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 .
Drawings
The drawings filed on: 06/28/2024 are accepted.
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claim 11 is rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
With regards to claim 11, it recites “… one or more parameters indicative of confidential information of the employee”, which is indefinite as it is subjective how confidential information is assessed/identified. For purposes of examination the examiner interprets this limitation to be a descriptive /nonfunctional aspect of information and any information of the employee is sufficient to correspond to the limitation’s scope. If the applicant is requiring that the parameters include confidential identifiers or there is a particular method to assess whether information is identified as ‘confidential’, then the examiner suggests the applicant consider clarifying the claim language in such a manner to help resolve this issue.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claim(s) 1-8 and 12-20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Muralidharan et al (US Application: US 2024/0273282, published: Aug. 15, 2024, filed: Feb. 15, 2023) in view of Nahum et al (US Application: US 20240403545, published: Dec. 5, 2024, filed: 03/ 20/ 2024, provisional 63/471129 with EEFD of : June, 5, 2023).
With regards to claim 1. Muralidharan et al teaches a system comprising:
one or more processors, coupled with memory, to (Fig. 23: processor and memory is implemented in the system):
identify one or more placeholders for one or more parameters of a [document] (paragraph 0112, 0113, Table 1: placeholders for parameters are identified to generate/populate a document/article);
identify a sample of a template for the [document] (paragraphs 0112 and 0114, Table 1: a sample/instance of a rules-configuration/template is identified as a sample template to define the output and output format/type);
generate, for a generative artificial intelligence (GAI) model, a prompt indicating the one or more placeholders for the one or more parameters, the sample and an instruction to condition a probability distribution of the GAI model to cause the GAI model to generate [instructions/logic] for providing the [document] that arranges the placeholders in accordance with the template (paragraphs 0057, 0112-0116, 0174, 0194, Table 1: a prompt is generated from the prompt configuration, the prompt having parameter values , the sample (‘Example’) parts /sections of the article also having placeholder markup that conveys placement/arrangement of content associated with the placeholder markup, and instructions for the article/sections. The seed is used as context to fine tune (condition probability distribution) the model to generate and provide output in accordance with the template data); and
receive, from the GAI model, [instructions/logic] providing the [document] comprising the placeholders for the one or more parameters arranged in accordance with the template (paragraphs 00117, 0128: the document output received from the generative model).
However Muralidharan et al does not expressly teach … cause the GAI model to generate a computer code for providing the form … ; receive, from the GAI mode, the computer code for providing the form …
Yet Nahum teaches … cause the GAI model to generate a computer code for providing the form … ; receive, from the GAI mode, the computer code for providing the form comprising the placeholders for the one or more parameters arranged in accordance with the template (paragraph 0028, 0029, 0031, 0036, 0052, 0060 of provisional application (Fig 2 of non-provisional): an interactive form is generated using a generative model based upon input query/tasks corresponding to input from a chatbot AI assistance application/user-interface, where the form includes placeholders (fields) for the parameters and is programmed/instructed (coded) to collect user input/data with additional optional functional capabilities and can be further coded with structural layout data in a web page).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Muralidharan et al’s ability to produce output based upon generated placeholder and parameter data associated with input as prompt(s) to the GAI model, such that generated placeholder/parameter data are associated with form fields to obtain form output that can be populated using the generative model and presented to the user based upon input of a chat application interface, as taught by Nahum. The combination would have implemented an enhanced/efficient way to help users perform tasks.
With regards to claim 2. The system of claim 1, the combination of Muralidharan et al and Nahum teaches further comprising the one or more processors to: receive, by a chatbot interface, a query; and generate, using an AI assistant and based on the query, the one or more placeholders and the sample of the template for the GAI model, as similarly explained in the rejection of claim 1 (as explained an AI assistant is implemented to process user input/task/query for placeholder(s) in correspondence with prompt template(s) and sent to a model for form output), and is rejected under similar rationale.
