DETAILED ACTION
Status of the Application
Claims 1-26 have been examined in this application. This communication is the first action on the merits. The submission is in compliance with the provisions of 37 CFR 1.97.
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 Non-Final Action on the merits in response to the application filed on 03/07/2024.
Claims 1-26 remain pending in this application.
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 1-13 are directed towards a method, and claims 14-26 are directed towards an apparatus, all of which are among the statutory categories of invention.
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 activating plans using 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 1-26, the independent claims (claims 1 and 14) are directed to generating of plans, In independent claim 1, the bolded limitations emphasized below correspond to the abstract ideas of the claimed invention:
a method comprising:
(i) receive a natural language telco plan request; and
(ii) transmit to the Al NLP telco plan generation system,
wherein the natural language telco plan data includes data describing a telco plan desired by a user of the telco planner client computer system; and
activating the Al NLP telco plan generation system to perform operations comprising:
a. determining and organizing metadata from the natural language telco plan data into a format that determines an intent of the machine-perceived user-intended telco plan, wherein the machine perceived user intended telco plan includes first telco plan features;
b. matching features of existing telco plans to the first telco plan features to identify competitive telco plans; and
c. generating a proposed telco plan consistent with the machine perceived user intended telco plan having features and feature values that exceed the competitive telco plan features and feature values within defined constraints .
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; managing personal behavior such as following rules or instructions (See MPEP 2106.04(a)(2), subsection II).
If a claim limitation, under its broadest reasonable interpretation, covers commercial interaction; managing personal behavior 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 artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning. The claims recite the steps are performed by the artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning.
The limitations of
providing a user interface to integrate communication between a telco planner client computer system and an artificial intelligence (AI) natural language processor (NLP) and telco plan generation system (Al NLP telco plan generation system) to:
wherein the Al NLP telco plan generation system includes an artificial intelligence system having a natural language processing engine that includes a language model and machine learning algorithms;
receiving, with the Al NLP telco plan generation system via the user interface, the natural language telco plan request framework data from the telco planner client computer system, wherein the natural language telco plan data includes data describing a telco plan desired by a user of the telco planner client computer system.
are mere data receiving and analyzing 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 artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning. The artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning are 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 artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning. 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 receiving and analyzing. Then, the artificial intelligence techniques recited in the claim are disclosed at a high-level of generality (see at least Specification [0042 “ utilizing artificial intelligence to identify features, feature subtypes, allowances, and priority such as the target segment and other features and feature subtypes of the telco plan 116 based on information obtained from one or more of the data sources 116-120. ”]) 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
providing a user interface to integrate communication between a telco planner client computer system and an artificial intelligence (AI) natural language processor (NLP) and telco plan generation system (Al NLP telco plan generation system) to:
wherein the Al NLP telco plan generation system includes an artificial intelligence system having a natural language processing engine that includes a language model and machine learning algorithms;
receiving, with the Al NLP telco plan generation system via the user interface, the natural language telco plan request framework data from the telco planner client computer system, wherein the natural language telco plan data includes data describing a telco plan desired by a user of the telco planner client computer system.
are recited at a high level of generality. These elements amount to transmitting 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 artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning 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- 13 and 15-26 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 2, 15 recite a processor and user interface to present suggestions; claim 5, 18 recite a processor to determine user’s intent; claims 8, 21 recite a processor to generate marketing data; claims 12, 13, 25, 26 recite a processor to generate a plan. The dependent claims 2- 13 and 15-26 recite limitations that are not technological in nature and merely limits the abstract idea to a particular environment. Claims 2- 13 and 15-26 recites artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning which are considered an insignificant extra-solution activities of receiving and analyzing data; see MPEP 2106.05(g). Claims 2- 13 and 15-26 recites artificial Intelligence, user interface, natural language processor, telco plan generation system, processing engine, model, machine learning, which merely recites an instruction to apply the abstract idea using a generic computer component; MPEP 2106.05(f). Additionally, claims 2- 13 and 15-26 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 and 14. Therefore claims 2- 13 and 15-26 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 1-7, 11-20, and 24-26 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20250104106, Manova, et al., to hereinafter Manova in view of United States Patent Number US 9338308, Adams, et al.
Referring to Claim 1, Manova teaches a method comprising:
providing a user interface to integrate communication between a telco planner client computer system and an artificial intelligence (AI) natural language processor (NLP) and telco plan generation system (Al NLP telco plan generation system) to:
(i) receive a natural language telco plan (See Adams) request (
Manova: Sec. 0031, generating the content for the action plan comprises applying one or more natural language processing (NLP) models on the user data repository to produce an output with personalized marketing content. According to some embodiments, the output is selected from a text document, a blog, an email, an ad, a social media post, investor deck, one pager, company profile, business plan, marketing plan, competitive analysis, swot analysis, financial plan and any combination thereof. Each possibility and combination of possibilities is a separate embodiment.
