20. The additional elements beyond the abstract ideas for consideration under Step 2A, Prong 2, and Step 2B recited by independent claim 9 are: memory unit, and processor. The additional elements beyond the abstract ideas for consideration under Step 2A, Prong 2, and Step 2B recited by independent claim 20 are: non-transitory computer-readable storage medium.
The dependent claims further narrow the abstract ideas and introduce the following additional elements for consideration:
From claim 10: data-driven fine tuning, prompt engineering, parameter optimization, meta-learning, transfer learning, agent self-analyses, and reinforcement learning from human feedback.
From claim 11: chart generator agent, and code interpreter agent.
Dependent claims 2-8, 12-19, and 21-23 further narrow the abstract idea and do not introduce any additional elements for consideration.
Step 2A, Prong 2: An evaluation is made whether a claim recites any additional element, or combination of additional elements, that integrate the judicial exception into a practical application of the exception. See MPEP 2106.04(d).
Regarding the computing additional elements, namely Application Programming Interface (API) endpoints, memory unit, processor, and non-transitory computer-readable storage medium, these additional elements have been evaluated but fail to integrate the abstract idea into a practical application because they amount to using generic computing elements or instructions (software) to perform the abstract idea, similar to adding the words “apply it” (or equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (generic computing environment). See MPEP 2106.05(f) and 2106.05(h). In addition, these limitations fail to provide an improvement to the functioning of a computer or to any other technology or technical field, fail to apply the exception with a particular machine, fail to apply the judicial exception to effect a particular treatment or prophylaxis for a disease or medical condition, fail to effect a transformation of a particular article to a different state or thing, and fail to apply/use the abstract idea in a meaningful way beyond generally linking the use of the judicial exception to a particular technological environment (generic computing environment).
With respect to the limitations for requesting modification or additional information in the data based on the analysis using the one or more AI agents within the MRS, retrieve data from the available data sources based on the strategy, the available data sources comprising non-agent sources such as web scraping, Application Programming Interface (API) endpoints, web browsing, and uploaded files, as well as agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection, wherein the one or more AI agents are fine-tuned using techniques including data-driven fine-tuning, prompt engineering, parameter optimization, and self-improvement techniques such as meta-learning, transfer learning, and agent self-analyses, and wherein the iterative optimization of market research parameters incorporates Reinforcement Learning from Human Feedback (RLHF) based on user feedback, these limitations fail to integrate the abstract idea into a practical application because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception.
Accordingly, because the Step 2A Prong One and Prong Two analysis resulted in the conclusion that the claims are directed to an abstract idea, additional analysis under Step 2B of the eligibility inquiry must be conducted in order to determine whether any claim element or combination of elements amount to significantly more than the judicial exception.
Step 2B: The claims are analyzed to determine whether any additional element, or combination of additional elements, is/are sufficient to ensure that the claims amount to significantly more than the judicial exception. This analysis is also termed a search for "inventive concept." See MPEP 2106.05.
Regarding the computing additional elements, namely Application Programming Interface (API) endpoints, memory unit, processor, and non-transitory computer-readable storage medium, these additional element(s) has/have been evaluated, but fail to add significantly more to the claims because they amount to using generic computing elements (computer hardware) or instructions/software (engine) to perform the abstract idea, similar to adding the words “apply it” (or an equivalent), which merely serves to link the use of the judicial exception to a particular technological environment (network computing environment, the internet, online) and does not amount to significantly more than the abstract idea itself. Applicant’s specification recites the computing additional elements at a high level of generality. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
With respect to the limitations for requesting modification or additional information in the data based on the analysis using the one or more AI agents within the MRS, retrieve data from the available data sources based on the strategy, the available data sources comprising non-agent sources such as web scraping, Application Programming Interface (API) endpoints, web browsing, and uploaded files, as well as agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection, wherein the one or more AI agents are fine-tuned using techniques including data-driven fine-tuning, prompt engineering, parameter optimization, and self-improvement techniques such as meta-learning, transfer learning, and agent self-analyses, and wherein the iterative optimization of market research parameters incorporates Reinforcement Learning from Human Feedback (RLHF) based on user feedback, these limitations fail to add significantly more to the abstract idea because the provide nothing more than mere instructions to implement an abstract idea on a generic computer. See MPEP 2106.05(f). MPEP 2106.05(f) provides the following considerations for determining whether a claim simply recites a judicial exception with the words “apply it” (or an equivalent), such as mere instructions to implement an abstract idea on a computer: (1) whether the claim recites only the idea of a solution or outcome i.e., the claim fails to recite details of how a solution to a problem is accomplished; (2) whether the claim invokes computers or other machinery merely as a tool to perform an existing process; and (3) the particularity or generality of the application of the judicial exception. Therefore, the additional elements merely describe generic computing elements or computer-executable instructions (software) merely serve to tie the abstract idea to a particular operating environment, which does not add significantly more to the abstract idea. See, e.g., Alice Corp., 134 S. Ct. 2347, 110 USPQ2d 1976; Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
Furthermore, even if the receiving a query, by querying the user, retrieving data, and requesting modification or additional information are interpreted as additional elements, these activities at most amount to insignificant extra-solution activity (insignificant application), which does not add significantly more to the abstract idea, as noted in MPEP 2106.05(g). Additionally, the receiving a query, by querying the user, retrieving data, and requesting modification or additional information extra-solution activity have been recognized as well-understood, routine, and conventional, and thus insufficient to add significantly more to the abstract idea. See MPEP 2106.05(d) - 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); TLI Communications LLC v. AV Auto. LLC, 823 F.3d 607, 610, 118 USPQ2d 1744, 1745 (Fed. Cir. 2016) (using a telephone for image transmission); OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)).
