DETAILED ACTION
Status of the Application
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 .
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 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
This action is a Final Action on the merits in response to the application filed on 05/26/2026.
Claims 1, 2, 8, 9, 12-14, 25, 26, and 30 have been amended.
Claims 3, 6, 10 and 17 have been cancelled.
Claim 31 has been added.
Claims 1, 2, 4, 5, 7-9, 11-16, 18-31 remain pending in this application.
Response to Amendment
Applicant’s amendments are acknowledged.
The 35 U.S.C. 101 rejections of claims in the previous office action have been maintained.
The 35 U.S.C. 102 rejections of claims in the previous office action are withdrawn in light of applicant’s amendments.
The 35 U.S.C. 103 rejections of claims in the previous office action have been maintained.
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, 2, 4, 5, 7, 13-16, 18-24, 31 are directed towards a method, claims 8-11, 25-29 are directed towards a system, and claims 12, 30 directed towards a computer-readable media, all of which are among the statutory categories of invention.
Claims 1, 2, 4, 5, 7-9, 11-16, 18-31 are rejected under 35 U.S.C. 101 because the claims are directed to a judicial exception without significantly more.
Step 1: This part of the eligibility analysis evaluates whether the claim falls within any statutory category. See MPEP 2106.03. The claim recites at least one step or act, including generating a business plan to artificial intelligence. Thus, the claim is to a process, which is one of the statutory categories of invention. (Step 1: YES).
Step 2A, Prong One: This part of the eligibility analysis evaluates whether the claim recites a judicial exception. As explained in MPEP 2106.04, subsection II, a claim “recites” a judicial exception when the judicial exception is “set forth” or “described” in the claim.
With respect to claims 1, 2, 4, 5, 7-9, 11-16, 18-31, the independent claims (claims 1, 8, 12, 13, 25, and 30) are directed to managing business information, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
Claim 1, A method for generating a business plan for a company, the method comprising:
receiving a business name associated with the company;
obtaining, by a first artificial intelligence (Al) agent, a business description for the company based on the business name, the obtaining comprising:
obtaining at least one of a competitor, a supplier or a customer associated with the company;
identifying a website associated with the at least one of the company, competitor, the supplier or the customer.
these steps fall within and recite an abstract ideas because they are directed to a method of organizing human activity which includes commercial interaction such as business relations (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction, then it falls within the “method of organizing human activity” grouping of abstract ideas. Therefore, If the identified limitation(s) falls within any of the groupings of abstract ideas enumerated in the MPEP 2106, the analysis should proceed to Prong Two. (Step 2A, Prong One: YES).
Step 2A, Prong Two: This part of the eligibility analysis evaluates whether the claim as a whole integrates the recited judicial exception into a practical application of the exception or whether the claim is “directed to” the judicial exception. This evaluation is performed by (1) identifying whether there are any additional elements recited in the claim beyond the judicial exception, and (2) evaluating those additional elements individually and in combination to determine whether the claim as a whole integrates the exception into a practical application. See MPEP 2106.04(d). The claim recites the additional elements of AI (Claim 8 memory, processor, AI; Claim 12 computer readable media, AI; Claim 13 AI; Claim 25 system, memory, processor, AI). The claims recite the steps are performed by the AI.
The limitations of
extracting details from the website of the at least one of the company, competitor, the supplier or the customer; and
generating, by a second Al agent, a business plan for the company based on the business description, wherein the business plan for the company comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation.
are mere data gathering and output recited at a high level of generality, and thus are insignificant extra-solution activity. See MPEP 2106.05(g) (“whether the limitation is significant”). In addition, all uses of the recited judicial exceptions require such data gathering and output, and, as such, these limitations do not impose any meaningful limits on the claim. These limitations amount to necessary data gathering and outputting. See MPEP 2106.05.
Further, the limitations are recited as being performed by AI. The AI is recited at a high level of generality. In limitation (a), the artificial intelligence is used as a tool to perform the generic computer function of receiving data. See MPEP 2106.05(f). The artificial intelligence is used to perform an abstract idea, as discussed above in Step 2A, Prong One, such that it amounts to no more than mere instructions to apply the exception using a generic computer. See MPEP 2106.05(f). Additionally, claim 1 recites artificial intelligence. The general use of an artificial intelligence does not provide a meaningful limitation to transform the abstract idea into a practical application.
Even when viewed in combination, these additional elements do not integrate the recited judicial exception into a practical application (Step 2A, Prong Two: NO), and the claim is directed to the judicial exception. (Step 2A: YES).
Step 2B: This part of the eligibility analysis evaluates whether the claim as a whole amounts to significantly more than the recited exception i.e., whether any additional element, or combination of additional elements, adds an inventive concept to the claim. See MPEP 2106.05. As explained with respect to Step 2A, Prong Two, the additional elements are the AI. The additional elements were found to be insignificant extra-solution activity in Step 2A, Prong Two, because they were determined to be insignificant limitations as necessary data gathering and outputting. Then, the artificial intelligence recited in the claim are disclosed at a high-level of generality (see at least Specification [0099 “ AI/machine learning (ML) predictor, which may receive and/or generate data, models and software applications. AI/ML predictor may also store its inputs and outputs in a memory of analytics engine 104′. As used herein, the term artificial intelligence (AI) also includes machine learning”]) and does not amount to significantly more than the abstract idea.
However, a conclusion that an additional element is insignificant extra solution activity in Step 2A, Prong Two should be re-evaluated in Step 2B. See MPEP 2106.05, subsection I.A. At Step 2B, the evaluation of the insignificant extra-solution activity consideration takes into account whether or not the extra-solution activity is well understood, routine, and conventional in the field. See MPEP 2106.05(g). As discussed in Step 2A, Prong Two above, the recitations of
extracting details from the website of the at least one of the company, competitor, the supplier or the customer; and
generating, by a second Al agent, a business plan for the company based on the business description, wherein the business plan for the company comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation.
are recited at a high level of generality. These elements amount to transmitting and processing data and are well understood, routine, conventional activity. See MPEP 2106.05(d), subsection II. 10 As discussed in Step 2A, Prong Two above, the recitation of an AI to perform limitations amounts to no more than mere instructions to apply the exception using a generic computer component. Even when considered in combination, these additional elements represent mere instructions to implement an abstract idea or other exception on a computer and insignificant extra-solution activity, which do not provide an inventive concept. (Step 2B: NO).
Dependent claims 2, 4, 5, 7, 9, 11, 14-16, 18-24, 26-29, 30, and 31 do not contain any new additional elements. Rather, these claims offer further descriptive limitations of elements found in the independent claims. In this case, the claims are rejected for the same reasons at step 2a, prong one; step 2a, prong 2; and step 2b. Thus, the claim is not patent eligible.