Manova: Sec. 0184, According to some embodiments, and similarly to step 161 of method 150 of FIG. 1B, the method 200 may include applying one or more natural language processing (NLP) models on data in the user data repository to produce a document with personalized marketing content. According to some embodiments, the one or more NLP models may include one or more artificial intelligence AI algorithms.);
and (ii) transmit to the Al NLP telco plan generation (See Adams) system, wherein the Al NLP telco plan generation (See Adams) system includes an artificial intelligence system having a natural language processing engine that includes a language model and machine learning algorithms (
Manova: Sec. 0184, According to some embodiments, and similarly to step 161 of method 150 of FIG. 1B, the method 200 may include applying one or more natural language processing (NLP) models on data in the user data repository to produce a document with personalized marketing content. According to some embodiments, the one or more NLP models may include one or more artificial intelligence AI algorithms.
Manova: Sec. 0147, According to some embodiments, and similarly to step 161 of method 150 of FIG. 1B, the method 200 may include applying one or more natural language processing (NLP) models on data in the user data repository to produce a document with personalized marketing content. According to some embodiments, the one or more NLP models may include one or more artificial intelligence AI algorithms.
the method may include applying one or more natural language processing (NLP) models on data in the user data repository to produce a document with personalized marketing content, such as depicted by step 161 of FIG. 1B. According to some embodiments, the one or more NLP models may include one or more artificial intelligence AI algorithms. According to some embodiments, the one or more NLP models may include one or more autoregressive language models);
receiving, with the Al NLP telco plan generation (See Adams) system via the user interface, the natural language telco plan (See Adams) request framework data from the telco planner (See Adams) client computer system, wherein the natural language telco plan (See Adams) data includes data describing a telco plan desired by a user of the telco planner client computer system (
Manova: Sec. 0008, to calculate an action plan, marketing strategy and marketing content with the highest probability of reaching said goals (predictive and/or prescriptive analytics). to 1) output an action plan including channels, frequency, priority, type etc. and 2) to generate the actual content, per each such channel, with the highest probability to yield the desired results, based upon which a full content editorial calendar encompassing the different channels is created
Manova: Sec. 0147, applying one or more natural language processing (NLP) models on data in the user data repository to produce a document with personalized marketing content, such as depicted by step 161 of FIG. 1B. According to some embodiments, the one or more NLP models may include one or more artificial intelligence AI algorithms. According to some embodiments, the one or more NLP models may include one or more autoregressive language models);
and activating the Al NLP telco plan generation (See Adams) system to perform operations comprising:
a. determining and organizing metadata from the natural language telco plan (See Adams) data into a format that determines an intent of the machine-perceived user-intended telco plan, wherein the machine perceived user intended telco plan includes first telco plan (See Adams) features (
Manova: Sec. 0226, According to some embodiments, the questionnaires responses may be analyzed using one or more NLP models configured to extract key features and characteristics therefrom.
Manova: Sec. 0045, According to some embodiments there is provided herein a computer implemented method for generation of products and outputs (document, blog, email, ad, social media post and the like), with personalized marketing content, and/or strategy, and/or action plan, the method including: receiving an identifying characteristic of a company, automatically extracting, using a machine learning model, data from the internet and/or at least one application program interface (API), based on an identifying characteristic of the company, generating a company data repository including company specific characteristics from the extracted data, applying predictive models on the company data repository to establish actionable insights and/or recommendations, providing a user with one or more products and/or outputs (document, blog, email, add, social media post and the like) including the actionable insights and/or recommendations.
Manova: Sec. 0046, According to some embodiments there is provided herein a computer implemented method for generation of products and outputs (document, blog, email, ad, social media post and the like) with personalized marketing content, and/or strategy, and/or action plan, the method including: receiving user inputted answers to a questionnaire, applying the inputted answers into at least one machine learning algorithm configured to generate one or more insights, strategies and/or action plans based, at least in part, on the user inputted answers, providing the user with one or more products and/or outputs including the actionable insights and/or recommendations.