In addition, when taken as an ordered combination, the ordered combination adds nothing that is not already present as when the elements are taken individually. Their collective functions merely provide generic computer implementation. Therefore, when viewed as a whole, these additional claim elements do not provide meaningful limitations to amount to significantly more than the abstract idea itself.
The ordered combination of elements in the claims (including the limitations inherited from the parent claim(s)) add nothing that is not already present as when the elements are taken individually. There is no indication that the combination of elements improves the functioning of a computer or improves any other technology. Their collective functions merely provide generic computer implementation. Accordingly, the subject matter encompassed by the dependent claims fails to amount to significantly more than the abstract idea itself.
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.
Claims 1-3 are rejected under 35 U.S.C. 103 as being unpatentable over Cella et al. (US 20230186201 A1, hereinafter “Cella”), in view of Balaji et al. (US 20210374776 A1, hereinafter “Balaji”).
Regarding claim 1: Cella teaches a method for automating market research ([0013] A need exists for improved methods and systems for data collection in industrial environments, as well as for improved methods and systems for using collected data to provide improved monitoring, control, intelligent diagnosis of problems and intelligent optimization of operations in various heavy industrial environments.) with the following limitations:
receiving a query from a user within a Market Research System (MRS), wherein the query comprises a standard report, a research objective and/or one or more questions; ([4365] the CMO digital twin 60628 may be configured to simulate marketing campaigns, such that the simulations of the marketing campaign may vary parameters such as vehicles (e.g., social media, television, billboards, print, etc.), budget, targeting parameters (e.g., geographic, demographic, or the like), and/or other suitable marketing campaign parameters. In these embodiments, the digital twin simulation system 60320 may receive a request to perform the simulation CMO digital twin, where the request indicates campaign features and the parameters that are to be varied.);
collecting context relevant to the query based on availability of internal information retrieval by querying the user with one or more clarification questions using one or more Artificial Intelligence (AI) agents within the MRS; ([4365] In response, the digital twin simulation system 60320 may return the simulation results to the CMO digital twin 60628, which in turn outputs the results to the user via the client device display. In this way, the user is provided with various outcomes corresponding to different parameter configurations. In some embodiments, the user may select a parameter set based on the various outcomes. In some embodiments, an executive agent trained by the user may select the parameter sets based on the various outcomes.);
forming a strategy to retrieve data from available data sources based on the context, wherein the strategy formation comprising: determining reliability of the available data sources and prioritizing the available data sources according to relevance of the available data sources for the research objective, ([4366] The CMO digital twin 60628 may utilize the machine learning, A.I. and other analytic capabilities, as described herein, to analyze the content of the four categories of content and classify and score the content characteristics that are probabilistically associated with improved financial or other performance for stated types of marketing campaigns or marketing subject matter.);
the available data sources comprising non-agent sources such as web scraping, Application Programming Interface (API) endpoints, web browsing, and uploaded files, as well as agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection; ([4367] In embodiments, a CMO digital twin 60628 may be configured to store, aggregate, merge, analyze, prepare, report and distribute material relating to market surveys, online surveys, customer panels, ratings, rankings, marketing trend data or other data related to marketing. A CMO digital twin 60628 may link to, interact with, and be associated with external data sources, and able to upload, download, aggregate external data sources, including with the EMP's internal data, and analyze such data, as described herein. Data analysis, machine learning, AI processing, and other analysis may be coordinated between the CMO digital twin 60628 and an analytics team based at least in part on using the artificial intelligence services system 60012. This cooperation and interaction may include assisting with seeding data elements and domains in the enterprise data store 60014 for use in modeling, machine learning, and AI processing to identify the optimal marketing content, sales channels, target consumers, price points, timing, or some other