DETAILED ACTION
This communication is in response to the Amendments filed on 2/4/2026. Claims 1-20 are pending and have been examined. Hence, this action id made FINAL.
Any previous objection/rejection not mentioned in this Office Action has been withdrawn by the examiner.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 13, 2013, is being examined under the first inventor to file provisions of the AIA .
Response to Arguments
With regards to 35 U.S.C. 101 the current amendments made to the independent claims have overcome the rejections. This is due to the additional component of an artificial intelligence model being integrated throughout the claim limitations. The type of AI model is selected based on the input and the parameters of the model are modified by a user interacting with a settings menu.
With regards to 35 U.S.C. 103 the applicant has amended the claims to overcome the existing prior art. An updated prior art search has been performed and updated rejections and mapping can be found below.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1-2, 6-7, 13-14, and 20 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 20250005294 A1 (Ochs et al.) in view of US Patent Application Publication US 20240320961 A1 (Shimada et al.) and US Patent Application Publication US 20250315629 A1 (De Wynter et al.).
Regarding Claim 1, Ochs et al. teaches A method for creating résumé content, comprising:
(Methods and systems are provided herein for automatically generating a targeted resume of an applicant applying for a job using a plurality of artificial intelligence (AI) models, where the targeted resume is based on an initial resume and a job description supplied by the applicant.) (Paragraph 18).
receiving a natural language prompt comprising prompt text submitted by a user by way of a user interface, the prompt text including at least a job title for which to generate the résumé content;
(As described in greater detail below, resume generator 102 may follow an automated process to generate a targeted resume tailored to a specific job description inputted into resume generator 102 based on information supplied in one or more text documents 120. In various embodiments, text documents 120 include an initial resume 122, and a job description 124. Text documents 120 may be submitted to resume generator 102 by a user 101, who may be an applicant for a job matching job description 124. In other words, based on initial resume 122 and job description 124, resume generator 102 may generate the targeted resume automatically without additional input by user 101.) (Paragraph 28).
(Text document 120 may be submitted to resume generator 102 via a user interface (UI)) (paragraph 29)
(The stored elements of job description 124 may include, for example, a job title, required education, required work experience, required skills, etc.) (Paragraph 36)
A user submits a job description via a user interface in order to generate a summary. This is submitted in the form of text documents which include natural language for an existing summary and job description.
Identifying, for a selected artificial intelligence (AI) model chosen from a set of AI models configured to process the prompt text,
(Second workflow 250 starts at AI model selection block 256, where an AI model is selected from a plurality of available AI models) (Paragraph 44).
(At a subsequent submission block 260, the selected resume element may be submitted to the AI model using chained prompts) (Paragraph 45).
Ochs et al. selects an AI model to process the prompts created from the input text.
a set of parameters configured to control processing of the prompt text by the selected AI model,
(At a subsequent submission block 260, the selected resume element may be submitted to the AI model using chained prompts) (Paragraph 45).
(The first chained prompt may include specific instructions regarding how the rewritten resume element should be structured. The first chained prompt may include specific instructions describing a desired style of the rewritten resume element, such as, for example, a length and/or number of sentences to include, or a desired syntax to be used in the sentences.) (Paragraph 104).
Ochs et al. creates updated prompts based off pf the input in the form of chained prompts. The prompts are also made to specify how the AI model should structure the output it creates which can be considered a set of parameters.
transmitting the updated prompt to the selected AI model;
(In a second chained prompt, a result of the first chained prompt may be submitted to the AI model, and the AI model may be further instructed to refine the output of the first chained prompt.) (Paragraph 106).
All of the chained prompts are submitted to the AI model and the above quote shows just one example of the first prompt being used.
and receiving a text-based response from the selected AI model based on the prompt text, the and in a format corresponding to the response formatting instructions.
(At 318, method 300 includes sending the resume and the final report to the user, and/or displaying the resume and/or the final report on a display device of the resume generation system, such as display device 130 of FIG. 1.) (Paragraph 69).
The created resume is output to the user. Figs 9 and 10 show examples of the text-based format for the resumes.
Ochs et al. does not explicitly teach: wherein the set of parameters are indicated within an implemented setting menu that is accessible by the user for adjusting the set of parameters; executing instructions to create an updated prompt by modifying the prompt text to include a set of response formatting instructions and a set of additional instructions, wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
However, Shimada et al. teaches wherein the set of parameters are indicated within an implemented setting menu that is accessible by the user for adjusting the set of parameters;
(FIG. 16 is a diagram showing one example of a hyperparameter setting screen. The hyperparameter setting screen is a screen displayed on the display device 156 in the step S53 of the machine learning process shown in FIG. 15.) (Paragraph 119).