Manova: Sec. 0165, the documents may be generated in a variety of formats, including, but not limited to, any one or more of: web-based document format, viewable documents, downloadable documents, printable documents or any combination thereof. Each possibility is a separate embodiment. According to some embodiments, the document may include, but is not limited to, any one or more of a prescription for marketing strategy, a marketing document, a marketing article and/or a marketing presentation. According to some embodiments, the one or more documents may include, but are not limited to, an investor deck, one pager, company profile, business plan, marketing plan, competitive analysis, swot analysis, financial plan (or a budget plan),
Manova describes formats for determining and generating features and products that the user desires to be part of their plan.);
b. matching features of existing telco plans (See Adams) to the first telco plan (See Adams) features to identify competitive telco plans (
Manova: Sec. 0127, one or more names of companies that are competitors of the company, one or more companies providing similar products and/or services, the vision of the company, the challenges the company may be facing, and the like. Each possibility is a separate embodiment.);
and c. generating a proposed telco plan (See Adams) consistent with the machine perceived user intended telco plan (See Adams) having features and feature values that exceed the competitive telco plan (See Adams) features and feature values within defined constraints (
Manova: Sec. 0118, According to some embodiments, the influencer marketing plan may include recommendations and/or prioritization of at least one media channel most relevant for the company of the user.
Manova: Sec. 0121, According to some embodiments, the extracted data, and/or the analyzed extracted data may include data associated with the target market of the company and/or activities of the competitors of the company. Each possibility is a separate embodiment.).
Manova describes generating plans that is analyzing competitors features and prioritizing most relevant to the user; which the Examiner is interpreting as values exceeding
Manova does not explicitly teach telco plan, telco plan generation, telco planner.
However, Adams teaches telco plan, telco plan generation, telco planner (
Adams: Col. 3 Ln. 28-45, a method for generating personalized telecommunications package recommendations comprising: building, by a matrix builder application on a server, a lifestyle attribute/component matrix; receiving, by an application on a server, a plurality of lifestyle attribute selections; and calculating, by the application, a total user score based on at least the selected lifestyle attributes.)
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Referring to Claim 2, Manova teaches the method of claim 1 wherein providing an integrated AI NLP telco plan generation system interface between the telco planner client computer system and an AI NLP telco plan generation system to receive a natural language telco plan request comprises:
Manova does not explicitly teach providing the integrated AI NLP telco plan generation system interface to present a user interface that includes telco plan suggestions.
However, Adams teaches providing the integrated AI NLP telco plan generation system interface to present a user interface that includes telco plan suggestions (
Adams: Col. 13 Ln. 19-37, A plurality of package suggestions are generated at block 328 based upon the total scores calculated at block 324 for each component.
Adams: Col. 2 Ln. 38-57, a method for generating personalized telecommunications package recommendations comprising: building, by a matrix builder application on a server, a lifestyle attribute/component matrix;… displaying, by the application, on a graphical user interface, the total user score for a plurality of packages, wherein each package of the plurality of packages comprises at least some of the package components. ).
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Referring to Claim 3, Manova teaches the method of claim 1 wherein determining the machine-perceived user intended telco plan comprises:
natural language (
Manova: Sec. 0226, the questionnaires responses may be analyzed using one or more NLP models configured to extract key features and characteristics therefrom. )
Manova describe analyzing with NLP to determine features.
Manova does not explicitly teach parsing the organized metadata from the natural language telco plan data, from the parsing, identifying the features and feature subtypes in the natural language (See Manova) telco plan data, and correlating the identified features and feature subtypes with user intent to determine the machine-perceived user intended telco plan.
However, Adams teaches these limitations
parsing the organized metadata from the natural language (See Manova) telco plan data (
Adams: Col. 3 Ln. 28-45, The personalized recommendation engine, however, may be able to analyze disparate behavior or lifestyle attributes revealed by the customer's answers to the survey questions to infer device features and/or communication services that would be desired by the customer.);
from the parsing, identifying the features and feature subtypes in the natural language (See Manova) telco plan data(
Adams: Col. 3 Ln. 28-45, the personalized recommendation engine takes into account this requested blocking/restriction/access feature and may recommend packages that satisfy this feature request. These personalized package recommendations may include devices that may not come with that type of blocking/restriction/access feature but that, in combination with subscription plans, applications, accessories, and/or services may satisfy this customer request.);
Adams describe analyzing packages to determine features.
and correlating the identified features and feature subtypes with user intent to determine the machine-perceived user intended telco plan (
Adams: Col. 6 Ln. 16-35, The personalized recommendation engine, however, may be able to analyze disparate behavior or lifestyle attributes revealed by the customer's answers to the survey questions to infer device features and/or communication services that would be desired by the customer.).
Referring to Claim 4, Manova teaches the method of claim 1 wherein the format is a JavaScript Object Notation (JSON) format (
Manova: Sec. 0256, Computer readable program instructions for carrying out operations of the present invention may be assembler instructions, instruction-set-architecture (ISA) instructions, machine instructions, machine dependent instructions, microcode, firmware instructions, state-setting data, or either source code or object code written in any combination of one or more programming languages, including an object oriented programming language such as Java, Smalltalk, C++ or the like, and conventional procedural programming languages, such as the “C” programming language or similar programming languages).
Manova describes JavaScript as an inventive element, wherein JSON is a routine/conventional programming language format.