With regards to claim 3. The system of claim 2, the combination of Muralidharan et al and Nahum teaches further comprising the one or more processors to: receive, using the AI assistant ( as similarly explained in the rejection of claim 2 an AI chatbot assistant is implemented to receive user input/task/query to provide to a generative model), and is rejected under similar rationale.
However the combination as explained in the rejection of claim 2 does not expressly teach receiving … an identifier of an API function of a plurality of API functions; and generate, by executing the API function using the one or more parameters, an API response.
Yet Nahum further teaches receiving … an identifier of an API function of a plurality of API functions; and generate, by executing the API function using the one or more parameters, an API response (paragraphs 0007, 0051: user parameter data is used as part of an API call to effectuate the generated output response)
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Muralidharan et al and Nahum’s ability to receive input and parameter data with an AI assistant/application (to invoke using prompt(s) through use of a generative model, generation of code to produce a form), such that the application identifies/invokes an API using the parameters to have the model produce the form output, as also taught by Nahum. The combination would have allowed Muralidharan et al and Nahum to have flexibly and efficiently allowed task performance on a variety of digital platforms (Nahum, paragraph 0007).
With regards to claim 4. The system of claim 3, the combination of Muralidharan et al and Nahum teaches further comprising the one or more processors to: receive, from the GAI model, the computer code responsive to the API response and the prompt input into the GAI model, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
With regards to claim 5. The system of claim 1, the combination of Muralidharan et al and Nahum teaches wherein the instruction of the prompt defines a context of the GAI model to adjust the probability distribution of the GAI model to cause the GAI model to generate the computer code according to content of the prompt, as similarly explained in the rejection of claim 1 (Paragraphs 0057, 0113-0116, 0174, 0194, Table 1 of Muralidharan et al was explained to teach a prompt is generated from the prompt configuration, the prompt having parameter values , the sample (‘Example’) parts /sections of the article also having placeholder markup that conveys placement/arrangement of content associated with the placeholder markup, and instructions for the article/sections. The seed is used as context to fine tune (condition probability distribution) the model to generate and provide output in accordance with the template data. Also Muralidharan et al’s model output was modified by Nahum, such that the output further included a coding/instruction to provide a form specific document output as explained by Nahum (paragraph 0028, 0029, 0031, 0036, 0052, 0060 of provisional application (Fig 2 of non-provisional): an interactive form is generated using a generative model based upon input query/tasks corresponding to input from a chatbot AI assistance application/user-interface, where the form includes placeholders (fields) for the parameters and is programmed/instructed (coded) to collect user input/data with additional optional functional capabilities and can be further coded with structural layout data in a web page)), and is rejected under similar rationale.
With regards to claim 6. The system of claim 1 , the combination of Muralidharan et al and Nahum teaches further comprising the one or more processors to generate the form according to the computer code, as similarly explained in the rejection of claim 1 (Muralidharan et al’s document output was modified to output a coded structured form paragraph 0028, 0029, 0031, 0036, 0052, 0060 of provisional for Nahum)), and is rejected under similar rationale.
With regards to claim 7. The system of claim 1, the combination of Muralidharan et al and Nahum teaches further comprising wherein the prompt includes a set of requirements listing the one or more placeholders and for each of the one or more placeholders identifying a corresponding parameter of the one or more parameters (paragraphs 0112 and 0113, Table 1 of Mauralidharan et al: (‘Example’) area in the prompt configuration specifies a list of parts /sections of the article that have placeholder markup that conveys placement/arrangement of content).
With regards to claim 8. The system of claim 1, the combination of Muralidharan et al and Nahum teaches further comprising wherein the prompt is configured to set the probability distribution of the GAI model to cause the GAI model to generate, in the computer code, a series of instructions to define the form, as similarly explained in the rejection of claim 1 (Muralidharan et al’s document output that was generated was modified with the teachings of Nahum, such that the generation would further output a specific type of coded form-type document output , such that the coding defines the structure of form layout), and is rejected under similar rationale.