(Further, because the GUI (the hyperparameter setting screen 600) for setting a hyperparameter is displayed on the display device 156, the user can easily set a hyperparameter before machine learning.) (Paragraph 149)
Shimada et al. teaches a system which gives the users a setting menu to modify parameters of the model prior to executing the prompt on the model. Fig. 16 shows the hyperparameter setting screen.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. to allow the user to modify parameters from a setting menu as taught by Shimada et al. This would have been an obvious improvement to allow the user to modify the behavior/training of the AI model being used (Shimada et al. Paragraph 62).
Ochs et al. in view of Shimada et al. does not explicitly teach: executing instructions to create an updated prompt by modifying the prompt text to include a set of response formatting instructions and a set of additional instructions, wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
However, De Wynter et al. teaches executing instructions to create an updated prompt by modifying the prompt text to include a set of response formatting instructions and a set of additional instructions,
(Prompt template 700 includes instructions which task an LLM with generating feedback with respect to a writing and a suggestion for how to modify the writing to improve its readability. Prompt template 700 includes a field for the selected content and for user-specific preferences with respect to readability of a writing, consistency of the assistive suggestions, targeted feedback, and display options for obtaining writing assistance.
De Wynter et al. teaches a system which creates AI prompts used to generate documents from an AI model. The prompts include formatting instructions and user-specific preferences (additional instructions). This can be seen in Figs. 7-8 where example prompts are shown with specific formatting instructions and an area for user preferences.
wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
(FIG. 5 illustrates elements of user experience 500 by which an application receives personalization information for a user in an implementation. An application may display chat interface 501 where a user can interact with an application assistant with respect to content creation. In menu 502, the user is presented with options for prompting the application assistant to obtain different types of assistance with respect to content generation, including an option to submit inquiries (“Ask”) to the application assistant.) (Paragraph 67).
The user-preferences (additional instructions) are submitted in a setting menu and can be considered a set of parameters as they modify the output of the AI model. This menu can be seen in Figs. 5 and 6C
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. to create a prompt using formatting instructions and user selected preferences as taught by De Wynter et al. This would have been an obvious improvement to allow the user to modify the behavior of the AI model in format that is simpler for anyone to understand regardless of knowledge of LLMs (De Wynter et al. Paragraph 18).
Regarding Claim 2, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the method of claim 1.
Furthermore, Ochs et al. teaches presenting the text-based response as one or more résumé contents to the user for review.
(At 318, method 300 includes sending the resume and the final report to the user, and/or displaying the resume and/or the final report on a display device of the resume generation system, such as display device 130 of FIG. 1.) (Paragraph 69).
(A report may be generated including the highest-quality revision, and the report may include a reasoning of the AI models used to generate each section. In this way, a user may read the report to see how the resume was improved by the AI models.) (Paragraph 128).
The final report consisting of both the generated resume and the AI’s justification for the decision is given to the user.
Regarding Claim 6, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the method of claim 1.
Furthermore, De Wynter et al. teaches wherein the set of parameters comprises one or more of a temperature parameter, a maximum token limit, a top probability parameter, or a frequency penalty to be implemented at the selected AI model during processing of the updated prompt
(The prompt template may also include instructions for the LLM, such as specifying parse-able format for the output, a maximum length (e.g., token length) of the output, and a sensitivity encoding scheme by which the LLM returns a value which classifies the selected content and the generated output for potentially insensitive content.) (Paragraph 51).
(Similar to review pane 611, review pane 612 includes graphical buttons by which the user can select a tone and/or temperature by which the LLM will generate an updated modified version of the content.) (Paragraph 73).
De Wynter et al. includes user selectable preferences for temperature and maximum tokens. The updated prompt can be seen in Figs. 7 and 8
Regarding Claim 7, Ochs et al. teaches A method for generating content for a résumé, comprising:
(Methods and systems are provided herein for automatically generating a targeted resume of an applicant applying for a job using a plurality of artificial intelligence (AI) models, where the targeted resume is based on an initial resume and a job description supplied by the applicant.) (Paragraph 18).
providing a user interface having a prompt field, a content output field, and one or more interactive elements;
(Method 300 starts at 302, where the method includes receiving a resume (e.g., the first resume) and a job description from a user of the resume generation system. In various embodiments, the job description and the resume may be received by the resume generator via a user interface displayed in a web browser, as described above referenced FIG. 1. For example, the resume and the job description may be saved as documents, and the documents may be uploaded to the resume generator via the user interface, or the resume and job description may be copied/cut and pasted into the user interface.) (Paragraph 57).
(generating a report including the rewritten resume and the reasoning, and displaying the report on a display device) (Paragraph 132).
