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 .
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 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Subject Matter Eligibility Analysis Step 1:
Claims 1-10 recite a process (“A method comprising”), one of the four statutory categories of patentable subject matter. Claims 11-19 recite a machine (“A stateful advice system comprising”), one of the four statutory categories of patentable subject matter. Claim 20 recites an article of manufacture (“A non-transitory computer readable medium comprising instructions to be executed by a stateful advice system”), one of the four statutory categories of patentable subject matter.
Regarding Claim 1:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“determining, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“generating, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Claim 1 therefore recites an abstract idea.
Subject Matter Eligibility Analysis Step 2A Prong 2:
"obtaining, by one or more processors of a stateful advice system, configuration data associated with configuring the stateful advice system to provide stateful advice within a domain” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
"obtaining, by the one or more processors, data indicative of an attribute for a strategy that is associated with the domain” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
"providing, by the one or more processors, the configuration data and the data indicative of the attribute to a generative artificial intelligence (AI) model configured to automatically ” (This step is directed to data gathering, which is understood to be insignificant extra solution activity - see MPEP 2106.05(g))
“by the one or more processors” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is directed to the abstract idea.
Subject Matter Eligibility Analysis Step 2B:
"obtaining, by one or more processors of a stateful advice system, configuration data associated with configuring the stateful advice system to provide stateful advice within a domain” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
"obtaining, by the one or more processors, data indicative of an attribute for a strategy that is associated with the domain” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
"providing, by the one or more processors, the configuration data and the data indicative of the attribute to a generative artificial intelligence (AI) model configured to automatically ” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity and well understood, routine and conventional activity of transmitting and receiving data as identified by the court - see MPEP 2106.05(d))
“by the one or more processors” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
The additional elements as disclosed above alone or in combination do not recite significantly more than the abstract idea itself as they are mere insignificant extra solution activity in combination of generic computer functions being implemented with generic computer elements in a high level of generality to perform the disclosed abstract idea above. Therefore, Claim 1 is subject-matter ineligible.
Regarding Claim 11:
The claim recites a system that performs the method as described in claim 1. Therefore, claim 11 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 11 are analyzed below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“A stateful advice system comprising: a generative artificial intelligence (AI) model; a memory including computer executable instructions; and one or more processors configured to execute the computer executable instructions and cause the one or more processors to perform a method comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 2 and 19:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“concatenating, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
“determining, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“by the one or more processors” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claim 3:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein obtaining the configuration data comprises obtaining, by the one or more processors, the configuration data via a user interface that a user interacts with to input the configuration data” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
Regarding Claims 4 and 12:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“obtaining, by the one or more processors, data indicative of a persona of the user” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“providing, by the one or more processors, the data indicative of the persona of the user as an input to the generative AI model” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
Regarding Claims 5 and 13:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“wherein the content automatically generated by the generative AI model is formatted based, at least in part, on the data indicative of the persona of the user” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B: None
Regarding Claim 6:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“when the data indicative of the persona of the user indicates the user has a first persona, the content automatically generated by the generative AI model has a first format when the persona of the user corresponds to a first persona” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
“when the data indicative of the persona of the user indicates the user has a second persona that is different from the first persona, the content automatically generated by the generative AI model has a second format that is different from the first format” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 7:
Subject Matter Eligibility Analysis Step 2A Prong 1:
“updating, ” (a mental process that can be performed in the human mind with the aid of pen and paper, i.e. judgement)
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“displaying, by the one or more processors and via a user interface of the stateful advice system, the content automatically authored by the generative AI model” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of gathering and analyzing information using conventional techniques and displaying the result as identified by the court (2106.05(d) in step 2B))