Referring to Claim 5, Manova teaches the method of claim 1 further comprising:
Manova does not explicitly teach determining if the user telco request includes sufficient information to determine the user intent for the desired customer telco plan; and if the user telco request is sufficient to determine the user intent for the desired customer telco plan, providing feedback to the user to provide additional information needed by the AI NLP telco plan generation system to determine the user intent.
However, Adams teaches determining if the user telco request includes sufficient information to determine the user intent for the desired customer telco plan; and if the user telco request is sufficient to determine the user intent for the desired customer telco plan, providing feedback to the user to provide additional information needed by the AI NLP telco plan generation system to determine the user intent (
Adams: Col. 7 Ln. 42-62, The importance weighting may be multiplied by the corresponding value from a matrix for the user preference or the lifestyle attribute for a pool of components. The corresponding values for each component of a pool of components are determined, summed, normalized, and weighted, as illustrated below. An importance weighting may range from 0.0-2.0 or any other numerical range depending upon the statistical discretion desired for the results.
Adams: Col. 11 Ln. 44-55, the multiplier that reflects the selected importance weighting may be 0.0, for a selected user preference 210 or lifestyle attribute 208 that is not important to the user, 1.33 for selected user preferences 210 or selected lifestyle attributes 208 that are slightly important to the user, 1.67 for selected user preferences 210 or selected lifestyle attributes 208 that are slightly more important to the user, and 2.00 selected user preferences 210 or selected lifestyle attributes 208 that are considered very important to the user. It is appreciated that other multipliers and/or indicators may be used depending upon the desired statistical discretion.);
Adams describe receiving a user information to determine packages that the user would desire.
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Referring to Claim 6, Manova teaches the method of claim 1, Manova does not explicitly teach
wherein generating the proposed telco plan consistent with the machine perceived user intended telco plan having features and feature values that exceed the competitive telco plan features and feature values within user-defined constraints comprises:
based on the organized metadata, initializing a telco plan with an empty list of features; iterating over all the additional telco plans; And for each feature subtype, creating a list of all unique features sorted in ascending order of value from a first to last element in accordance with a predetermined measure of value; creating a max feature telco plan with features of the telco plan having maximum allowance values, which is the last element for each feature subtype; and revising the allowances values to meet predetermined telco plan constraints.
However, Adams teaches these limitations
wherein generating the proposed telco plan consistent with the machine perceived user intended telco plan having features and feature values that exceed the competitive telco plan features and feature values within user-defined constraints comprises:
based on the organized metadata, initializing a telco plan with an empty list of features (
Adams: Col. 5 Ln. 13-25, the personalized recommendation engine starts with a pool of components in categories such as portable electronic devices, accessories, subscription plans, services, and applications. In response to input from a user or a customer care representative, the personalized recommendation engine ranks each component in the pool of components and recommends packages of components based on the rankings of each component.);
iterating over all the additional telco plans (
Adams: Col. 13 Ln. 35-45, At block 328, a plurality of package options are generated and displayed, for example, on the graphical user interface 218. Each package option of the plurality of package options comprises a plurality of package components and an associated total score which may be the sum of the scores calculated at block 326 for each component, the plurality of package options may be displayed and/or ranked in descending order by package score. );
And for each feature subtype, creating a list of all unique features sorted in ascending order of value from a first to last element in accordance with a predetermined measure of value (
Adams: Col. 1 Ln. 60 – 2 Ln. 5, the plurality of personalized telecommunications packages in a ranked order based upon the sum of the total component scores for each component in each personalized telecommunications package of the plurality of personalized telecommunications packages; and receiving, by the application, a request for purchase of at least one package of the plurality of packages.);
Adams describes the ranking of components, in which the Examiner is interpreting as sorting features.
creating a max feature telco plan with features of the telco plan having maximum allowance values, which is the last element for each feature subtype; and revising the allowances values to meet predetermined telco plan constraints (
Adams: Col. 13 Ln. 45-60, The highest-rated component from each category such as devices, accessories, subscription plans, services, and applications may be selected and packaged together, then the second-highest rated component from each category, and so on. In an alternate embodiment, components with varying ratings may be packaged together to meet not only the use habits identified by the user but also to meet unanticipated needs of the user. Some packages may comprise at least one component from each of the devices, applications, subscription plans, accessories, and services, and some packages may only comprise two total components.).