Ochs et al. describes a device with interactive elements for submitting a prompt in the form of a job description. There is also an output displayed to the user of the generated resume.
receiving a natural language prompt comprising prompt text at the prompt field, the prompt text being a user request including a job title for which to generate a résumé content;
(As described in greater detail below, resume generator 102 may follow an automated process to generate a targeted resume tailored to a specific job description inputted into resume generator 102 based on information supplied in one or more text documents 120. In various embodiments, text documents 120 include an initial resume 122, and a job description 124. Text documents 120 may be submitted to resume generator 102 by a user 101, who may be an applicant for a job matching job description 124. In other words, based on initial resume 122 and job description 124, resume generator 102 may generate the targeted resume automatically without additional input by user 101.) (Paragraph 28).
(Text document 120 may be submitted to resume generator 102 via a user interface (UI)) (paragraph 29)
(The stored elements of job description 124 may include, for example, a job title, required education, required work experience, required skills, etc.) (Paragraph 36)
A user submits a job description via a user interface in order to generate a summary. This is submitted in the form of text documents which include natural language for an existing summary and job description.
selecting, based on the prompt text and a set of defined criteria, an artificial intelligence (AI) model from a set of AI models configured to process the prompt text;
(The AI model may be selected from the plurality of available AI models 252 at an AI service optimization block 254, which may determine a most suitable AI model based on first resume 202 and job description 204. In various embodiments, AI service optimization block 254 may rely on a predictive ML model such as a decision tree model, as described above in reference to FIG. 1. The selection of the AI model may be performed by the AI services optimization module 112 of resume generator 102.) (Paragraph 44).
The AI model is selected based on the text from the resume and job description provided in the input where a decision tree represents a set of defined criteria for selecting the model.
Identifying, for a selected AI model, a set of parameters configured to control processing of the prompt text by the selected AI model,
(At a subsequent submission block 260, the selected resume element may be submitted to the AI model using chained prompts) (Paragraph 45).
(The first chained prompt may include specific instructions regarding how the rewritten resume element should be structured. The first chained prompt may include specific instructions describing a desired style of the rewritten resume element, such as, for example, a length and/or number of sentences to include, or a desired syntax to be used in the sentences.) (Paragraph 104).
Ochs et al. creates updated prompts based off pf the input in the form of chained prompts. The prompts are also made to specify how the AI model should structure the output it creates which can be considered a set of parameters.
and receiving, at a content output field, one or more résumé contents generated by the selected AI model based on the prompt text, the one or more résumé contents being presented in a format corresponding to the response formatting instructions.
(At 318, method 300 includes sending the resume and the final report to the user, and/or displaying the resume and/or the final report on a display device of the resume generation system, such as display device 130 of FIG. 1.) (Paragraph 69).
(The first chained prompt may include specific instructions regarding how the rewritten resume element should be structured. ) (Paragraph 104).
The created resume is output to the user on a display in the format specified in the generated prompt. Figs 9 and 10 show examples of the text-based format for the resumes.
Ochs et al. does not explicitly teach: wherein the set of parameters are indicated within an implemented setting menu that is accessible by the user for adjusting the set of parameters; executing instructions to generate, based on the selected AI model, an updated prompt including: a set of response formatting instructions, the selected AI model, and a set of additional instructions, wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
However, Shimada et al. teaches wherein the set of parameters are indicated within an implemented setting menu that is accessible by the user for adjusting the set of parameters;
(FIG. 16 is a diagram showing one example of a hyperparameter setting screen. The hyperparameter setting screen is a screen displayed on the display device 156 in the step S53 of the machine learning process shown in FIG. 15.) (Paragraph 119).
(Further, because the GUI (the hyperparameter setting screen 600) for setting a hyperparameter is displayed on the display device 156, the user can easily set a hyperparameter before machine learning.) (Paragraph 149)
Shimada et al. teaches a system which gives the users a setting menu to modify parameters of the model prior to executing the prompt on the model. Fig. 16 shows the hyperparameter setting screen.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. to allow the user to modify parameters from a setting menu as taught by Shimada et al. This would have been an obvious improvement to allow the user to modify the behavior/training of the AI model being used (Shimada et al. Paragraph 62).
Ochs et al. in view of Shimada et al. does not explicitly teach: executing instructions to generate, based on the selected AI model, an updated prompt including: a set of response formatting instructions, the selected AI model, and a set of additional instructions, wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
However, De Wynter et al. teaches executing instructions to generate, based on the selected AI model, an updated prompt including: a set of response formatting instructions, the selected AI model, and a set of additional instructions,
(Prompt template 700 includes instructions which task an LLM with generating feedback with respect to a writing and a suggestion for how to modify the writing to improve its readability. Prompt template 700 includes a field for the selected content and for user-specific preferences with respect to readability of a writing, consistency of the assistive suggestions, targeted feedback, and display options for obtaining writing assistance.
De Wynter et al. teaches a system which creates AI prompts used to generate documents from an AI model. The prompts include formatting instructions and user-specific preferences (additional instructions). This can be seen in Figs. 7-8 where example prompts are shown with specific formatting instructions and an area for user preferences.
wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
(FIG. 5 illustrates elements of user experience 500 by which an application receives personalization information for a user in an implementation. An application may display chat interface 501 where a user can interact with an application assistant with respect to content creation. In menu 502, the user is presented with options for prompting the application assistant to obtain different types of assistance with respect to content generation, including an option to submit inquiries (“Ask”) to the application assistant.) (Paragraph 67).