“obtaining, by the one or more processors and via the user interface, one or more modifications to the content automatically authored by the generative AI model” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“obtaining, by the one or more processors, updated content as an output of the generative AI model, the updated content incorporating the one or more modifications” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“by the one or more processors” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 8 and 16:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“training, by the one or more processors, the generative AI model based, at least in part, on the one or more modifications to the content” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 9 and 17:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the configuration data includes a prompt that is indicative of whether the stateful advice system is configured to operate in a first mode in which the generative AI model has direct access to the domain model or a second mode in which the generative AI model does not have direct access to the domain model” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 10:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“the domain comprises preparation of tax returns; the strategy comprises a tax savings strategy; and the domain model comprises a tax return populated with live data” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 14:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“wherein the generative AI model comprises a large language model (LLM)” (merely specifies a particular technological environment in which the abstract idea is to take place, ie. a field of use, and thus does not integrate the abstract idea into a practical application nor cannot provide significantly more than the abstract idea itself - see MPEP 2106.05(h))
Regarding Claim 15:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“displaying, on a user interface, the content automatically authored by the generative AI model” (This step is directed to transmitting or receiving information, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of gathering and analyzing information using conventional techniques and displaying the result as identified by the court (2106.05(d) in step 2B))
“obtaining, via the user interface, one or more modifications to the content automatically authored by the generative AI model” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“providing the one or more modifications to the one or more prompts to the generative AI model” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
“obtaining updated content as an output of the generative AI model, the updated content incorporating the one or more modifications” (This step is directed to data gathering, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of transmitting and receiving data as identified by the court (2106.05(d) in step 2B))
Regarding Claim 18:
Subject Matter Eligibility Analysis Step 2A Prong 1: None
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“updating a database storing a plurality of advice artifacts to include the advice artifact for which the generative AI model automatically authored the content” (This step is directed to storing data in memory, which is understood to be insignificant extra solution activity (2106.05(g) in step 2A prong 2) and well understood, routine and conventional activity of storing and retrieving information in memory as identified by the court (2106.05(d) in step 2B))
Regarding Claim 20:
The claim recites an article of manufacture that performs the method as described in claim 1. Therefore, claim 20 is rejected for the same reasons as disclosed for claim 1. The limitations for additional elements of claim 20 are analyzed below.
Subject Matter Eligibility Analysis Step 2A Prong 1:
Please see Step 2A Prong 1 analysis of claim 1
Subject Matter Eligibility Analysis Step 2A Prong 2 & 2B:
“A non-transitory computer readable medium comprising instructions to be executed by a stateful advice system comprising one or more processors, wherein the instructions executed in the one or processors perform a method comprising” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
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.
Claims 1-3, 7-11, and 14-20 are rejected under 35 U.S.C. 103 as being unpatentable over Castro (US11847707B1) in view of Gandhi (US20240378399A1).
Regarding claim 1, Castro teaches:
“A method comprising: obtaining, by one or more processors of a stateful advice system, configuration data associated with configuring the stateful advice system to provide stateful advice within a domain” ([col. 2, lines 20-55; col. 3, lines 19-36], The system includes a computer having a computer processor. The user provides input to the system. The input may include user provided data, rules, and decisions variables. The user data is analyzed with models, algorithms, and sets of rules to identify factual patterns of the input data. The sets of rules (configuration data) help the system to generate a tax solution (advice within a domain).)
“obtaining, by the one or more processors, data indicative of an attribute for a strategy that is associated with the domain” ([abstract; col. 2, lines 56-67; col. 3, lines 1-18], The user provided data may include user tax data, which includes data relating to potential tax liability, potential tax credits, and investments. The system also receives historical data that includes historical judiciary data related to judges and data relating to specific case law. The input data are used to generate a recommended tax filing strategy.)
“providing, by the one or more processors, the configuration data and the data indicative of the attribute to a generative artificial intelligence (AI) model configured to automatically author content for an advice artifact for the strategy, the content ” ([col. 3, lines 37-50; col. 4, lines 26-36; Figure 3], The inputs of the system consist of user provided data and rules. The system identifies factual patterns to generate a tax solution (content for an advice artifact), which may include proposed actions to be taken in the future or include classifying past actions to affect one’s tax liability. The methods proposed may include using AI model such as IBM Watson (generative AI model) to perform the process of generating a tax solution. Castro does not explicitly disclose a plurality of input prompts that may be provided to a generative AI model such as a large language model.)
“determining, by the one or more processors, the strategy is applicable to a domain model associated with the domain based, at least in part, on the content automatically authored by the generative AI model” ([col. 3, lines 37-67], The tax solution depends on the user’s risk tolerance. The system may associate the tax solution with a key identifier. When a user has a low risk tolerance, the tax solution may include conservative aspects. When the user has high risk tolerance, the tax solution may omit the conservative aspects of the solution.)