Regarding the dependent claims, dependent claims 4, 22 recites LLM, MLLM; claims; claim 20 recite a software application for executing a business plan; claims 21, 29, 31 recite AI for receiving human input. The dependent claims 2, 4, 5, 7, 9, 11, 14-16, 18-24, 26-29, 30, and 31 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2, 4, 5, 7, 9, 11, 14-16, 18-24, 26-29, 30, and 31 recites AI, LLM, MLLM, memory, processor, computer readable media which are considered an insignificant extra-solution activities of collecting and analyzing data; see MPEP 2106.05(g). Claims 2, 4, 5, 7, 9, 11, 14-16, 18-24, 26-29, 30, and 31 recites AI, LLM, MLLM, memory, processor, computer readable media, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2, 4, 5, 7, 9, 11, 14-16, 18-24, 26-29, 30, and 31 recite steps that further narrow the abstract idea. No additional elements are disclosed in the dependent claims that were not considered in independent claims 1, 8, 12, 13, 25, and 30. Therefore claims 2, 4, 5, 7, 9, 11, 14-16, 18-24, 26-29, 30, and 31 do not provide meaningful limitations to transform the abstract idea into a patent eligible application of the abstract idea such that the claims amount to significantly more than the abstract idea itself.
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 of this title, 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, 2, 4, 5, 7-9, 11-16, 18-31 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20240249229, Rao, to hereinafter Rao in view of United States Patent Publication US 20220405775, Siebel et al.,
Referring to Claim 1, Rao teaches a method for generating a business plan for a company, the method comprising:
receiving a business name associated with the company (
Rao: Sec. 0661, The BP intake form comprises fields input BP name, reference Id.
Rao: Sec. 0669, The BP aggregate form comprises fields initiative name, initiative sponsor, funding stage, initiative co-sponsors and enables users to provide appropriate inputs. The BP aggregate form also enables users to aggregate relevant BPs under an initiative. The BP aggregate form lists out BP ID, BP name, objective and enables users to provide inputs such as T-shirt size, target completion, and expertise gap and conflicts.
Rao: Sec. 0675, The initiative funding request template shown in FIG. 30 a comprises fields such as initiative name, initiative priority, stage, initiative sponsor, and required approvals. The initiative name field enables the user to input the relevant initiative name. The initiative name field enables the user (sponsor) to set the initiative priority.);
obtaining, by a first artificial intelligence (Al) agent, a business description for the company based on the business name (
Rao: Sec. 0226, The processor 104 using the artificial intelligence engine creates the one or more initiatives from the one or more business propositions by executing the following technical steps. The processor 104 analyzes the one or more business propositions and identifies the one or more business propositions that are relevant (for example, in the same field, same industry, same technology, same interests, same line of business, same operations etc.). The processor 104 analyzes the one or more business propositions by interpreting the contents of the one or more business propositions using natural language processing. In one embodiment, the processor is operable to interpret written text using the artificial intelligence engine through natural language processing.
Rao: Sec. 0582, the initiative “enhance client experience” comprises the one or more business propositions (for example, client self-service, and enhance client capabilities) as shown. The initiative “enhance client experience” is assigned with an ID (for example, INIT010). The initiative “enhance client experience” is created with the business propositions “client self-service,” and “enhance client capabilities.” The processor, through the user interface, depicts the business propositions grouped under the initiative upon clicking the respective initiative. The user interface further depicts details of the business propositions associated with the respective initiative. The details comprise at least one of primary objectives, effort estimates (L1 estimate, L2 estimate, etc.), working hours range (referred to as T-shirt size), target completion, and expertise gaps and conflicts.
Rao: Sec. 0623, The enhancement backlog tool is configured to maintain backlog of solution enhancements and Id, description, package, capability and function, benefits, priority, effort estimate, additional cost, status. );
Rao describe the use of AI for identifying a company.
and
generating, by a second Al agent, a business plan for the company based on the business description (
Rao: Sec. 0227, The processor 104 identifies the one or more business propositions that are relevant based on the analysis of the contents. The processor 104 using the artificial intelligence engine groups the one or more identified business propositions and creates one or more initiatives. In an embodiment, the processor 104 groups the one or more business propositions and creates the one or more initiatives based on at least one of the business objectives, scope, business outcomes, total budget, total resources incurred for those business propositions. The initiatives comprise more details than the business propositions. The initiatives comprise at least one of primary objectives, effort estimates (L1 estimate, L2 estimate, etc.), working hours range, target completion, and expertise gaps and conflicts. In an embodiment, the processor 104 automatically groups the one or more business propositions and creates the one or more initiatives (as shown in FIG. 6 ). In another embodiment, the processor 104 enables the users to manually select and group the one or more business propositions and create the one or more initiatives. The processor 104 enables the users to manually select and drop the one or more business propositions to create the one or more initiatives (as shown in FIG. 6 ). The processor 104 creates the one or more initiatives for one or more categories (for example, geographies, countries, departments, markets, etc.).).
Rao describe the use of AI for providing the details of a business plan, in which the Examiner is interpreting as business description.
wherein the business plan for the company comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation (
Rao: Sec. 0236, Advanced analytics techniques often go beyond traditional statistical methods and basic data analysis, leveraging technologies such as machine learning, artificial intelligence, predictive modeling, data mining, and optimization algorithms. Some common applications of advanced analytics include the following. Predictive analytics: The system performs predictive analytics using historical data to make predictions about future events or outcomes. The predictive analytics could include forecasting sales, predicting customer churn, or anticipating equipment failures. Prescriptive analytics: The system performs prescriptive analytics by recommending actions to optimize outcomes based on analysis of data and constraints.
Rao: Sec. 0581, In one embodiment, the processor identifies the business propositions and recommends to the users by highlighting the identified business propositions and further enables the users to select and/or deselect the business propositions as per his/her objectives.
Rao: Sec. 0585, The processor using the artificial intelligence engine analyzes the one or more initiatives and assigns the priority for the initiatives. In an embodiment, the processor may assign the priority and recommend the initiatives based on priority. The processor may assign the priority based on at least one of budget, resource, and scope of the initiatives. The processor may enable the user to provide funding based on the priority assigned.
Rao: Sec. 0601, The outcome of this session is to confirm the aggregate list of initiatives and RTB for alignment with the business objectives for the plan year and to discuss execution viability with available resources. Concurrently, in step seven, the processor may rationalize the initiatives. During the fifth executive planning session (EPS #5), executives review the aggregate funding plan across EVIs and RTB. In this session, the processor may model various funding scenarios and recommend a short list of high probability initiatives that would be included in the business plan.).
Rao does not explicitly teach the obtaining comprising:
obtaining at least one of a competitor, a supplier or a customer associated with the company; identifying a website associated with the at least one of the company, competitor, the supplier or the customer; and extracting details from the website of the at least one of the company, competitor, the supplier or the customer.