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Referring to Claim 7, Manova teaches the method of claim 6 Manova does not explicitly teach
wherein revising the allowances values for each feature to meet predetermined telco plan constraints comprises: prioritizing features; and adjusting allowance values of features in order of ascending feature priority from low priority to highest priority;
However, Adams teaches these limitations
wherein revising the allowances values for each feature to meet predetermined telco plan constraints comprises:
prioritizing features (
Adams: Col. 13 Ln. 45-60, The highest-rated component from each category such as devices, accessories, subscription plans, services, and applications may be selected and packaged together, then the second-highest rated component from each category, and so on. In an alternate embodiment, components with varying ratings may be packaged together to meet not only the use habits identified by the user but also to meet unanticipated needs of the user. Some packages may comprise at least one component from each of the devices, applications, subscription plans, accessories, and services, and some packages may only comprise two total components.);
Adams describe ranking features based on high values.
and adjusting allowance values of features in order of ascending feature priority from low priority to highest priority (
Adams: Col. 1 Ln. 60 - Col. 2 Ln. 15, the plurality of personalized telecommunications packages in a ranked order based upon the sum of the total component scores for each component in each personalized telecommunications package of the plurality of personalized telecommunications packages;
Adams: Col. 5 Ln. 1-13, the systems and methods described herein relate to the use of a personalized recommendation engine to recommend packages of components to a user and ranks the entire pool of components instead of removing components from the pool. The personalized recommendation engine takes into account a plurality of lifestyle factors and ranks the entire pool of components based on at least user input instead of removing components from the pool and creates personalized packages from the pool of ranked components to recommend to users.
Adams: Col. 5 Ln. 13-25, In response to input from a user or a customer care representative, the personalized recommendation engine ranks each component in the pool of components and recommends packages of components based on the rankings of each component.).
Adams describe ranking features which can be adjusted.
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20). Additionally, an ordering/ranking of features from high to low have been identified in Adams and therefore predictable potential solutions of ordering/ranking low to high, to the recognized need or problem would be obvious to try with a reasonable expectation of success.
Referring to Claim 11, Manova teaches the method of claim 1 further comprising:
providing prompts to a user to solicit and collect information to generate the telco plan (See Adams) and the marketing information (
Manova: Sec. 0067, the method, further includes utilizing a virtual marketing assistant to prompt the user to input answers in response to the questionnaire, and wherein producing the marketing content, and/or strategy, and/or action plan includes utilizing the virtual marketing assistant to display the produced outputs.
Manova: Sec. 0097, the method may include implementing a virtual marketing assistant. According to some embodiments, the virtual marketing assistant may be configured to prompt the user. According to some embodiments, the virtual marketing assistant may be configured to communicate with the user. According to some embodiments, the method may include displaying and/or outputting data to the user using the virtual marketing assistant. According to some embodiments, the method may include receiving data from the user by having the virtual marketing assistant collect the data from the user. According to some embodiments, the method may include utilizing the virtual marketing assistant to prompt the user to input answers in response to the questionnaire. According to some embodiments, producing the document, marketing content, and/or strategy, and/or action plan may include utilizing the virtual marketing assistant to display the produced document, marketing content, and/or strategy, and/or action plan.).
Manova does not explicitly teach generate the telco plan.
However, Adams teaches generate the telco plan (
Adams: Col. 3 Ln. 28-45, a method for generating personalized telecommunications package recommendations comprising: building, by a matrix builder application on a server, a lifestyle attribute/component matrix; receiving, by an application on a server, a plurality of lifestyle attribute selections; and calculating, by the application, a total user score based on at least the selected lifestyle attributes.)
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Referring to Claim 12, Manova teaches the method of claim 1 further comprising:
Manova does not explicitly teach repeating the activated AI NLP telco plan generation system operations a, b, and c and performing operations comprising:
generating at least one additional proposed telco plan consistent with the machine perceived user intended telco plan with each additional proposed telco plan having at least one unique feature or feature value or at least one unique feature and feature value.
However, Adams teaches repeating the activated AI NLP telco plan generation system operations a, b, and c and performing operations comprising:
generating at least one additional proposed telco plan consistent with the machine perceived user intended telco plan with each additional proposed telco plan having at least one unique feature or feature value or at least one unique feature and feature value (
Adams: Col. 13 Ln. 60-14 Ln. 12, additional components from the components in a recommended package, for example, extra car chargers, may also be discounted with the purchase of a package. Telecommunications service providers may also consider factors such as performance history, compatibility with other devices and/or accessories, and technical compatibility in addition to user input to recommend not just a single component but rather a package of components to a user. The personalized recommendation engine discussed herein scores an entire pool of components that could potentially be packaged together in various combinations and does not remove any components from the pool based upon user input or telecommunications service provider input.
Adams: Col. 15 Ln. 45-60, may have the option to multiply the number of components in a package, for example, in order to obtain an extra device charger or an additional case or protector in a different color or style. The package price may comprise discounts as compared to the price of each component if purchased alone, and further discounts may be available for additional components.).