“generating, by the one or more processors, a recommendation to apply the strategy to the domain model in response to determining the strategy is applicable to the domain model” ([col. 4, lines 20-25], The system generates tax documents based on the tax solution. The system may prepopulate templates using the tax solution or generate a tax opinion based on the tax solution.)
Castro does not explicitly disclose an implementation of “the content including a series of prompts associated with providing stateful advice regarding the strategy”. However, Gandhi discloses in the same field of endeavor:
“providing, by the one or more processors, the configuration data and the data indicative of the attribute to a generative artificial intelligence (AI) model configured to automatically author content for an advice artifact for the strategy, the content including a series of prompts associated with providing stateful advice regarding the strategy” ([0034-0036, 0068], The prompt construction unit includes the natural language query, the DSL samples, the document context, and additional context to generate a prompt for the LLM. The additional context can include rules and/or guidelines information that is added to the input prompt for the LLM to generate an improved output. The LLM generates a DSL program code to perform actions based on the user intent expressed in the natural language query. The grounded textual content generated by the LLM can be provided to the prompt concatenation unit as an input. The prompt concatenation unit can concatenate multiple inputs together into a single prompt for the LLM.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “the content including a series of prompts associated with providing stateful advice regarding the strategy” from Gandhi into the teaching of Castro. Doing so can improve the performance of a model by generating a prompt based on the natural language query from a client device for a LLM to generate the appropriate actions (Gandhi, abstract).
Regarding claim 11:
Claim 11 recites a system that performs the same process as described in Claim 1. Therefore claim 11 is rejected under the same reasons mention for claim 1. The additional elements of claim 11 is addressed below by Castro:
“A stateful advice system comprising: a generative artificial intelligence (AI) model; a memory including computer executable instructions; and one or more processors configured to execute the computer executable instructions and cause the one or more processors to perform a method comprising” ([col. 2, lines 20-38, col. 4, lines 26-36], The system includes a computer that consists of a processor and a computer readable medium that stores instructions. The system may use IBM Watson as the AI model to generate the tax solution.)
Regarding claims 2 and 19, Castro in view of Gandhi teaches:
“concatenating, by the one or more processors, the series of prompts automatically authored by the generative AI model to one or more natural language prompts included in the configuration data to generate a concatenated prompt” ([Gandhi, 0068], The knowledge-grounded prompt is used to generate factually grounded content that will be included in the DSL program code that is to be generated by the LLM. Thus, the grounded textual content generated by the LLM is combined with the DSL program code output. Also, the prompt concatenation unit combines a plurality of information into a prompt for the LLM.)
“determining, by the one or more processors, the strategy is applicable to the domain model based, at least in part, on the concatenated prompt” ([Gandhi, 0037-0038], The DSL program code generated by the LLM is provided to the content moderation services to analyze the DSL to ensure it does not include potentially offensive or objectionable material. Offensive material is not part of the domain and the system will send a blocked content notification to the client-side interface.)
Regarding claim 3, Castro teaches:
“wherein obtaining the configuration data comprises obtaining, by the one or more processors, the configuration data via a user interface that a user interacts with to input the configuration data” ([col. 2, lines 39-55], The user provides input to the system via the GUI. The input may include user provided data, rules, and decisions variables. Thus, the set of rules can be provided by the user as input via the GUI.)
Regarding claim 7, Castro in view of Gandhi teaches:
“displaying, by the one or more processors and via a user interface of the stateful advice system, the content automatically authored by the generative AI model” ([Castro, col. 4, lines 9-30], The system generates and display the tax solution via the GUI for the user. The system uses a generative AI model to generate the tax solution.)
“obtaining, by the one or more processors and via the user interface, one or more modifications to the content automatically authored by the generative AI model” ([Castro, col. 6, lines 19-37], After the tax strategy is applied to the user’s tax return, the system identifies future tax planning opportunities and makes recommendations (modifications) to the user.)