However, Siebel teaches these limitations
the obtaining comprising:
obtaining at least one of a competitor, a supplier or a customer associated with the company (
Siebel: Sec. 0299, The data sources 1202 also include information about known engagements of the company and its competitors with the customers.);
identifying a website associated with the at least one of the company, competitor, the supplier or the customer (
Siebel: Sec. 0375, Here, the user interface 2000 includes an account details section 2002, which identifies various information about the customer. In this example, the information about the customer includes a name and description of the customer, revenue and contact information of the customer, a location of the customer, a number of employees for the customer, and a website for the customer.); and
extracting details from the website of the at least one of the company, competitor, the supplier or the customer (
Siebel: Sec. 0119, That is, the AI-based CRM engine function 244 may operate by using one or more machine learning models to generate probabilities of certain results or other outputs, and these outputs are typically based on features extracted from input data by the machine learning pipeline.
Siebel: Sec. 0350, The one or more machine learning models are used with at least one CRM function to calculate transaction opportunities, and the machine learning model features can be extracted from outputs of the one or more machine learning models
Siebel: Sec. 0357, the most successful Internet self-service customer journey (navigation) and provide signals to website clients on which journey will achieve a desired customer outcome (such as successful sales or other transactions, rapid delivery of support, or streamlined browsing to find the most relevant data on a website).
Siebel: Sec. 0426, The additional data sources 3004 that can be used with AI-based CRM functions may also include sources providing information such as customer communications, customer website interactions, energy market data, maintenance or service logs, social media content, customer news, drone or satellite imagery, and historical or forecasted weather.
Siebel: Sec. 0429, Customer engagement functionality can be used to unify customer service experiences across all channels (such as call centers, websites, direct mailings, and emails) and leverage machine learning to deliver personalized messaging at the right time through the right channel.
Siebel: Sec. 0440, Additional data sources 3704 that can be used with AI-based CRM functions may also include sources providing information such as distributed energy resource data, historical and forecasted weather data, satellite imagery, social media content, and customer website interactions.).
Rao and Siebel are both directed to the analysis of the use of artificial intelligence (See Rao at 0236, 0223-0227; Siebel at 0090, 0112, 0202). Rao discloses that additional elements, such as an artificial intelligence engine can be considered (See Rao at 0035). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao, which teaches detecting and repairing information technology problems in view of Siebel, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Siebel at 0349, 0362, 0363).
Referring to Claim 2, Rao teaches the method of claim 1, wherein the business description comprises at least one of an industry, the website, revenue, a number of employees, the supplier, the customer or the competitor (
Rao: Sec. 0226, The processor 104 analyzes the one or more business propositions and identifies the one or more business propositions that are relevant (for example, in the same field, same industry, same technology, same interests, same line of business, same operations etc.).
Rao: Sec. 0580, The processor identifies the one or more business propositions that are similar in interests, industry, field, budget, etc. The processor groups the business propositions identified and creates the initiatives. The initiatives refers to the next stage of the business propositions that has been shortlisted and sent for seeking funding. The processor creates the one or more initiatives for one or more categories (e.g., demographics, market affiliations, departments, etc.).).
Referring to Claim 4, Rao teaches the method of claim 1, Rao does not explicitly teach wherein the first agent and the second agent each comprise a large language model (LLM) or a multi-model large language model (MLLM).
However, Siebel teaches wherein the first agent and the second agent each comprise a large language model (LLM) or a multi-model large language model (MLLM) (
Siebel: Sec. 0090, The AI-based system's machine learning approaches are also able to “self-learn” as the system recognizes patterns in the data (such as which factors are common in deals that close, the amount of a given transaction, or which customers chum) to update its own machine learning model(s) and use these machine learning insights as an additional exogenous factor (typically with some significance) in making future predictions.
Siebel: Sec. 0125, The components 252-256 support the use of machine learning, which enables the development of self-learning algorithms and analytics.).
Siebel describes the machine learning trained to perform one or more tasks for analytics engine.
Rao and Siebel are both directed to the analysis of the use of artificial intelligence (See Rao at 0236, 0223-0227; Siebel at 0090, 0112, 0202). Rao discloses that additional elements, such as an artificial intelligence engine can be considered (See Rao at 0035). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao, which teaches detecting and repairing information technology problems in view of Siebel, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Siebel at 0349, 0362, 0363).
Referring to Claim 5, Rao teaches the method of claim 1, further comprising receiving human input on the business plan and modifying the business plan based on the human input (
Rao: Sec. 0708, Where a model gives an image a high probability score, it is auto labelled with the predicted concept. However, in the cases where the model returns a low probability score, this input may be sent to a controller (may be a human moderator) which verifies and, as necessary, corrects the result. The human moderator may be used only in exception cases. ).
Referring to Claim 7, Rao teaches the method of claim 1, wherein the business plan for the company comprises at least one of a strategic plan, a research and development plan, a marketing plan, an operational plan, a supply chain plan or a financial plan (
Rao: Sec. 0250, The program management framework defines the organization's strategic objectives and goals; identifies key initiatives or programs required to achieve those objectives; and ensures that each program directly contributes to the organization's overall strategic plan.
strategic plan
Rao: Sec. 0647, The processor then creates the initiatives from the business propositions. The scope definition, design of business plan, development of initiatives, updating initiatives, etc. takes place at this stage. The business planning and project management executes at this stage.
development plan).
Claims 8, 9, and 11 recite limitations that stand rejected via the art citations and rationale applied to claims 1, 2, and 7. Regarding, a system for generating a business plan for a company, the system comprising:
a memory; at least one processor (
Rao: Sec. 0598, structure and classification of components of the business planning method and workflow, according to one or more embodiments. The system comprises a business plan creation phase and program execution phase. The system comprises a processor and a memory. The processor receives information from the users and/or the agents. The processor creates the business plan based on the information. The information may comprise contents related to vision, concept, strategy, and plan. The processor creates the business plan based on the information at the program execution phase):
Claim 12 recites limitations that stand rejected via the art citations and rationale applied to claim 1. Regarding, one or more non-transitory computer readable media storing computer- executable instructions thereon that, when executed by at least one computer, cause the at least one computer to perform a method (
Rao: Sec. 0151, structure and classification of components of the business planning method and workflow, according to one or more embodiments. The system comprises a business plan creation phase and program execution phase. The system comprises a processor and a memory. The processor receives information from the users and/or the agents. The processor creates the business plan based on the information. The information may comprise contents related to vision, concept, strategy, and plan. The processor creates the business plan based on the information at the program execution phaseEmbodiments within the scope of the present invention may also include physical and other computer readable media for carrying or storing computer-executable instructions and/or data structures. Such computer readable media can be any media accessible by a general purpose or special purpose computer system. Computer readable media that store computer-executable instructions are physical storage media. Computer readable media that carry computer-executable instructions are transmission media. Thus, by way of example and not limitation, embodiments of the invention can comprise at least two distinct kinds of computer readable media: physical computer readable storage media and transmission computer readable media.))
Referring to Claim 4, Rao teaches the method of claim 1, Rao does not explicitly teach wherein the first agent and the second agent each comprise a large language model (LLM) or a multi-model large language model (MLLM).