The user-preferences (additional instructions) are submitted in a setting menu and can be considered a set of parameters as they modify the output of the AI model. This menu can be seen in Figs. 5 and 6C
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. to create a prompt using formatting instructions and user selected preferences as taught by De Wynter et al. This would have been an obvious improvement to allow the user to modify the behavior of the AI model in format that is simpler for anyone to understand regardless of knowledge of LLMs (De Wynter et al. Paragraph 18).
Regarding Claim 13, Ochs et al. teaches A processing system, comprising: a memory comprising computer-executable instructions;
(Resume generator 102 includes a processor 104 and a non-transitory memory 106. Processor 104 may be configured to execute machine readable instructions stored in non-transitory memory 106.) (Paragraph 30).
and one or more processors configured to execute computer-executable instructions causing the processing system to:
(Resume generator 102 includes a processor 104 and a non-transitory memory 106. Processor 104 may be configured to execute machine readable instructions stored in non-transitory memory 106.) (Paragraph 30).
receive a natural language prompt comprising prompt text submitted by a user by way of a user interface, the prompt text including at least a job title for which to generate a résumé content;
(As described in greater detail below, resume generator 102 may follow an automated process to generate a targeted resume tailored to a specific job description inputted into resume generator 102 based on information supplied in one or more text documents 120. In various embodiments, text documents 120 include an initial resume 122, and a job description 124. Text documents 120 may be submitted to resume generator 102 by a user 101, who may be an applicant for a job matching job description 124. In other words, based on initial resume 122 and job description 124, resume generator 102 may generate the targeted resume automatically without additional input by user 101.) (Paragraph 28).
(Text document 120 may be submitted to resume generator 102 via a user interface (UI)) (paragraph 29)
(The stored elements of job description 124 may include, for example, a job title, required education, required work experience, required skills, etc.) (Paragraph 36)
A user submits a job description via a user interface in order to generate a summary.
identify, for a selected artificial intelligence (AI) model chosen from a set of AI models configured to process the prompt text,
(Second workflow 250 starts at AI model selection block 256, where an AI model is selected from a plurality of available AI models) (Paragraph 44).
(At a subsequent submission block 260, the selected resume element may be submitted to the AI model using chained prompts) (Paragraph 45).
Ochs et al. selects an AI model to process the prompts created from the input text.
a set of parameters configured to control processing of the prompt text by the selected AI model,
(At a subsequent submission block 260, the selected resume element may be submitted to the AI model using chained prompts) (Paragraph 45).
(The first chained prompt may include specific instructions regarding how the rewritten resume element should be structured. The first chained prompt may include specific instructions describing a desired style of the rewritten resume element, such as, for example, a length and/or number of sentences to include, or a desired syntax to be used in the sentences.) (Paragraph 104).
Ochs et al. creates updated prompts based off pf the input in the form of chained prompts. The prompts are also made to specify how the AI model should structure the output it creates which can be considered a set of parameters.
transmitting the updated prompt to the selected AI model;
(In a second chained prompt, a result of the first chained prompt may be submitted to the AI model, and the AI model may be further instructed to refine the output of the first chained prompt.) (Paragraph 106).
All of the chained prompts are submitted to the AI model and the above quote shows just one example of the first prompt being used.
and receive a text-based response from the selected AI model based on the prompt text, the text-based response being received in a format corresponding to the response formatting instructions. (At 318, method 300 includes sending the resume and the final report to the user, and/or displaying the resume and/or the final report on a display device of the resume generation system, such as display device 130 of FIG. 1.) (Paragraph 69).
(The first chained prompt may include specific instructions regarding how the rewritten resume element should be structured. ) (Paragraph 104).
The created resume is output to the user on a display in the format specified in the generated prompt. Figs 9 and 10 show examples of the text-based format for the resumes.
Ochs et al. does not explicitly teach: wherein the set of parameters are indicated within an implemented setting menu that is accessible by the user for adjusting the set of parameters; executing instructions to create an updated prompt by modifying the prompt text to include a set of response formatting instructions and a set of additional instructions, wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
However, Shimada et al. teaches wherein the set of parameters are indicated within an implemented setting menu that is accessible by the user for adjusting the set of parameters;
(FIG. 16 is a diagram showing one example of a hyperparameter setting screen. The hyperparameter setting screen is a screen displayed on the display device 156 in the step S53 of the machine learning process shown in FIG. 15.) (Paragraph 119).