With regards to claim 12, the combination of Muralidharan et al and Nahum teaches a method comprising: identifying, by one or more processors coupled with memory, one or more placeholders for one or more parameters of a form; identifying, by the one or more processors, sample of a template for the form; generating, by the one or more processors for a generative artificial intelligence (GAI) model, a prompt indicating the one or more placeholders for the one or more parameters, the sample and an instruction to condition a probability distribution of the GAI model to cause the GAI model to generate a computer code for providing the form that arranges the placeholders in accordance with the template; and receiving, by the one or more processors from the GAI model, the computer code for providing the form comprising the placeholders for the one or more parameters arranged in accordance with the template, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
With regards to claim 13. The method of claim 12, the combination of Muralidharan et al and Nahum teaches comprising: receiving, by the one or more processors, via a chatbot interface for the GAI model, a query; and generating, by the one or more processors using an AI assistant for the GAI model and based on the query, the one or more placeholders and the sample of the template for the GAI model, as similarly explained in the rejection of claim 2, and is rejected under similar rationale.
With regards to claim 14. The method of claim 13, the combination of Muralidharan et al and Nahum teaches comprising: receiving, by the one or more processors, using the AI assistant for the GAI model, identifier of an API function of a plurality of API functions; and generating, by the one or more processors, by executing the API function using the one or more parameters, an API response, as similarly explained in the rejection of claim 3, and is rejected under similar rationale.
With regards to claim 15. The method of claim 14, the combination of Muralidharan et al and Nahum teaches comprising: receiving, by the one or more processors, from the GAI model, the computer code responsive to the API response and the prompt input into the GAI model, as similarly explained in the rejection of claim 4, and is rejected under similar rationale.
With regards to claim 16. The method of claim 12, the combination of Muralidharan et al and Nahum teaches comprising: defining, by the one or more processors in the instruction of the prompt, a context of the GAI model by adjusting the probability distribution to cause the GAI model to generate the computer code, as similarly explained in the rejection of claim 5, and is rejected under similar rationale.
With regards to claim 17. The method of claim 12, the combination of Muralidharan et al and Nahum teaches comprising: generating, by the one or more processors, the form according to the computer code, as similarly explained in the rejection of claim 6, and is rejected under similar rationale.
With regards to claim 18. The method of claim 12, the combination of Muralidharan et al and Nahum teaches comprising: including, by the one or more processors within the prompt, a set of requirements listing the one or more placeholders; and identifying, by the one or more processors for each of the one or more placeholders, a corresponding parameter of the one or more parameters , as similarly explained in the rejection of claim 7, and is rejected under similar rationale.
With regards to claim 19. The method of claim 12, the combination of Muralidharan et al and Nahum teaches wherein the prompt is configured to set the probability distribution of the GAI model to cause the GAI model to generate, in the computer code, a series of instructions to define the form, as similarly explained in the rejection of claim 8, and is rejected under similar rationale.
With regards to claim 20, the combination of Muralidharan et al and Nahum teaches a non-transitory computer readable medium storing program instructions for causing at least one processor to: identify one or more placeholders for one or more parameters of a form; identify a sample of a template for the form; generate, for a generative artificial intelligence (GAI) model, a prompt indicating the one or more placeholders for the one or more parameters, the sample and an instruction to condition a probability distribution of the GAI model to cause the GAI model to generate a computer code for providing the form that arranges the placeholders in accordance with the template; and receive, from the GAI model, the computer code for providing the form comprising the placeholders for the one or more parameters arranged in accordance with the template, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
Claim(s) 9 and 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Muralidharan et al (US Application: US 2024/0273282, published: Aug. 15, 2024, filed: Feb. 15, 2023) in view of Nahum et al (US Application: US 20240403545, published: Dec. 5, 2024, filed: 03/ 20/ 2024, provisional 63/471129 with EEFD of : June, 5, 2023) in view of Maschmeyer et al (US Patent: 12561512, issued: Feb. 24, 2026, filed: Oct. 20, 2023, provisional/EEFD: May 12, 2023).