However, Siebel teaches wherein the first agent and the second agent each comprise a large language model (LLM) or a multi-model large language model (MLLM) (
Siebel: Sec. 0090, The AI-based system's machine learning approaches are also able to “self-learn” as the system recognizes patterns in the data (such as which factors are common in deals that close, the amount of a given transaction, or which customers chum) to update its own machine learning model(s) and use these machine learning insights as an additional exogenous factor (typically with some significance) in making future predictions.
Siebel: Sec. 0125, The components 252-256 support the use of machine learning, which enables the development of self-learning algorithms and analytics.).
Siebel describes the machine learning trained to perform one or more tasks for analytics engine.
Rao and Siebel are both directed to the analysis of the use of artificial intelligence (See Rao at 0236, 0223-0227; Siebel at 0090, 0112, 0202). Rao discloses that additional elements, such as an artificial intelligence engine can be considered (See Rao at 0035). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao, which teaches detecting and repairing information technology problems in view of Siebel, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Siebel at 0349, 0362, 0363).
Claim 10 recites limitations that stand rejected via the art citations and rationale applied to claim 3.
Referring to Claim 13, Rao teaches the method for executing a business plan for a company, the method comprising:
receiving, at a first artificial intelligence (Al) agent, input describing the company (
Rao: Sec. 0637, The step, finalize business plan is adapted to enable decision makers and senior executives to finalize the business plan for the following year. The step, finalize business plan enables to convert “Benefits” to KPIs based on user input; create a business plan and enable decision makers and senior executives to update metrics.
Rao: Sec. 0661, rendering business proposition (BP) intake form, according to one or more embodiments. The BP intake form is rendered via the user interface to receive inputs. The inputs may be received from the users. The BP intake form comprises fields input BP name, reference Id. The BP intake form depicts the sponsoring department at the top right corner. The BP intake form also enables users to provide relevant BP IDs. The BP intake form also enables users to describe BP target state. The BP intake form also enables users to describe major business impact. );
wherein the business plan for the company comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation (
Rao: Sec. 0236, Advanced analytics techniques often go beyond traditional statistical methods and basic data analysis, leveraging technologies such as machine learning, artificial intelligence, predictive modeling, data mining, and optimization algorithms. Some common applications of advanced analytics include the following. Predictive analytics: The system performs predictive analytics using historical data to make predictions about future events or outcomes. The predictive analytics could include forecasting sales, predicting customer churn, or anticipating equipment failures. Prescriptive analytics: The system performs prescriptive analytics by recommending actions to optimize outcomes based on analysis of data and constraints.
Rao: Sec. 0581, In one embodiment, the processor identifies the business propositions and recommends to the users by highlighting the identified business propositions and further enables the users to select and/or deselect the business propositions as per his/her objectives.
Rao: Sec. 0585, The processor using the artificial intelligence engine analyzes the one or more initiatives and assigns the priority for the initiatives. In an embodiment, the processor may assign the priority and recommend the initiatives based on priority. The processor may assign the priority based on at least one of budget, resource, and scope of the initiatives. The processor may enable the user to provide funding based on the priority assigned.
Rao: Sec. 0601, The outcome of this session is to confirm the aggregate list of initiatives and RTB for alignment with the business objectives for the plan year and to discuss execution viability with available resources. Concurrently, in step seven, the processor may rationalize the initiatives. During the fifth executive planning session (EPS #5), executives review the aggregate funding plan across EVIs and RTB. In this session, the processor may model various funding scenarios and recommend a short list of high probability initiatives that would be included in the business plan.).
And executing, by a third Al agent, the business plan (
Rao: Sec. 0061, when executed by a processor causes: receiving information related to one or more business propositions; creating one or more business propositions based on the information received; analyzing, using an artificial intelligence engine, the one or more business propositions and group the one or more business propositions for one or more categories; and creating, using the artificial intelligence engine, one or more initiatives for the one or more categories.
Rao: Sec. 0222, The processor renders the user interface that enables the users to perform intended functions (such as creating business propositions (BP), creating initiatives, collaborate, discuss, deploy BPs and initiatives into target environment, monitor execution of BPs, update BP and/or initiatives concurrently, control agents, etc.). The processor enables the users (executives, senior executives of organizations) to engage in constructive dialog to rationalize initiatives and create a portfolio of funded programs through the user interface.
Rao: Sec. 0224, The constraints refer to factors that impact the outcome of the business. The opportunities for improvement refers to factors that the organization should execute and/or overcome to achieve the goals
Rao: Sec. 0223, FIG. 1 illustrates a system according to one or more embodiments. The system 100 comprises: a memory 102; and a processor 104 that is communicatively coupled to the memory 102 storing a sequence of instructions that when executed causes the processor 104 to: receive information related to one or more business propositions (at step 103); create one or more business propositions based on the information received (at step 105); analyze, using an artificial intelligence engine, the one or more business propositions and group the one or more business propositions for one or more categories (at step 107); and create, using the artificial intelligence engine, one or more initiatives for the one or more categories (at step 109).
Rao: Sec. 0266, The method 200 comprises the following technical steps: receiving information related to one or more business propositions (at step 203); creating one or more business propositions based on the information received (at step 205); analyzing, using an artificial intelligence engine, the one or more business propositions and group the one or more business propositions for one or more categories (at step 207); and creating, using the artificial intelligence engine, one or more initiatives for the one or more categories (at step 209). In one embodiment, the method further comprises: receiving the information from at least one of one or more users and one or more agents.).
Rao describes executing goals of a business, which the Examiner is interpreting as executing business plans.
Rao does not explicitly teach determining, by the first Al agent, a business overview for the company based on the input; the determining comprising: obtaining at least one of a competitor, a supplier or a customer associated with the company; identifying a website associated with the at least one of the company, the competitor, the supplier or the customer; and extracting details from the website of the at least one of the company, the competitor, the supplier or the customer; determining, by a second Al agent, the business plan for the company based on the business overview, .
However, Siebel teaches these limitations
determining, by the first Al agent, a business overview for the company based on the input (
Siebel: Sec. 0441, Sales and tracking functionality can be used to provide an accessible overview of a company's supply, demand, and outlook with integrated price curves and machine learning capabilities to identify opportunities and help sales and trading staff make informed decision);
the determining comprising:
obtaining at least one of a competitor, a supplier or a customer associated with the company (
Siebel: Sec. 0299, The data sources 1202 also include information about known engagements of the company and its competitors with the customers.);
identifying a website associated with the at least one of the company, the competitor, the supplier or the customer (
Siebel: Sec. 0375, Here, the user interface 2000 includes an account details section 2002, which identifies various information about the customer. In this example, the information about the customer includes a name and description of the customer, revenue and contact information of the customer, a location of the customer, a number of employees for the customer, and a website for the customer.); and
extracting details from the website of the at least one of the company, the competitor, the supplier or the customer (
Siebel: Sec. 0357, the most successful Internet self-service customer journey (navigation) and provide signals to website clients on which journey will achieve a desired customer outcome (such as successful sales or other transactions, rapid delivery of support, or streamlined browsing to find the most relevant data on a website).