(Further, because the GUI (the hyperparameter setting screen 600) for setting a hyperparameter is displayed on the display device 156, the user can easily set a hyperparameter before machine learning.) (Paragraph 149)
Shimada et al. teaches a system which gives the users a setting menu to modify parameters of the model prior to executing the prompt on the model. Fig. 16 shows the hyperparameter setting screen.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. to allow the user to modify parameters from a setting menu as taught by Shimada et al. This would have been an obvious improvement to allow the user to modify the behavior/training of the AI model being used (Shimada et al. Paragraph 62).
Ochs et al. in view of Shimada et al. does not explicitly teach: executing instructions to create an updated prompt by modifying the prompt text to include a set of response formatting instructions and a set of additional instructions, wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
However, De Wynter et al. teaches executing instructions to create an updated prompt by modifying the prompt text to include a set of response formatting instructions and a set of additional instructions,
(Prompt template 700 includes instructions which task an LLM with generating feedback with respect to a writing and a suggestion for how to modify the writing to improve its readability. Prompt template 700 includes a field for the selected content and for user-specific preferences with respect to readability of a writing, consistency of the assistive suggestions, targeted feedback, and display options for obtaining writing assistance.
De Wynter et al. teaches a system which creates AI prompts used to generate documents from an AI model. The prompts include formatting instructions and user-specific preferences (additional instructions). This can be seen in Figs. 7-8 where example prompts are shown with specific formatting instructions and an area for user preferences.
wherein the set of additional instructions are generated based on the set of parameters indicated within the implemented setting menu;
(FIG. 5 illustrates elements of user experience 500 by which an application receives personalization information for a user in an implementation. An application may display chat interface 501 where a user can interact with an application assistant with respect to content creation. In menu 502, the user is presented with options for prompting the application assistant to obtain different types of assistance with respect to content generation, including an option to submit inquiries (“Ask”) to the application assistant.) (Paragraph 67).
The user-preferences (additional instructions) are submitted in a setting menu and can be considered a set of parameters as they modify the output of the AI model. This menu can be seen in Figs. 5 and 6C
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. to create a prompt using formatting instructions and user selected preferences as taught by De Wynter et al. This would have been an obvious improvement to allow the user to modify the behavior of the AI model in format that is simpler for anyone to understand regardless of knowledge of LLMs (De Wynter et al. Paragraph 18).
Regarding Claim 14, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the method of claim 13.
Furthermore, Ochs et al. teaches wherein the one or more processors are further configured to execute computer-executable instructions causing the processing system to present the text-based response as one or more résumé contents to the user for review
(At 318, method 300 includes sending the resume and the final report to the user, and/or displaying the resume and/or the final report on a display device of the resume generation system, such as display device 130 of FIG. 1.) (Paragraph 69).
(A report may be generated including the highest-quality revision, and the report may include a reasoning of the AI models used to generate each section. In this way, a user may read the report to see how the resume was improved by the AI models.) (Paragraph 128).
The final report consisting of both the generated resume and the AI’s justification for the decision is given to the user.
Regarding Claim 20, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the method of claim 1.
Furthermore, Shimada et al. teaches further comprising receiving a selection, made by the user within the settings menu, of the selected AI model chosen from the set of AI models configured to process the prompt text.
(FIG. 14 is a diagram showing one example of an external learning model acquisition screen. The external learning model acquisition screen is a screen displayed on the display device 156 in the step S34 of the learning model acquisition process.) (Paragraph 110).
(In a case in which the user designates the button 594 with one learning model selected in the area 592, the selected learning model is selected as being subjected to machine learning.) (Paragraph 113)
Shimada et al. allows the user to select which AI model they would like to use via a GUI settings menu. This can be seen in Fig. 14 where a model select screen is displayed.
Claims 3, 4, 8, 9, 15, and 19 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 20250005294 A1 (Ochs et al.) in view of US Patent Application Publication US 20240320961 A1 (Shimada et al.) and US Patent Application Publication US 20250315629 A1 (De Wynter et al.) and further in view of US Patent Application Publication US 20120084633 A1 (Dondurur et al.).
Regarding Claim 3, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the system of claim 1.
Ochs et al. in view of Shimada et al. and De Wynter et al. does not explicitly teach: receiving a subset of the one or more résumé contents, selected by the user, as selected résumé contents; and storing the selected résumé contents in a content database for on-demand retrieval and inclusion in a résumé.
However, Dondurur et al. teaches receiving a subset of the one or more résumé contents, selected by the user, as selected résumé contents;
(A plurality of resume formats are then displayed to the user. The plurality of resume formats correspond to the textual resume template and the plurality of graphical resume templates stored in the database (represented by 20 and 22, respectively, in FIG. 1). Upon user selection of one of the plurality of resume formats (step 12), the at least one fillable resume field of the corresponding one of the textual resume template and the plurality of graphical resume templates stored in the database is filled with the bibliographic data stored in the database to produce a resume (step 14).) (Paragraph 33).