marketing-relating metric or aspect, as well as identification of the optimal data measurement parameters on which to base judgment of a marketing endeavor's success. Examples of data sources 60030 that may be connected to, associated with, and/or accessed from the CMO digital twin 60628 may include, but are not limited to, a sensor system 60032, a sales database 60034 that is updated with sales figures in real time, a CRM system 60038, a marketing campaign platform 60040, news websites, a financial database 60048 that tracks costs of the business, surveys 60050 (e.g., customer satisfaction surveys), an org chart 60052, a workflow management system 60054, customer databases 60062 structured to store customer data, and/or third-party datastores 60060 structured to store third-party data.; [4371] In embodiments, a CMO digital twin 60628 may be configured to monitor, store, aggregate, merge, analyze, prepare, report and distribute material relating to competitors of a CMO's organization, or named entities of interest. In embodiments, such data may be collected by the EMP 60000 via data aggregation, spidering, web-scraping, or other techniques to search and collect competitor information from sources including, but not limited to, press releases, SEC or other financial reports, mergers and acquisitions activity, or some other publicly available data.; [4265] After an enterprise digital twin is served, some enterprise digital twins may be subsequently updated with real-time data received via the API system 60018.);
retrieving data from the available data sources based on the strategy; ([4371] In embodiments, a CMO digital twin 60628 may be configured to monitor, store, aggregate, merge, analyze, prepare, report and distribute material relating to competitors of a CMO's organization, or named entities of interest. In embodiments, such data may be collected by the EMP 60000 via data aggregation, spidering, web-scraping, or other techniques to search and collect competitor information from sources including, but not limited to, press releases, SEC or other financial reports, mergers and acquisitions activity, or some other publicly available data.);
and analysing the data to determine if the retrieved data is sufficient to complete the research objective; ([4371] In embodiments, a CMO digital twin 60628 may be configured to monitor, store, aggregate, merge, analyze, prepare, report and distribute material relating to competitors of a CMO's organization, or named entities of interest. In embodiments, such data may be collected by the EMP 60000 via data aggregation, spidering, web-scraping, or other techniques to search and collect competitor information from sources including, but not limited to, press releases, SEC or other financial reports, mergers and acquisitions activity, or some other publicly available data.);
determining market research parameters based on the analysed data, the market research parameters comprising dynamic reports with visualizations and/or structured reports based on predefined templates; ([4365] the CMO digital twin 60628 may be configured to simulate marketing campaigns, such that the simulations of the marketing campaign may vary parameters such as vehicles (e.g., social media, television, billboards, print, etc.), budget, targeting parameters (e.g., geographic, demographic, or the like), and/or other suitable marketing campaign parameters. In these embodiments, the digital twin simulation system 60320 may receive a request to perform the simulation CMO digital twin, where the request indicates campaign features and the parameters that are to be varied. In response, the digital twin simulation system 60320 may return the simulation results to the CMO digital twin 60628, which in turn outputs the results to the user via the client device display. In this way, the user is provided with various outcomes corresponding to different parameter configurations. In some embodiments, the user may select a parameter set based on the various outcomes. In some embodiments, an executive agent trained by the user may select the parameter sets based on the various outcomes.; [4367] a CMO digital twin 60628 may be configured to store, aggregate, merge, analyze, prepare, report and distribute material relating to market surveys, online surveys, customer panels, ratings, rankings, marketing trend data or other data related to marketing. A CMO digital twin 60628 may link to, interact with, and be associated with external data sources, and able to upload, download, aggregate external data sources, including with the EMP's internal data, and analyze such data, as described herein. Data analysis, machine learning, AI processing, and other analysis may be coordinated between the CMO digital twin 60628 and an analytics team based at least in part on using the artificial intelligence services system 60012. This cooperation and interaction may include assisting with seeding data elements and domains in the enterprise data store 60014 for use in modeling, machine learning, and AI processing to identify the optimal marketing content, sales