Siebel: Sec. 0426, The additional data sources 3004 that can be used with AI-based CRM functions may also include sources providing information such as customer communications, customer website interactions, energy market data, maintenance or service logs, social media content, customer news, drone or satellite imagery, and historical or forecasted weather.
Siebel: Sec. 0429, Customer engagement functionality can be used to unify customer service experiences across all channels (such as call centers, websites, direct mailings, and emails) and leverage machine learning to deliver personalized messaging at the right time through the right channel.
Siebel: Sec. 0440, Additional data sources 3704 that can be used with AI-based CRM functions may also include sources providing information such as distributed energy resource data, historical and forecasted weather data, satellite imagery, social media content, and customer website interactions.).
determining, by a second Al agent, the business plan for the company based on the business overview(
Siebel: Sec. 0372, an “Overview” option has been selected in the controls 1906, and an opportunity details section 1908 identifies various information about the opportunity. As shown here, this information may include the primary representative associated with the opportunity, a next step that is recommended to be taken for the opportunity, the age of the opportunity, the total number of days (or other time period) in which the opportunity has been in its current pipeline stage, a number of times that the opportunity has been raised with the customer (push count), and the product/service name(s)/volume(s)/price(s) associated with the opportunity. Only a summary or a portion of the opportunity details may be shown in the opportunity details section 1908, and a control 1910 may be used to expand the opportunity details section 1908 to view additional information about the opportunity or to contract the opportunity details section 1908.
Siebel: Sec. 0396, overview section 2502 that identifies various information about the precision revenue forecast for the team of representatives or any selected representative(s). Here, the information includes a location and a manager for the team or selected representative(s) and a current fiscal period for the precision revenue forecast. The information also includes an identification of the remaining time (such as number of days) until the end of the current fiscal period. In addition, the information includes overall values associated with opportunities for the team of representatives or any selected representative(s) in different categories, such as committed, best cast, and pipeline opportunities.);
Rao and Siebel are both directed to the analysis of the use of artificial intelligence (See Rao at 0236, 0223-0227; Siebel at 0090, 0112, 0202). Rao discloses that additional elements, such as an artificial intelligence engine can be considered (See Rao at 0035). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao, which teaches detecting and repairing information technology problems in view of Siebel, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Siebel at 0349, 0362, 0363).
Referring to Claim 14, Rao teaches the method of claim 13, wherein the input describing the company comprises at least one of a business name, an industry, the website, revenue, a number of employees, the supplier, the customer or the competitor (
Rao: Sec. 0226, The processor 104 analyzes the one or more business propositions and identifies the one or more business propositions that are relevant (for example, in the same field, same industry, same technology, same interests, same line of business, same operations etc.).
Rao: Sec. 0580, The processor identifies the one or more business propositions that are similar in interests, industry, field, budget, etc. The processor groups the business propositions identified and creates the initiatives. The initiatives refers to the next stage of the business propositions that has been shortlisted and sent for seeking funding. The processor creates the one or more initiatives for one or more categories (e.g., demographics, market affiliations, departments, etc.).
Rao: Sec. 0669, The BP aggregate form is rendered via the user interface to receive inputs. The inputs may be received from the users. The BP aggregate form comprises fields initiative name, initiative sponsor, funding stage, initiative co-sponsors and enables users to provide appropriate inputs. The BP aggregate form also enables users to aggregate relevant BPs under an initiative. The BP aggregate form lists out BP ID, BP name, objective and enables users to provide inputs such as T-shirt size, target completion, and expertise gap and conflicts. ).
Referring to Claim 15, Rao teaches the method of claim 13, wherein the business overview comprises at least one of a description of the company, an internal analysis of the company, an external landscape of an industry associated with the company, recommendations for the company, opportunities for the company or a prediction of sales for the company (
Rao: Sec. 0236, Advanced analytics techniques often go beyond traditional statistical methods and basic data analysis, leveraging technologies such as machine learning, artificial intelligence, predictive modeling, data mining, and optimization algorithms. Some common applications of advanced analytics include the following. Predictive analytics: The system performs predictive analytics using historical data to make predictions about future events or outcomes. The predictive analytics could include forecasting sales, predicting customer churn, or anticipating equipment failures.).
Referring to Claim 16, Rao teaches the method of claim 15, wherein the prediction of sales for the company is based on economic statistics (
Rao: Sec. 0236, Advanced analytics techniques often go beyond traditional statistical methods and basic data analysis, leveraging technologies such as machine learning, artificial intelligence, predictive modeling, data mining, and optimization algorithms. Some common applications of advanced analytics include the following. Predictive analytics: The system performs predictive analytics using historical data to make predictions about future events or outcomes. The predictive analytics could include forecasting sales, predicting customer churn, or anticipating equipment failures.
Rao: Sec. 0261, phase utilizes collaborative project management tools with real-time updates to develop detailed project plans, resource allocations, and timelines. The system, at this phase, implements real-time budget tracking and forecasting to monitor expenditure against allocated funds and identify potential budget overruns early.).
Claims 18 and 19 recite limitations that stand rejected via the art citations and rationale applied to claims 7 and 5.
Referring to Claim 20, Rao teaches the method of claim 13, wherein executing the business plan comprises at least one of developing software applications for the company, contracting with a supplier or marketing the company on social media (
Rao: Sec. 0221, the system is configured to perform business planning. The system comprises business planning taxonomy, protocols, models, algorithms, and software platform. In short, the system is configured to provide one on-line platform to enable all stakeholders across an enterprise to develop robust business plans using a uniform lexicon across departments, geographies, and markets. The system described herein enables users and organizations to plan for major investments in business, operations, and technology initiatives every year. The system is a cloud-native platform that enables collaboration by senior executives across geographies, lines of business, departments, and functions, using real-time decision-making methods and tools. The system enables businesses to scale more efficiently.
Rao describes executing the business plan using developing software.
Rao: Sec. 0236, A subset of artificial intelligence that enables systems to learn from data and improve performance without being explicitly programmed. Machine learning algorithms can be used for tasks such as image recognition, natural language processing, and anomaly detection. Data mining: Data mining refers to the process of discovering patterns and relationships in large datasets. Data mining can involve techniques such as clustering, classification, association rule mining, and anomaly detection. Text analytics: Text analytics include analyzing unstructured text data, such as customer reviews, social media posts, or survey responses, to extract insights and sentiment. Big data analytics: Big data analytics include handling and analyzing large volumes of data that traditional analytics tools may struggle to process. Big data analytics often involves distributed computing frameworks like Hadoop or Spark.).
Rao describes executing the business plan using social media posting.