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Referring to Claim 13, Manova teaches the method of claim 1
natural language, identification of one or more competitor plans (
Manova: Sec. 0100, According to some embodiments, the one or more documents may include, but are not limited to, an investor deck, one pager, company profile, business plan, marketing plan, competitive analysis, swot analysis, financial plan (or a budget plan), sales deck, marketing deck, two pager, daily/weekly/monthly/annual: social media plan, content marketing plan, Google ads plan, digital advertising plan, social advertising plan, organic content calendar, email marketing plan, comprehensive editorial calendar and the like.
Manova: Sec. 0107, the competitive analysis may refer to a document or a portion of a document associated with competitors of the company. According to some embodiments, the competitive analysis may include a list of one or more competitors of the company. According to some embodiments, the competitive analysis may include an analysis of one or more competitors of the company. According to some embodiments, the competitive analysis may include a chart or graph depicting the type of competition that each competitor provides in relation to the company, when comparing one or more of the products and/or services of the user's company with the competitor's company across various criteria.),
Manova does not explicitly teach wherein the natural language telco plan request framework includes sufficient information for the AI NLP telco plan generation system to determine a business objective, a target segment, and telco plan features and feature values.
However, Adams teaches wherein the natural language telco plan request framework includes sufficient information for the AI NLP telco plan generation system to determine a business objective, a target segment, and telco plan features and feature values (
Adams: Col. 4 Ln. 22-37, Customer satisfaction, retention, growth, and profitability may be goals for telecommunications service providers as well as other providers of user-preference and lifestyle-related services.
Adams: Col. 6 Ln. 1-15, personalized package recommendation may also include a weighting. This may include the telecommunications service provider providing input as to the expected revenue, inventory level, serviceability, compatibility, and reliability of a package component).
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Claims 14-20 and 24-26 recite limitations that stand rejected via the art citations and rationale applied to claims 1-7 and 11-13. Regarding, an apparatus comprising:
one or more processors; and a memory, coupled to the one or more processors, that includes code that when executed causes the one or more processors to perform operations comprising (
Manova: Sec. 0253, The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing a processor to carry out aspects of the present invention
Manova: Sec. 0254, A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or Flash memory), a static random access memory (SRAM), a portable compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory stick, a floppy disk, a mechanically encoded device having instructions recorded thereon, and any suitable combination of the foregoing.):
Claims 8-10 and 21-23 are rejected under 35 U.S.C. 103 as being unpatentable over United States Patent Publication US 20250104106, Manova, et al., to hereinafter Manova in view of United States Patent Number US 9338308, Adams, et al., to hereinafter Adams in view of United States Patent Publication US 20250200358, Lee, et al., to hereinafter Lee in view of United States Patent Publication US 20220122344, Liu, et al., to hereinafter Liu in view of United States Patent Publication US 20210209656, Bonzi, et al.
Referring to Claim 8, Manova teaches the method of claim 1 wherein revising the allowances values for each feature to meet predetermined telco plan constraints comprises:
generating marketing information for the generated proposed telco plan (See Adams), wherein generating the marketing information comprises; generate marketing metadata for the marketing; providing the marketing information based on the marketing information (
Manova: Sec. 0028, Deep learning models on the user-specific data repository to automatically generate a digital marketing action plan and associated content.
Manova: Sec. 0081, FIG. 1A shows a flowchart of functional steps in a computer implemented method for generation of documents, marketing content, and/or strategy, and/or action plan with personalized marketing content, and/or strategy, and/or action plan, in accordance with some embodiments of the present invention;
Manova: Sec. 0200, the method for generation of documents with personalized marketing content, and/or strategy, and/or action plan may include generating documents that are personalized to the user and/or the company of the user. ):
access pre-configured marketing information settings (
Manova: Sec. 0164, According to some embodiments, the method may include outputting and/or displaying, to the user, one or more options for documents that can be produced According to some embodiments, the one or more documents may include, but are not limited to, personalized marketing content, and/or strategies and/or action plans for the company of the user..);
Manova describes setting similar to Applicant’s spec at 0072.
Manova does not explicitly teach generated proposed telco plan; activating the AI NLP telco plan generation.
However, Adams teaches these limitations.
generated proposed telco plan (
Adams: Col. 3 Ln. 28-45, a method for generating personalized telecommunications package recommendations comprising: building, by a matrix builder application on a server, a lifestyle attribute/component matrix; receiving, by an application on a server, a plurality of lifestyle attribute selections; and calculating, by the application, a total user score based on at least the selected lifestyle attributes.);
activating the AI NLP telco plan generation system to utilize the generated proposed telco plan (
Adams: Col. 7 Ln. 1-15, The personalized recommendation engine may take the user's input and recommend packages that include social media applications, devices capable of executing those applications, and subscription plans suited to mid-to-high levels of traffic and data consumption resulting from those applications.).