channels, target consumers, price points, timing, or some other marketing-relating metric or aspect, as well as identification of the optimal data measurement parameters on which to base judgment of a marketing endeavor's success. Examples of data sources 60030 that may be connected to, associated with, and/or accessed from the CMO digital twin 60628 may include, but are not limited to, a sensor system 60032, a sales database 60034 that is updated with sales figures in real time, a CRM system 60038, a marketing campaign platform 60040, news websites, a financial database 60048 that tracks costs of the business, surveys 60050 (e.g., customer satisfaction surveys), an org chart 60052, a workflow management system 60054, customer databases 60062 structured to store customer data, and/or third-party datastores 60060 structured to store third-party data.);
and iteratively optimizing market research parameters by activating the one or more AI agents within the MRS based on incoming data and feedback. ([0299] the system may learn to recognize faults, to recognize patterns, to develop models or functions, to develop rules, to optimize performance, to minimize failure rates, to optimize profits, to optimize resource utilization, to optimize flow (such as flow of traffic), or to optimize many other parameters that may be relevant to successful outcomes (such as outcomes in a wide range of environments). Machine learning may use genetic programming techniques, such as promoting or demoting one or more input sources, structures, data types, objects, weights, nodes, links, or other factors based on feedback (such that successful elements emerge over a series of generations). For example, alternative available sensor inputs for a data collection system 102 may be arranged in alternative configurations and permutations, such that the system may, using generic programming techniques over a series of data collection events, determine what permutations provide successful outcomes based on various conditions (such as conditions of components of the platform 100, conditions of the network 110, conditions of a data collection system 102, conditions of an environment 104), or the like.; [0387] By continuously adjusting parameters to cause outputs to match actual conditions, the machine learning facility may self-organize to provide a highly accurate model of the conditions of an environment (such as for predicting faults, optimizing operational parameters, and the like).).
Cella doesn’t explicitly teach:
requesting modification or additional information in the data based on the analysis using the one or more AI agents within the MRS, responsive to the data not being determined as sufficient to complete the research objective;
Balaji teaches:
requesting modification or additional information in the data based on the analysis using the one or more AI agents within the MRS, responsive to the data not being determined as sufficient to complete the research objective; ([0010] Autonomous digital agents in the proposed evaluation system would evaluate the accuracy of collected data arriving from various data collection channels using machine learning models. Further, example autonomous digital agents also use machine learning models to determine whether to obtain additional and/or replacement data when previously collected data is found to be inaccurate and/or otherwise unreliable, thereby enabling the iterative improvement of the data collection accuracy.
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine Cella with Balaji’s feature(s) listed above. One would’ve been motivated to do so in order to evaluate and iteratively improve market research data collection accuracy based on the patterns of various data collection features (or attributes) (Balaji; [0010]). By incorporating the teachings of Balaji, one would’ve been able to determine if more data to complete the research objectives.
Regarding claim 2: Cella teaches:
wherein the internal information retrieval comprises simulating user personas and gathering insights into user preferences and behaviors. ([4247] Executive digital twins may refer to digital twins that are configured for a respective executive within an enterprise. Examples of executive digital twins may include CEO digital twins, CFO (Financial) digital twins, COO (Operations) digital twins, HR digital twins, CTO (Technology) digital twins, CMO (Marketing) digital twins, General Counsel (Legal) digital twins, CIO (Information) digital twins, and the like… an artificial intelligence system may be trained, such as on a labeled industry-specific or domain-specific data set, to automatically generate an industry-specific or domain-specific digital twin for an instance of an EMP for an organization).
Regarding claim 3: Cella doesn’t explicitly teach:
wherein if the internal information retrieval is available, the clarification questions are provided to gather additional details to refine the research,
and wherein if the internal information retrieval is not available, the clarification questions are provided to establish a baseline of the context.