Rao: Sec. 0287, 5) Key Activities are the essential list of activities that are typically performed by team members as part of RTB, in the furtherance of supporting the organization's roster of clients and to meet their contractual service levels with such clients.
Referring to Claim 21, Rao teaches the method of claim 13, further comprising:
receiving, at a fourth Al agent, at least one of human input or feedback on the business plan, the feedback comprising at least one of customer feedback, a competitor market shift or a performance outcome for the company (
Rao: Sec. 0269, communicating feedback information back to a processor based on the issues. The feedback information comprises at least one of a resource strategy, a scope strategy, a priority strategy, and a budget strategy. In one embodiment, the method 200 further comprises: updating at least one of the one or more business propositions and the one or more initiatives based on the feedback information. In one embodiment, the method 200 further comprises: updating at least one of scope, budget, resource, and priority in at least one of the one or more business propositions and the one or more initiatives based on the feedback information. In another embodiment, the method 200 further comprises: generating a real-time report using at least one of historical trends, historical patterns, historical records, the analysis, the issues, the feedback information, and the update performed.
Rao: Sec. 0583, The processor may receive feedback from the users. The processor then may update the initiatives based on the feedback. The processor may also receive approval or rejection from the users. The processor then communicates the initiatives for funding when the initiatives are validated (i.e., approved).
Rao: Sec. 0599, The processor also scales the business propositions and the initiatives based on the feedback from the users. The processor also validates the business propositions and the initiatives once approved by the users. );
determining, by the fourth Al agent, a modified business plan based on the business plan and the feedback (
Rao: Sec. 0599, The processor also scales the business propositions and the initiatives based on the feedback from the users. The processor also validates the business propositions and the initiatives once approved by the users. );
and executing, by the third Al agent, the modified business plan (
Rao: Sec. 0599, The processor monitors the program execution and communicates the feedback information. The processor updates the business proposition and the initiatives based on the feedback information at the program execution phase.).
Claim 22 recite limitations that stand rejected via the art citations and rationale applied to claim 4.
Referring to Claim 23, Rao teaches the method of claim 13, further comprising:
Rao does not explicitly teach receiving, at a fifth Al agent, a label image associated with merchandise sent or received by the company, wherein the label comprises merchandise details; and determining, by the fifth Al agent, the merchandise details.
However, Siebel teaches these limitations
receiving, at a fifth Al agent, a label image associated with merchandise sent or received by the company, wherein the label comprises merchandise details (
Siebel: Sec. 0205, To train the PSF machine learning model 312, each of multiple training labels may represent the amount of revenue or bookings to be realized between a specific day and the end of a timeframe (such as the end of a fiscal quarter). The training data may include features describing the pipeline, other CRM-derived features, external features, and the labels.
Siebel: Sec. 0234, The information 702 is used to generate labels 708, and the information 704 and 706 is used to generate features 710. The labels 708 and some of the features 710 (historical features) are used during a training and validation phase 712, where that information is used to train an aggregate-level machine learning model 404. The following table represents one example of the labels 708 and features 710 that may be used in particular embodiments of the aggregate-level machine learning model 404, although this list is for illustration only and does not limit the scope of this disclosure to this particular collection of labels and features.
Siebel: Table 2,);
and determining, by the fifth Al agent, the merchandise details (
Siebel: Sec. 0235, The labels 708 and features 710 are used here to train the aggregate-level machine learning model 404, which may represent a regressor model. The model 404 can be trained here to predict the total bookings of a fiscal period directly. Once trained, the aggregate-level machine learning model 404 is used during an inference phase, where features 710 related to current data are provided to the model 404.
Siebel: Sec. 0319, historical records identifying various characteristics of customers specifically or a market more generally (such as macroeconomic conditions in a region, a country, or the world) may be labeled with actual sales. Suitable training of the pricing model 1410 may then occur to train the pricing model 1410 to predict acceptable prices for customers).
Rao and Siebel are both directed to the analysis of the use of artificial intelligence (See Rao at 0236, 0223-0227; Siebel at 0090, 0112, 0202). Rao discloses that additional elements, such as an artificial intelligence engine can be considered (See Rao at 0035). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao, which teaches detecting and repairing information technology problems in view of Siebel, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Siebel at 0349, 0362, 0363).
Referring to Claim 24, Rao teaches the method of claim 23, Rao does not explicitly teach wherein the input describing the company comprises the merchandise details.
However, Siebel teaches wherein the input describing the company comprises the merchandise details (
Siebel: Sec. 0260,
TABLE 3
Customer
Industry
Information about a customer's business
SKU
information for all customers
Shopping basket
information (items sold
together)
Discount status
Timestamp
Representative
Sequence of events
Terms of sale (duration of
contract)
(For B2C) online cart
activity information, such
as how long the item was
in the cart, etc.
(For B2C) Payment
methods used
Siebel: Sec. 0441, data sources 3702-3704 can provide data used by a number of AI-based CRM functions 3706. Note that while many of these functions are described in detail above, some of these AI-based CRM functions 3706 may provide additional functionality while being implemented in the same or similar ways as discussed above…Customer fulfillment functionality can be used to track customer fulfillment from sale to installation to activation in order to ensure efficient delivery of products and services and use machine learning to optimize operations and detect delays in deliveries and activations).
Rao and Siebel are both directed to the analysis of the use of artificial intelligence (See Rao at 0236, 0223-0227; Siebel at 0090, 0112, 0202). Rao discloses that additional elements, such as an artificial intelligence engine can be considered (See Rao at 0035). It would have been obvious for one having ordinary skill in the art before the effective filing date of the claimed invention to have modified Rao, which teaches detecting and repairing information technology problems in view of Siebel, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Siebel at 0349, 0362, 0363).
Claims 25-29 recite limitations that stand rejected via the art citations and rationale applied to claims 13, 14 , 15, 18, 21. Regarding, the system for executing a business plan for a company, the system comprising: a memory: at least one processor (
Rao: Sec. 0598, structure and classification of components of the business planning method and workflow, according to one or more embodiments. The system comprises a business plan creation phase and program execution phase. The system comprises a processor and a memory. The processor receives information from the users and/or the agents. The processor creates the business plan based on the information. The information may comprise contents related to vision, concept, strategy, and plan. The processor creates the business plan based on the information at the program execution phase):
Claim 30 recites limitations that stand rejected via the art citations and rationale applied to claim 13. Regarding, one or more non-transitory computer readable media storing computer-executable instructions thereon that, when executed by at least one computer, cause the at least one computer to perform a method (
Rao: Sec. 0151, structure and classification of components of the business planning method and workflow, according to one or more embodiments. The system comprises a business plan creation phase and program execution phase. The system comprises a processor and a memory. The processor receives information from the users and/or the agents. The processor creates the business plan based on the information. The information may comprise contents related to vision, concept, strategy, and plan. The processor creates the business plan based on the information at the program execution phaseEmbodiments within the scope of the present invention may also include physical and other computer readable media for carrying or storing computer-executable instructions and/or data structures. Such computer readable media can be any media accessible by a general purpose or special purpose computer system. Computer readable media that store computer-executable instructions are physical storage media. Computer readable media that carry computer-executable instructions are transmission media. Thus, by way of example and not limitation, embodiments of the invention can comprise at least two distinct kinds of computer readable media: physical computer readable storage media and transmission computer readable media.)