Manova and Adams are both directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova, which teaches detecting and repairing information technology problems in view of Adams, to efficiently apply analysis of the use of artificial intelligence to enhancing the capability to collecting and comparing business features. (See Adams at Col. 3 Ln. 44-47, Col. 5 Ln. 60 - 6 Ln. 7, Col. 12 Ln. 4-20).
Manova in view of Adams does not explicitly teach to a text-to-image generative artificial intelligence (AI) system to generate an image; sending all the metadata to a video automater to generate video marketing information.
However, Lee teaches these limitations
to a text-to-image generative artificial intelligence (AI) system to generate an image (
Lee: Sec. 0003, generative artificial intelligence models can be used include stable diffusion, in which a model generates an image from an input text description of the content of the desired image, and decision transformers, in which future actions are predicted based on sequences of prior actions within a given environment.);
sending all the metadata to a video automater to generate video marketing information (See Manova) (Lee: Sec. 0041, to generate textual data and feature outputs from data captured by the peripheral devices 112 that can be provided as an input into a generative artificial intelligence model (e.g., the generative model 114 deployed on the edge inferencing system 110, the generative model 134 deployed on the cloud inferencing system 130, and/or other generative models deployed on other devices within the hybrid computing environment 200 and not illustrated in FIG. 2 ). The high-performance model 214 may, for example, allow for the integration of multiple data modalities into a multimodal query output to a generative model for further processing. By doing so, text, audio, video, sensor, and other data modalities may be combined to allow for broader contextual information to be included in a query, which may allow the generative model to generate a response to a query that leverages the additional information captured by various peripheral devices as context for a textual representation of a query.
Lee: Sec. 0042, In some aspects, the low-power model 212 may be omitted, and the high-performance model 214 can be invoked on-demand in order to process inputs from the peripherals integrated with or connected to the edge inferencing system 110 and generate the data (e.g., textual data) and/or feature outputs that can be provided as an input into a generative artificial intelligence model.
Lee: Sec. 0043, The orchestrator 116 at the edge inferencing system 110 can use the data and/or feature outputs generated by the prompt-generating models 210 to generate a prompt and transmit the prompt to the cloud inferencing system (or the local inferencing system 120 (not shown in FIG. 2 )) for processing. In response, the generative model 134 at the cloud inferencing system 130 generates a response based on the received prompt and transmits the generated response to the orchestrator 116 at the edge inferencing system 110. The orchestrator 116 can then output (e.g., textually, visually, audibly, etc.) the generated response to the user of the edge inferencing system 110.);
Lee describes the generating the output of a video via AI
Manova, Adams, and Lee are all directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45; Lee at 0004-0006, 0024, 0075). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova in view of Adams, which teaches detecting and repairing information technology problems in view of Lee, to efficiently apply analysis of the use of artificial intelligence to improving the processing of data to include generating images. (See Lee at 0003, 0123, 0109).
Manova in view of Adams in view of Lee does not explicitly teach removing background information from the AI generated image to blend the image with a background of a template for the marketing information.
However, Liu teaches removing background information from the AI generated image to blend the image with a background of a template for the marketing information (See Manova) (
Liu: Sec. 0011, cropping and adjusting a background part of the to-be-optimized image according to resolution requirements of an output target image and a basic composition principle;
Liu: Sec. 0018, Preferably, the application scenario is obtained according to an application scenario that is calibrated by the template image, and at least comprises: an avatar, a background, a desktop, or a cover;
Liu: Sec. 0022, an image optimization system based on artificial intelligence, comprising: a template database module, an object recognition module, a template matching module, an object conversion module, a background optimization module and a synthesis module);
Manova, Adams, Lee, and Liu are all directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45; Lee at 0004-0006, 0024, 0075, Liu at 0039, ). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova in view of Adams, which teaches detecting and repairing information technology problems in view of Lee, to efficiently apply analysis of the use of artificial intelligence to improving the processing of data to include generating images and its backgrounds. (See Liu at 0015, 0032, 0067).
Manova in view of Adams in view of Lee send the output image uniform resource locators to one or more computer systems.
However, Bonzi teaches send the output image uniform resource locators to one or more computer systems (
Bonzi: Sec. 0054, The image URLs extracted from lines 18-20 of Table 1 are included at lines 14-16 of Table 3. The color values extracted from lines 14-17 of Table 1 are included at lines 18-21 of Table 3. In step 307, the facility parses the XML definition constructed in step 306 to obtain an XML tree. In step 308, the facility uses the tree obtained in step 307 to construct the advertising message.).
It would have been obvious for one having ordinary skill in the art at the time of the invention, to have modified Manova in view of Adams in view of Lee with the motivation of refining the processing of data to include the placing of advertisement online.