Balaji teaches:
wherein if the internal information retrieval is available, the clarification questions are provided to gather additional details to refine the research, ([0010] example autonomous digital agents also use machine learning models to determine whether to obtain additional and/or replacement data when previously collected data is found to be inaccurate and/or otherwise unreliable, thereby enabling the iterative improvement of the data collection accuracy.; [0020] Additionally or alternatively, in some examples, particular individuals (e.g., store managers and/or employees, auditors, etc.) may enter their observations directly onto the data collectors 106 (e.g., via a keyboard and/or touchscreen) as part of the data collection process.);
and wherein if the internal information retrieval is not available, the clarification questions are provided to establish a baseline of the context. ([0020] Additionally or alternatively, in some examples, particular individuals (e.g., store managers and/or employees, auditors, etc.) may enter their observations directly onto the data collectors 106 (e.g., via a keyboard and/or touchscreen) as part of the data collection process.); [0023] when collection information is determined to be inaccurate (e.g., the discrepancy between predicted and actual values satisfies a threshold), the market research entity 108 may generate a work order or request for new collection information to be obtained. For example, the market research entity 108 may provide instructions to an auditor to return to a particular store 104 associated with the inaccurate collection information and re-collect the relevant information. Obtaining replacement collection information in this manner can be cost prohibitive. Furthermore, as noted above, particular events and/or circumstances may create situations where original collection information and/or replacement collection information is not available. Accordingly, in some examples, the market research entity 108 may generate simulated, synthetic, or synthesized data to replace inaccurate collection information in lieu of obtaining replacement collection information and/or to provide additional collection information when such information is otherwise unavailable for a particular period of interest. In some examples, the synthesized data is generated based on the application of a machine learning model to historical collection information for a particular store 104 of interest and/or for other similar stores 104.).
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Cella with Balaji’s feature(s) listed above. One would’ve been motivated to do so in order to enhance market research data collection quality (Balaji; [Abstract]). By incorporating the teachings of Balaji, one would’ve been able to get additional information by asking for clarification.
Regarding claim 4: Cella doesn’t explicitly teach:
further comprising adjusting the strategy formation based on feedback received from the user.
Balaji teaches:
further comprising adjusting the strategy formation based on feedback received from the user. ([0020] particular individuals (e.g., store managers and/or employees, auditors, etc.) may enter their observations directly onto the data collectors 106 (e.g., via a keyboard and/or touchscreen) as part of the data collection process.); [0038] this final data (e.g., after all iterations through the process) is provided to the example report generator 224 to generate a report. The report may be provided (e.g., transmitted via the communications interface 202) to the product provider(s) 102 and/or the stores(s) 104 to use as appropriate (e.g., adjust marketing campaigns, restock inventory, etc.).;
It would have been obvious to one of ordinary skill in the art, at the time of applicant’s invention, to combine modified Cella with Balaji’s feature(s) listed above. One would’ve been motivated to do so in order to provide to the data packet database 218 new and/or additional data packets that may be analyzed (Balaji; [0038]). By incorporating the teachings of Balaji, one would’ve been able to adjust the strategy.
Regarding claim 5/16: Cella teaches:
wherein the context relevant to the query comprises information about the user including target demographics, company goals, pricing strategies, and product features. ([4357] In embodiments, the types of data that may populate and/or be utilized by a CMO digital twin 60628 may include, but are not limited to, macroeconomic data; market pricing data; competitive product and pricing data; microeconomic analytic data; forecast data; demand planning data; competitive matrix data; product roadmap; product capability data; consumer; consumer profile data; collaborative filtering data; analytic results of AI and/or machine learning modeling; channel data; demographic data; geographic data; prediction data; recommendation data, or some other type of data relevant to the operations of the CMO and/or marketing department.).
Regarding claim 7/18: Cella doesn’t explicitly teach:
iteratively finetuning the one or more AI agents within the MRS based on the data, user feedback, and predefined optimization criteria. ([0299] Machine learning may be used to improve the foregoing, such as by adjusting one or more weights, structures, rules, or the like (such as changing a function within a model) based on feedback (such as regarding the success of a model in a given situation) or based on iteration (such as in a recursive process).; [0424] Embodiments include training a model to identify preferred sensor sets to diagnose a condition of an industrial environment, where a training set is created by a human user and the model is improved based on feedback from data collected about conditions in an industrial environment.; [0430] As noted above, methods and systems are disclosed herein for training AI models based on industry-specific feedback, such as that reflects a measure of utilization, yield, or impact, where the AI model operates on sensor data from an industrial environment.; [0980] This may include optimizing the coordination using an expert system, such as a rule-based optimization, a model-based optimization, or optimization using machine learning.).