Siebel teaches the analyzing of cost with AI tools, wherein the AI tools are used to manage, forecast, and streamline cost, in which the Examiner is interpreting as comparing and saving predict cost
Referring to Claim 31, Rao teaches the method of claim 1, further comprising further comprising comparing predicted cost savings associated with implementing the recommendation for incorporating Al into the company to actual measured cost savings resulting from implementing the recommendation, and retraining the second Al agent based on the comparison (
Siebel: Sec. 0427, Sustainability management can be used to leverage AI-driven insights from integrated data across an energy value chain in order to quantify a company's carbon footprint across all operations. Compliance and regulatory management can be used to leverage integrated data and automated workflows to support regulatory compliance with AI-driven insights to catch errors, identify red flags, and remind users of next steps to minimize non-compliance costs. Billing and cost management can be used to streamline billing and cost control processes with AI-driven insights to forecast project costs and customer bills, identify anomalous patterns, and proactively mitigate billing errors and project cost overruns. Aftermarket insights and fuel station management can be used to monitor fuel station portfolio performances and predict consumer demand growth in order to determine expansion areas, station designs (such as car washes, groceries, restaurants) and franchise incentives to maximize profitable station footprints.).
Siebel teaches the analyzing of cost with AI tools, wherein the AI tools are used to manage, forecast, and streamline cost, in which the Examiner is interpreting as comparing and saving predict cost.
Response to Arguments
Applicant’s arguments filed 05/26/2026 have been fully considered but they are not persuasive. Applicant’s arguments will be addressed hereinbelow in the order in which they appear in the response filed 05/26/2026.
Regarding the 35 U.S.C. 101 rejection, at pg. 11-17 Applicant argues with respect to claims at issue are not directed to an abstract idea
In response to the 35 USC § 101 claim rejection argument, the Examiner respectfully disagrees. The Examiner did consider each claim and every limitation both individually and as a whole, since the grounds of rejection clearly indicates that an abstract idea has been identified from elements recited in the claims. Using the two-part analysis, the Office has determined there are no elements, in the claim sufficient enough to ensure that the claims amounts to significantly more than the abstract idea itself. As recited, the claims are directed towards:
A method for generating a business plan for a company, the method comprising:
receiving a business name associated with the company;
obtaining, by a first artificial intelligence (Al) agent, a business description for the company based on the business name, the obtaining comprising:
obtaining at least one of a competitor, a supplier or a customer associated with the company;
identifying a website associated with the at least one of the company, competitor, the supplier or the customer; and
extracting details from the website of the at least one of the company, competitor, the supplier or the customer; and
generating, by a second Al agent, a business plan for the company based on the business description, wherein the business plan for the company comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation.
The claim(s) does not include additional elements that are sufficient to amount to significantly more than the judicial exception because the computer as recited is a generic computer component that performs functions.
Examiner finds the claim recite concepts which are now described in the 2019 PEG as certain methods of organizing human activity. In particular the claims recites limitations for directed to managing business information, which constitutes methods related to commercial interactions such as business relations which are still considered an abstract idea under the 2019 PEG. The AI Agent is comprised of generic computer elements to perform an existing business process. Examiner finds the claims recite mere instructions to implement the abstract idea on a computer and uses the computer as a tool to perform the abstract idea without reciting any improvements to a technology, technological process or computer-related technology.
Regarding, the steps at pg. 14-16 that Applicant points to as practical application are merely narrowing the abstract idea to a particular technological environment, which has been found to be ineffective to render an abstract idea eligible. Furthermore, the Examiner respectfully disagrees because the steps and arguments at pg. 14:
“Applicant submits that under Step 2A, Prong Two, the claims integrate any alleged abstract idea into a practical application. The Specification explains that the business plan may identify concrete steps for achieving recommendations and may include steps automated in whole or in part by Al agents or another Al or software tool. See Specification, ¶[0142]. The Specification further explains that the business plan may recommend incorporating Al or analytics into the company to replace inefficiencies with Al-assisted automation and may predict cost savings from following that recommendation. See Specification, [0167]. The Specification further describes the execution of the business plan by an Al agent in some embodiments and provides examples of implementation through company internal systems, communication systems, social media, and other environments. See Specification, ¶¶[0172], [0218], [0220]. At least these limitations meaningfully constrain the claims to a particular Al-agent-based implementation”
seems to describe a “particular way” of directed to managing business information are part of the abstract idea. “ The Applicant is basically relying on the AI as integrating the abstract idea into a practical application but those AI aren't really utilized in any particular manner, and the specification indicates that at 0259 " AI/machine learning (ML) predictor, which may receive and/or generate data, models and software applications. AI/ML predictor may also store its inputs and outputs in a memory of analytics engine 104′. As used herein, the term artificial intelligence (AI) also includes machine learning", which indicates the lack of particularity in the application to the technological environment. Furthermore, at 0349 the Applicant recites that “ it should be understood that the disclosed system and method may be implemented in other manners. For example, the described system embodiment is merely an example. For example, the unit division is merely logical function division and may be other division in actual implementation. For example, a plurality of units or components may be combined or integrated into another system, or some features may be ignored or not performed. In addition, the displayed or discussed mutual couplings or direct couplings or communication connections may be implemented by using some interfaces. The indirect couplings or communication connections between the apparatuses or units may be implemented in electronic, mechanical, or other forms” These citations are a strong indicator that the technical application is NOT particular, and furthermore the claim invention does not “improves the functioning of a computer or improves another technology or technical field.” or “an improvement to another technology or technical field.
Regarding, at pg. 15, “Moreover, the Specification at ¶¶[0348]-[0350] identifies several technical problems related to existing business-planning systems, namely that these systems may not adequately and automatically obtain broad company-relevant information, generate/refine a company-specific business plan from limited input, and identify non-obvious opportunities for incorporating Al and automation into the company.”