In Dondurur et al. a method of creating resumes for users is presented in which the created resumes are presented to the user for their selection. In this case a resume content represents the visual style of the template which is then filled in to generate the completed resume.
and storing the selected résumé contents in a content database for on-demand retrieval and inclusion in a résumé.
(The resume is stored in the database as an electronic document and is displayed to the user (step 18). The electronic document may then be printed or transmitted over the computer network.) (Paragraph 33).
The resumes are stored away and can be accessed by the user via printing or a computer.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. and De Wynter et al. to show the user multiple resumes that they can choose from as taught by Dondurur et al. This would have been an obvious improvement to give the user a choice on the most desired version (Dondurur et al. Paragraph 34).
Regarding Claim 4, Ochs et al. in view of Shimada et al., De Wynter et al., and Dondurdur et al. teaches the method of claim 3.
Furthermore, Dondurdur et al. teaches mapping one or more of the selected résumé contents to at least the job title by attaching metadata tags the one or more of the selected résumé contents at least the job title extracted from the prompt text.
(The seventh field 44 allows the user to enter or import from a separate electronic document the user's employment background, including job title(s), companies, date(s), and brief descriptions of duties.) (Paragraph 32)
(Upon user selection of one of the plurality of resume formats (step 12), the at least one fillable resume field of the corresponding one of the textual resume template and the plurality of graphical resume templates stored in the database is filled with the bibliographic data stored in the database to produce a resume (step 14).) (Paragraph 33).
(The resume is stored in the database as an electronic document and is displayed to the user (step 18).) (Paragraph 34).
The resume is stored in the systems after being filled with bibliographic data that includes a job title. This bibliographic data is also stored separately in the system thus the resume is tagged by corresponding database entries.
Regarding Claim 8, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the system of claim 7.
Ochs et al. in view of Shimada et al. and De Wynter et al. does not explicitly teach: accepting a user selection of at least one selected résumé content from one or more of the résumé contents; and storing, upon activation of a first interactive element of the one or more interactive elements, the at least one selected résumé content in a résumé content database accessible by a second user interface configured to construct the résumé.
However, Dondurur et al. teaches accepting a user selection of at least one selected résumé content from one or more of the résumé contents;
(A plurality of resume formats are then displayed to the user. The plurality of resume formats correspond to the textual resume template and the plurality of graphical resume templates stored in the database (represented by 20 and 22, respectively, in FIG. 1). Upon user selection of one of the plurality of resume formats (step 12), the at least one fillable resume field of the corresponding one of the textual resume template and the plurality of graphical resume templates stored in the database is filled with the bibliographic data stored in the database to produce a resume (step 14).) (Paragraph 33).
In Dondurur et al. a method of creating resumes for users is presented in which the created resumes are presented to the user for their selection. In this case a resume content represents the visual style of the template which is then filled in to generate the completed resume.
and storing, upon activation of a first interactive element of the one or more interactive elements, the at least one selected résumé content in a résumé content database accessible by a second user interface configured to construct the résumé.
(The resume is stored in the database as an electronic document and is displayed to the user (step 18). The electronic document may then be printed or transmitted over the computer network. If the user chooses to produce a textual resume in step 12, then the electronic document produced is a conventional text resume document. If the user chooses to produce a graphical resume in step 12, then the user may choose from a plurality of graphical templates, including a wide variety of customizable graphical features and layouts, stored in the database.) (Paragraph 33).
The resumes are stored away and can be accessed by the user via printing or a computer where the printed version or accessing the resume from another device represent a second user interface.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. and De Wynter et al. to show the user multiple resumes that they can choose from as taught by Dondurur et al. This would have been an obvious improvement to give the user a choice on the most desired version (Dondurur et al. Paragraph 34).
Regarding Claim 9, Ochs et al. in view of Shimada et al., De Wynter et al., and Dondurdur et al. teaches the method of claim 8.
Furthermore, Dondurdur et al. teaches further comprising mapping the at least one selected résumé content to at least the job title by attaching to metadata of the one or more of the selected résumé contents at least the job title extracted from the prompt text.
(The seventh field 44 allows the user to enter or import from a separate electronic document the user's employment background, including job title(s), companies, date(s), and brief descriptions of duties.) (Paragraph 32)
(Upon user selection of one of the plurality of resume formats (step 12), the at least one fillable resume field of the corresponding one of the textual resume template and the plurality of graphical resume templates stored in the database is filled with the bibliographic data stored in the database to produce a resume (step 14).) (Paragraph 33).
(The resume is stored in the database as an electronic document and is displayed to the user (step 18).) (Paragraph 34).
The resume is stored in the systems after being filled with bibliographic data that includes a job title. This bibliographic data is also stored separately in the system thus the resume is tagged by corresponding database entries.
Regarding Claim 15, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the system of claim 14.