Bonzi: Sec. 0022, The user input typically identifies the model page by specifying its URL, and can be received from the user in a variety of ways. In some embodiments, the facility serves a web page (not shown) to the user, and asks the user to type or paste the URL of the model page into a model page URL field. In some embodiments, the operator of the facility works with one or more publishers to cause an advertising message generation control to be incorporated in a number of different potential model pages.
Bonzi: Sec. 0024, The page also contains control 450 that the user may activate in order to generate advertising messages for this page. In some embodiments, this control is included only in instances of the page served to a user who is the “owner” of the page, in this case a user associated with the auto dealer that is selling the car. In some embodiments, activation of this control causes the user's browser to send a request or other communication to a server on which aspects of the facility are executing. This communication contains the URL of the page, or some other basis for identifying the page such as a page ID.
Referring to Claim 9, Manova teaches the method of claim 8
providing the marketing information, advertising the marketing information (
Manova: Sec. 0028, Deep learning models on the user-specific data repository to automatically generate a digital marketing action plan and associated content.
Manova: Sec. 0081, FIG. 1A shows a flowchart of functional steps in a computer implemented method for generation of documents, marketing content, and/or strategy, and/or action plan with personalized marketing content, and/or strategy, and/or action plan, in accordance with some embodiments of the present invention;
Manova: Sec. 0200, the method for generation of documents with personalized marketing content, and/or strategy, and/or action plan may include generating documents that are personalized to the user and/or the company of the user. )
Manova in view of Adams does not explicitly teach wherein revising the allowances values for each feature to meet predetermined telco plan constraints comprises: a stable diffusion XL image generation model to the text-to-image generative artificial intelligence system to generate an image.
However, Lee teaches wherein revising the allowances values for each feature to meet predetermined telco plan constraints comprises: a stable diffusion XL image generation model to the text-to-image generative artificial intelligence system to generate an image (
Lee: Sec. 0003, Generative artificial intelligence models can be used in various environments in order to generate a response to an input query. For example, generative artificial intelligence models can be used in chatbot applications in which large language models are used to generate an answer, or at least a response, to an input query. Other examples in which generative artificial intelligence models can be used include stable diffusion, in which a model generates an image from an input text description of the content of the desired image, and decision transformers, in which future actions are predicted based on sequences of prior actions within a given environment.).
Manova, Adams, and Lee are all directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45; Lee at 0004-0006, 0024, 0075, ). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova in view of Adams, which teaches detecting and repairing information technology problems in view of Lee, to efficiently apply analysis of the use of artificial intelligence to improving the processing of data to include generating images. (See Lee at 0003, 0123, 0109).
Referring to Claim 10, Manova teaches the method of claim 8 further comprising:
Manova in view of Adams does not explicitly teach using a python library.
However, Lee teaches using a python library (
Lee: Sec. 0048, a code generation example in which the query relates to generating source code (e.g., in Python, C++, or some other programming language) to perform a particular task, the orchestrator 116 can use a compiler/interpreter and a unit testing framework to check the generated source code).
Manova, Adams, and Lee are all directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45; Lee at 0004-0006, 0024, 0075, ). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova in view of Adams, which teaches detecting and repairing information technology problems in view of Lee, to efficiently apply analysis of the use of artificial intelligence to improving the processing of data to include generating images. (See Lee at 0003, 0123, 0109).
Manova in view of Adams in view of Lee does not explicitly teach blending the image with a background of a template.
However, Liu teaches blending the image with a background of a template (
Liu: Sec. 0065, 5) The background optimization module is configured to crop and adjust the background part of the to-be-optimized image according to the resolution requirements of an output target image and a basic composition principle (for example, the principle of trichotomy, golden ratio, etc.).
Liu: Sec. 0066, The synthesis module is configured to, according to the layout information in the optimal template information, combine the image of the primary object processed by the object conversion module with the background image processed by the background optimization module to restore to a complete image. Specifically, the combining and restoring is performed according to image location information in the layout information.)
Manova, Adams, Lee and Liu are all directed to the analysis of the use of artificial intelligence (See Manova:,0095, 0131, 0147 ; Adams at Col. 5, 6, Col. 7 Ln. 1-45; Lee at 0004-0006, 0024, 0075, Liu at 0039, ). Manova discloses that additional elements, such as an natural language processing models can be considered (See Manova: Col. 20 Ln. 10-30,). 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 Manova in view of Adams, which teaches detecting and repairing information technology problems in view of Lee, to efficiently apply analysis of the use of artificial intelligence to improving the processing of data to include generating images and its backgrounds. (See Liu at 0015, 0032, 0067).
Claims 21-23 recite limitations that stand rejected via the art citations and rationale applied to claims 8-10.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Khoury et al., U.S. Pub. 20210224858, (discussing the operation and managing of artificial intelligence).
Liu et al., W.O. Pub. 2020145691, (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).
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