In response, the Examiner respectfully disagrees. When determining whether a computer-implemented functional claim would have been obvious, examiners should note that broadly claiming an automated means to replace a manual function to accomplish the same result does not distinguish over the prior art. See Leapfrog Enters., Inc. v. Fisher-Price, Inc., 485 F.3d 1157, 1161, 82 USPQ2d 1687, 1691 (Fed. Cir. 2007) (“Accommodating a prior art mechanical device that accomplishes [a desired] goal to modern electronics would have been reasonably obvious to one of ordinary skill in designing children’s learning devices. Applying modern electronics to older mechanical devices has been commonplace in recent years.”); In re Venner, 262 F.2d 91, 95, 120 USPQ 193, 194 (CCPA 1958); see also MPEP § 2144.04. Furthermore, implementing a known function on a computer has been deemed obvious to one of ordinary skill in the art if the automation of the known function on a general purpose computer is nothing more than the predictable use of prior art elements according to their established functions. KSR Int’l Co. v. Teleflex Inc., 550 U.S. 398, 417, 82 USPQ2d 1385, 1396 (2007); see also MPEP § 2143, Exemplary Rationales D and F. Likewise, it has been found to be obvious to adapt an existing process to incorporate Internet and Web browser technologies for communicating and displaying information because these technologies had become commonplace for those functions. Muniauction, Inc. v. Thomson Corp., 532 F.3d 1318, 1326-27, 87 USPQ2d 1350, 1357 (Fed. Cir. 2008).
Then at pg. 15 the Applicant admission that the application is directed to improving the user’s experience and not the computer itself
(at pg.15 “Instead, the Specification expressly describes that the disclosed systems "may also improve business planning" by allowing a user to provide limited business information, such as a business name, and automatically receive a business overview and/or business plan. See Specification, ¶[0348]. The Specification further explains that Al agents may retrieve additional information about the business from an environment and generate the business overview and/or business plan with no input or some input from the user, and that feedback may be used to refine the business overview and/or business plan.”)
Then at pg. 15, regarding the following argument:
(at pg.15 “Similarly, this technical improvement is recited in the amended independent claims. For example, amended independent claim 1 recites "generating, by a second Al agent, a business plan for the company based on the business description, wherein the business plan comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation.”
at pg. 16 “The Specification additionally identifies concrete improvements over existing business-planning approaches. It explains that, in addition to streamlining business plan generation, the disclosed systems and methods may provide business planners and executives with a wider variety of information compared to existing methods for generating a business plan, including automatically accessed information relating to competitors, suppliers, the economy, and other factors that may not be as easily obtained without Al assistance. See Specification, [0349]. The Specification further explains that the disclosed systems and methods may identify improvements to the business achievable by adopting Al and automation at various stages in the business, and that such improvements may lead to reduced expenses, increased profitability, improved logistics, improved customer satisfaction and engagement, and other tangible business outcomes.”)
The Examiner wants to point out that claims and arguments do not show or break down how this is actually executed, at this point it’s just an aspirational statement. Moreover, the current claims are just using typically known actions/steps of AI and no improvements.
Additionally, the Examiner would like to point the Applicant to the 2019 PEG, in which directed to managing business information will fall under. The 2019 PEG which states:
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).
Adding insignificant extra-solution activity to the judicial exception - see MPEP 2106.05(g)
Generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h)
Regarding the 35 U.S.C. 102 rejection, at pg. 17 and 18, Applicant argues “As amended, independent claims 1, 8, and 12 each require that the generated business plan for the company comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation. Rao fails to disclose or suggest at least these limitations recited in the independent claims.”; “This paragraph concerns the analytics capabilities of Rao's disclosed system; however, Rao at this paragraph does not disclose a generating a business plan for a company that includes a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation. Similarly, Rao at paragraph [0581] merely concerns identifying and recommending business propositions, Rao at [0585] merely concerns recommending initiatives based on priority, and Rao at [0601] merely concerns recommending a short list of initiatives to be included in a business plan. None of these cited passages of Rao disclose recommending that Al be incorporated into the company, as now disclosed in the amended independent claims. ”
In response, the Examiner respectfully disagrees. Rao teaches recommendation for business handling and propositions working with AI. Then, combine with the teaching of Siebel that discloses the analyzing of cost with AI tools, wherein the AI tools are used to manage, forecast, and streamline cost, in which the Examiner is interpreting as comparing and saving predict cost. Thus, Rao in view of Siebel does teach these limitations.
At pg. 18, Applicant argues “Rao fails to disclose or suggest "generating, by a second Al agent, a business plan for the company based on the business description, wherein the business description includes a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation,"”
In response, the Examiner respectfully disagrees. Rao teaches recommendation for business handling and propositions working with AI. Then, combine with the teaching of Siebel that discloses the analyzing of cost with AI tools, wherein the AI tools are used to manage, forecast, and streamline cost, in which the Examiner is interpreting as comparing and saving predict cost. Additionally, but Rao and Siebel teaches multiple AI elements working to produce data outcome, therefore teaching a second AI agent. Thus, Rao in view of Siebel does teach these limitations.
At pg. 19, Applicant argues “As amended, independent claims 13, 25, and 30 each require determining, by a second Al agent, the business plan for the company based on the business overview, wherein the business plan comprises a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation, and executing, by a third Al agent, the business plan. Rao does not teach or suggest these limitations for at least the reasons discussed above.”
In response, the Examiner respectfully disagrees. Rao teaches recommendation for business handling and propositions working with AI. Then, combine with the teaching of Siebel that discloses the analyzing of cost with AI tools, wherein the AI tools are used to manage, forecast, and streamline cost, in which the Examiner is interpreting as comparing and saving predict cost. Additionally, but Rao and Siebel teaches multiple AI elements working to produce data outcome, therefore teaching a second and third AI agent. Thus, Rao in view of Siebel does teach these limitations.
At pg. 20, Applicant argues “Applicant notes that Siebel does not actually teach or suggest the amended limitation requiring the business plan for the company to comprise a recommendation for incorporating Al into the company and a prediction of cost savings from following the recommendation.”
In response, the Examiner respectfully disagrees. Rao teaches recommendation for business handling and propositions working with AI. Then, combine with the teaching of Siebel that discloses the analyzing of cost with AI tools, wherein the AI tools are used to manage, forecast, and streamline cost, in which the Examiner is interpreting as comparing and saving predict cost. Thus, Rao in view of Siebel does teach these limitations.
At pg. 21, Applicant argues “Applicant notes that Siebel at the paragraphs [0357], [0426] and [0440] cited by the Office Action does not actually disclose "extracting details from the website of the at least one of the competitor, the supplier or the customer," as recited in the amended independent claims.”
In response, the Examiner respectfully disagrees. Siebel teaches the analyzing and extraction of data from website and various sources that are to be plugged in a machine learning. Thus, Siebel teaches this limitation.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Cella et al., U.S. Pub. 20190146474, (discussing the operation and managing of artificial intelligence).
Cella et al., W.O. Pub. 2022133330, (discussing the operation and managing of artificial intelligence).
Panimalar et al., A Review Of Churn Prediction Models Using Different Machine Learning And Deep Learning Approaches In Cloud Environment, https://ph04.tci-thaijo.org/index.php/JCST/article/download/211/12, Journal of Current Science and Technology, 2023 (discussing the use of machine learning in different environments).
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to UCHE BYRD whose telephone number is (571)272-3113. The examiner can normally be reached Mon.-Fri..
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Patricia Munson can be reached at (571) 270-5396. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/UCHE BYRD/Examiner, Art Unit 3624
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