Ochs et al. in view of Shimada et al. and De Wynter et al. does not explicitly teach: receive a subset of the one or more résumé contents, selected by the user, as selected résumé contents; and store the selected résumé contents in a content database for on-demand retrieval and inclusion in a résumé.
However, Dondurur et al. teaches receive a subset of the one or more résumé contents, selected by the user, as selected résumé contents;
(A plurality of resume formats are then displayed to the user. The plurality of resume formats correspond to the textual resume template and the plurality of graphical resume templates stored in the database (represented by 20 and 22, respectively, in FIG. 1). Upon user selection of one of the plurality of resume formats (step 12), the at least one fillable resume field of the corresponding one of the textual resume template and the plurality of graphical resume templates stored in the database is filled with the bibliographic data stored in the database to produce a resume (step 14).) (Paragraph 33).
In Dondurur et al. a method of creating resumes for users is presented in which the created resumes are presented to the user for their selection. In this case a resume content represents the visual style of the template which is then filled in to generate the completed resume.
and store the selected résumé contents in a content database for on-demand retrieval and inclusion in a résumé.
(The resume is stored in the database as an electronic document and is displayed to the user (step 18). The electronic document may then be printed or transmitted over the computer network.) (Paragraph 33).
The resumes are stored away and can be accessed by the user via printing or a computer.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. and De Wynter et al. to show the user multiple resumes that they can choose from as taught by Dondurur et al. This would have been an obvious improvement to give the user a choice on the most desired version (Dondurur et al. Paragraph 34).
Regarding Claim 19, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the method of claim 13.
Furthermore, Dondurur et al. teaches map one or more of the selected résumé content to at least the job title by attaching to metadata of the one or more of the selected résumé contents at least the job title extracted from the prompt text.
(The seventh field 44 allows the user to enter or import from a separate electronic document the user's employment background, including job title(s), companies, date(s), and brief descriptions of duties.) (Paragraph 32)
(Upon user selection of one of the plurality of resume formats (step 12), the at least one fillable resume field of the corresponding one of the textual resume template and the plurality of graphical resume templates stored in the database is filled with the bibliographic data stored in the database to produce a resume (step 14).) (Paragraph 33).
(The resume is stored in the database as an electronic document and is displayed to the user (step 18).) (Paragraph 34).
In Dondurdur et al. the prompt text is represented by the bibliographic data filled out by the user (Fig. 2) and that data is attached to the selected resume templates
Claims 5, 10-12, and 16-18 are rejected under 35 U.S.C. 103 as being unpatentable over US Patent Publication US 20250005294 A1 (Ochs et al.) in view of US Patent Application Publication US 20240320961 A1 (Shimada et al.) and US Patent Application Publication US 20250315629 A1 (De Wynter et al.) and further in view of US Patent Application Publication US 20250278633 A1 (Smith et al.).
Regarding Claim 5, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the system of claim 1.
Ochs et al. in view of Shimada et al. and De Wynter et al. does not explicitly teach: transmitting to the user interface a set of saved prompts from which the prompt text is chosen.
However, Smith et al. teaches transmitting to the user interface a set of saved prompts from which the prompt text is chosen.
(The custom base prompt may be any previously submitted prompt which has been stored or archived in a memory coupled to the user interface 300, such as in the database 120 or another memory coupled to the system 100. Selecting a base prompt from among the archived prompts also selects the respective response to the archived prompt as the base response.) (Paragraph 37)
(The user interface 300 may enable the user to archive the current prompt and its corresponding response in response to selecting the archive prompt and response button 370. For example, the archived current prompt and response may be added to the list of archived prompts 356 in the base prompt selection region) (Paragraph 39)
Smith et al. teaches methods surrounding the submission of prompts to an AI model. One of these methods shown in Fig. 3 is the inclusion of archived prompts on the user interface that the user can choose from.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. and De Wynter et al. to allow the user to access prior inputs that have been archived as taught by Smith et al. This would have been an obvious improvement to allow users to view prior inputs again or aid them in comparing how different inputs have produced different results (Smith et al. Paragraph 34).
Regarding Claim 10, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the system of claim 7.
Ochs et al. in view of Shimada et al. and De Wynter et al. does not explicitly teach: further comprising providing a list of saved prompt texts upon activation of a second interactive element of the one or more interactive elements.
However, Smith et al. teaches further comprising providing a list of saved prompt texts upon activation of a second interactive element of the one or more interactive elements.
(The custom base prompt may be any previously submitted prompt which has been stored or archived in a memory coupled to the user interface 300, such as in the database 120 or another memory coupled to the system 100. Selecting a base prompt from among the archived prompts also selects the respective response to the archived prompt as the base response.) (Paragraph 37)
(The user interface 300 may enable the user to archive the current prompt and its corresponding response in response to selecting the archive prompt and response button 370. For example, the archived current prompt and response may be added to the list of archived prompts 356 in the base prompt selection region) (Paragraph 39)
Smith et al. teaches methods surrounding the submission of prompts to an AI model. One of these methods shown in Fig. 3 is the inclusion of archived prompts on the user interface that the user can choose from.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. and De Wynter et al. to allow the user to access prior inputs that have been archived as taught by Smith et al. This would have been an obvious improvement to allow users to view prior inputs again or aid them in comparing how different inputs have produced different results (Smith et al. Paragraph 34).