Regarding claim 8/19: Cella doesn’t explicitly teach:
wherein the structured reports provided to the user are generated based on predefined templates utilizing the one or more AI agents within the MRS to fill in relevant data and analysis. ([0482] In embodiments, an expert analysis module 5100 may generate reports 5102 that may use machine or measurement point specific information from the information store 5040 to analyze the stream data 5050 using a stream data analyzer module 5104 and the local data control application 5062 with the extract/process (“EP”) align module 5068. In embodiments, the expert analysis module 5100 may generate new alarms or ingest alarm settings into an alarms module 5108 that is relevant to the stream data 5050. In embodiments, the stream data analyzer module 5104 may provide a manual or automated mechanism for extracting meaningful information from the stream data 5050 in a variety of plotting and report formats.).
Regarding claim 9/20: Cella teaches a computer system ([4632] The methods and systems described herein may be deployed in part or in whole through a machine that executes computer software, program codes, and/or instructions on a processor.) and a non-transitory computer-readable storage medium storing computer-executable instructions ([0988] In embodiments, one or more non-transitory computer-readable media comprising computer executable instructions) for automating market research with the following limitations:
receive a query from a user, wherein the query comprises a standard report, a research objective and/or one or more questions; ([4365] the CMO digital twin 60628 may be configured to simulate marketing campaigns, such that the simulations of the marketing campaign may vary parameters such as vehicles (e.g., social media, television, billboards, print, etc.), budget, targeting parameters (e.g., geographic, demographic, or the like), and/or other suitable marketing campaign parameters. In these embodiments, the digital twin simulation system 60320 may receive a request to perform the simulation CMO digital twin, where the request indicates campaign features and the parameters that are to be varied.);
collect context relevant to the query based on availability of internal information retrieval by querying the user with one or more clarification questions from the user using one or more Artificial Intelligence (AI) agents; ([4365] In response, the digital twin simulation system 60320 may return the simulation results to the CMO digital twin 60628, which in turn outputs the results to the user via the client device display. In this way, the user is provided with various outcomes corresponding to different parameter configurations. In some embodiments, the user may select a parameter set based on the various outcomes. In some embodiments, an executive agent trained by the user may select the parameter sets based on the various outcomes.);
form a strategy to retrieve data based on available data sources based on the context, wherein the strategy formation comprising: determining reliability of the available data sources and prioritize the available data sources according to their relevance of the available data sources for the research objective; ([4366] The CMO digital twin 60628 may utilize the machine learning, A.I. and other analytic capabilities, as described herein, to analyze the content of the four categories of content and classify and score the content characteristics that are probabilistically associated with improved financial or other performance for stated types of marketing campaigns or marketing subject matter.);
retrieve data from the available data sources based on the strategy, ([4371] In embodiments, a CMO digital twin 60628 may be configured to monitor, store, aggregate, merge, analyze, prepare, report and distribute material relating to competitors of a CMO's organization, or named entities of interest. In embodiments, such data may be collected by the EMP 60000 via data aggregation, spidering, web-scraping, or other techniques to search and collect competitor information from sources including, but not limited to, press releases, SEC or other financial reports, mergers and acquisitions activity, or some other publicly available data.);
the available data sources comprising non-agent sources such as web scraping, Application Programming Interface (API) endpoints, web browsing, and uploaded files, as well as agent-based sources such as synthetic focus groups, synthetic surveys, and autonomous survey collection; ([4367] In embodiments, a CMO digital twin 60628 may be configured to store, aggregate, merge, analyze, prepare, report and distribute material relating to market surveys, online surveys, customer panels, ratings, rankings, marketing trend data or other data related to marketing. A CMO digital twin 60628 may link to, interact with, and be associated with external data sources, and able to upload, download, aggregate external data sources, including with the EMP's internal data, and analyze such data, as described herein. Data analysis, machine learning, AI processing, and other analysis may be coordinated between the CMO digital twin 60628 and an analytics team based at least in part on using the artificial intelligence services system 60012. This cooperation and interaction may include assisting with seeding data elements and domains in the enterprise data store 60014 for use in modeling, machine learning, and AI processing to identify the optimal marketing content, sales channels, target consumers, price points, timing, or some other marketing-relating metric or aspect, as well as identification of the optimal data measurement parameters on which to base judgment of a marketing endeavor's success. Examples of data sources 60030 that may be connected to, associated with, and/or accessed from the CMO digital twin 60628 may include, but are not limited to, a sensor system 60032, a sales database 60034 that is updated with sales figures in real time, a CRM system 60038, a marketing campaign platform 60040, news websites, a financial database 60048 that tracks costs of the business, surveys 60050 (e.g., customer satisf