With regards to claim 9. The system of claim 1, Muralidharan et al and Nahum et al teaches wherein the computer code includes one or more computer instructions, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination does not expressly teach … keywords or parameters implemented using at least one computer language including: Goland (Go) programing language, hypertext markup language (HTML), cascading style sheets (CSS), JavaScript language, Python programming language, Ruby programming language, Java programming language, C sharp (C#) programming language and PHP: Hypertext Processor (PHP) scripting language.
Yet Maschmeyer et al teaches … keywords or parameters implemented using at least one computer language including: Goland (Go) programing language, hypertext markup language (HTML), cascading style sheets (CSS), JavaScript language, Python programming language, Ruby programming language, Java programming language, C sharp (C#) programming language and PHP: Hypertext Processor (PHP) scripting language (column 14, lines 45-53, column 18, lines 50-67, column 20, lines 53-67: prompt generator identifies/takes HTML as parameter input and provides coded output in HTML formatted computer language, using a model).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Muralidharan et al and Nahum et al’s ability to produce instructions/code to produce a form/structured form based on parameters, such that the parameters and structured output could have been in a computer language such as HTML, as taught by Maschmeyer et al. The combination would have allowed efficiently and flexibly processed formatted input to generate desired formatted output.
With regards to claim 10. The system of claim 1, the combination of Muralidharan et al, Nahum et al and Maschmeyer et al teaches wherein the prompt identifies a computer language for the computer code to cause the GAI model to provide the computer code in the identified computer language, as similarly explained in the rejection of claim 9, and is rejected under similar rationale.
Claim(s) 11 is/are rejected under 35 U.S.C. 103 as being unpatentable over Muralidharan et al (US Application: US 2024/0273282, published: Aug. 15, 2024, filed: Feb. 15, 2023) in view of Nahum et al (US Application: US 20240403545, published: Dec. 5, 2024, filed: 03/ 20/ 2024, provisional 63/471129 with EEFD of : June, 5, 2023) in view of Nahum et al (2) (US Patent: 10819664, issued: Oct. 27, 2020, filed: Mar. 19, 2019).
With regards to claim 11. The system of claim 1, the combination of Muralidharan et al and Nahum teaches wherein the prompt identifies the one or more placeholders for the one or more parameters, as similarly explained in the rejection of claim 1, and is rejected under similar rationale.
However the combination explained in the rejection of claim 1 did not address the one or more parameters of an employee of the enterprise, the one or more parameters indicative of confidential information of the employee.
Yet Nahum et al (2) teaches the one or more parameters of an employee of the enterprise, the one or more parameters indicative of confidential information of the employee (Abstract, Fig 2C, Fig. 5B, Fig. 6: one or more parameters obtained are associated with employee information that could be considered ‘confidential’ such as employee ID, reporting manager, employment status).
It would have been obvious to one of ordinary skill in the art before the effective filing of the invention to have modified Muralidharan et al and Nahum’s ability to identify and process placeholders for parameters based upon collected input, such that the parameters associated with the input could have been associated with an employee with confidential type information as taught by Nahum et al (2). The combination would have allowed efficient implementation of an automation process through automatic invocation by a natural language interface and using one or more values provided by the user to the natural language interface for the one or more parameters (Nahum et al (2), Abstract)
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
Lange et al (US Application: US 20220050661): This reference teaches analyzing graphical user interface to facilitate assistant implemented-automatic interaction with GUIs.
Moon (US Application: US 20210157618): This reference teaches executing an action in an application using information associated with a chat interface.
Iu et al (US Application: US 20240273286): This reference teaches generating by a generative language model, a version of a document.
Goligorsky (US Patent: 12608548): This reference teaches parsing features and instructions into a prompt.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to WILSON W TSUI whose telephone number is (571)272-7596. The examiner can normally be reached Monday - Friday 9 am -6 pm.
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/WILSON W TSUI/Primary Examiner, Art Unit 2172
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