Regarding Claim 11, Ochs et al. in view of Shimada et al., De Wynter et al., and Smith et al. teaches the system of claim 10.
Furthermore, Smith et al. teaches saving the prompt text to the list of saved prompt texts upon activation of a third interactive element of the one or more interactive elements.
(The user interface 300 may enable the user to archive the current prompt and its corresponding response in response to selecting the archive prompt and response button 370. For example, the archived current prompt and response may be added to the list of archived prompts 356 in the base prompt selection region) (Paragraph 39)
In Smith et al. the archive prompt and response button server as an interactive element to add the prompt to the archive list as seen in Fig.3
Regarding Claim 12, Ochs et al. in view of Shimada et al., De Wynter et al., and Smith et al. teaches the method of claim 11.
Furthermore, Ochs et al. teaches wherein the prompt field is configured to selectively receive a selected prompt text from the list of saved prompt texts, or a manually entered prompt text.
(As described in greater detail below, resume generator 102 may follow an automated process to generate a targeted resume tailored to a specific job description inputted into resume generator 102, based on information supplied in one or more text documents 120.) (Paragraph 28).
Text documents can be manually entered as input to the system of Ochs et al.
Regarding Claim 16, Ochs et al. in view of Shimada et al. and De Wynter et al. teaches the system of claim 13.
Ochs et al. in view of Shimada et al. and De Wynter et al. does not explicitly teach: provide a list of previously saved prompt texts upon activation of a first interactive element of the user interface.
However, Smith et al. teaches provide a list of previously saved prompt texts upon activation of a first interactive element of the user interface.
(The custom base prompt may be any previously submitted prompt which has been stored or archived in a memory coupled to the user interface 300, such as in the database 120 or another memory coupled to the system 100. Selecting a base prompt from among the archived prompts also selects the respective response to the archived prompt as the base response.) (Paragraph 37)
(The user interface 300 may enable the user to archive the current prompt and its corresponding response in response to selecting the archive prompt and response button 370. For example, the archived current prompt and response may be added to the list of archived prompts 356 in the base prompt selection region) (Paragraph 39)
Smith et al. teaches methods surrounding the submission of prompts to an AI model. One of these methods shown in Fig. 3 is the inclusion of archived prompts on the user interface that the user can choose from.
It would have been obvious to a person having ordinary skill in the art before the time of the effective filing date of the claimed invention of the instant application to modify the AI resume building software as taught by Ochs et al. in view of Shimada et al. and De Wynter et al. to allow the user to access prior inputs that have been archived as taught by Smith et al. This would have been an obvious improvement to allow users to view prior inputs again or aid them in comparing how different inputs have produced different results (Smith et al. Paragraph 34).
Regarding Claim 17, Ochs et al. in view of Shimada et al., De Wynter et al., and Smith et al. teaches the system of claim 16.
Furthermore, Smith et al. teaches wherein the one or more processors are further configured to execute computer-executable instructions causing the processing system to save the prompt text to the list of saved prompt texts upon activation of a second interactive element of the user interface.
(In some aspects, a means for receiving the first prompt may include the interface 110, the processor 130 executing instructions stored in the memory 135, the database 120, the bus 170, or the prompt comparison engine 150 of the system 100, or the base prompt selection region 350 or prompt entry region 310 of the user interface 300.) (Paragraph 44).
(The user interface 300 may enable the user to archive the current prompt and its corresponding response in response to selecting the archive prompt and response button 370. For example, the archived current prompt and response may be added to the list of archived prompts 356 in the base prompt selection region) (Paragraph 39).
In Smith et al. the archive prompt and response button server as an interactive element to add the prompt to the archive list as seen in Fig.3
Regarding Claim 18, Ochs et al. in view of Shimada et al., De Wynter et al., and Smith et al. teaches the method of claim 17.
Furthermore, Ochs et al. teaches wherein a prompt field provided on the user interface is configured to selectively receive a selected prompt text from the list of saved prompt texts, and a manually entered prompt text.
(As described in greater detail below, resume generator 102 may follow an automated process to generate a targeted resume tailored to a specific job description inputted into resume generator 102, based on information supplied in one or more text documents 120.) (Paragraph 28).
Text documents can be manually entered as input to the system of Ochs et al.
Conclusion
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.
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/NICHOLAS D LOWEN/Examiner, Art Unit 2653
/Paras D Shah/Supervisory Patent Examiner, Art Unit 2653
05/30/2026
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