“updating, by the one or more processors, one or more prompts in the series of prompts based, at least in part, on the one or more modifications” ([Castro, col. 6, lines 38-55], The system implements a feedback loop that allows the machine learning model to update itself. It is implied that the inputs of the model would be updated to produce improved tax strategies yielding greater returns for the user. Gandhi (par. 49) further discloses that the user interface provides information indicating why the blocked content notification was issued and the user may refine the natural language query in response to the notification.)
“obtaining, by the one or more processors, updated content as an output of the generative AI model, the updated content incorporating the one or more modifications” ([Castro, col. 6, lines 38-55], The system implements a feedback loop that allows the machine learning model to update itself based on recommendations generated with the use of AI and produce improved tax strategies yielding greater returns for the user.)
Regarding claims 8 and 16, Castro teaches:
“training, by the one or more processors, the generative AI model based, at least in part, on the one or more modifications to the content” ([col. 5, lines 1-9, col. 6, lines 19-37], The system is trained using a plurality of factual scenarios from case law. The system identifies future tax planning opportunities and makes recommendations to the user. In some embodiment, the machine learning model identifies particular law and factual elements that may have been overlooked and incorporate those to train the model to produce improved tax strategies.)
Regarding claims 9 and 17, Castro in view of Gandhi teaches:
“wherein the configuration data includes a prompt that is indicative of whether the stateful advice system is configured to operate in a first mode in which the generative AI model has direct access to the domain model or a second mode in which the generative AI model does not have direct access to the domain model” ([Castro, col. 2, lines 56-67, col. 3, lines 1-6, col. 6, lines 28-37], In one embodiment, the system receives user tax data to generate the user tax return and the system does not have direct access to the user tax return. In another embodiment, the system identifies prospective tax planning opportunities based on the completed return and comparing the current year tax return with prior year tax returns. Thus, the system may be provided with the user’s previous tax returns as an input. In addition, Gandhi (par 30-31) discloses an optional document content may be provided as an input to the LLM to modify existing electronic content.)
Regarding claim 10, Castro teaches:
“the domain comprises preparation of tax returns” ([col. 6, lines 4-15], The system is trained to generate user tax return.)
“the strategy comprises a tax savings strategy” ([col. 6, lines 19-23], The system identifies the most applicable tax strategy for the user’s specific factual situation.)
“the domain model comprises a tax return populated with live data” ([col. 6, lines 19-37], The system applies the tax strategy to the user’s tax return and generates the user tax return.)
Regarding claim 14, Castro in view of Gandhi teaches:
“wherein the generative AI model comprises a large language model (LLM)” ([Gandhi, 0034], The system consists of a LLM as the generative model.)
Regarding claim 15, Castro in view of Gandhi teaches:
“displaying, on a user interface, the content automatically authored by the generative AI model” ([Castro, col. 4, lines 9-30], The system generates and display the tax solution via the GUI for the user. The system uses a generative AI model to generate the tax solution.)
“obtaining, via the user interface, one or more modifications to the content automatically authored by the generative AI model” ([Castro, col. 6, lines 19-37], After the tax strategy is applied to the user’s tax return, the system identifies future tax planning opportunities and makes recommendations (modifications) to the user.)
“providing the one or more modifications to the one or more prompts to the generative AI model” ([Castro, col. 6, lines 38-55], The system implements a feedback loop that allows the machine learning model to update itself. It is implied that the inputs of the model would be updated to produce improved tax strategies yielding greater returns for the user. Gandhi (par. 49) further discloses that the user interface provides information indicating why the blocked content notification was issued and the user may refine the natural language query to be resubmitted to the LLM.)
“obtaining updated content as an output of the generative AI model, the updated content incorporating the one or more modifications” ([Castro, col. 6, lines 38-55], The system implements a feedback loop that allows the machine learning model to update itself based on recommendations generated with the use of AI and produce improved tax strategies yielding greater returns for the user.)
Regarding claim 18, Castro teaches:
“updating a database storing a plurality of advice artifacts to include the advice artifact for which the generative AI model automatically authored the content” ([col. 3, lines 37-67, col. 4, lines 1-19], The system generates a tax solution and identifies relevant videos (advice artifacts) from the video library based on the tax solution. The system can compile the relevant videos into a compilation video that is related to the generated tax solution for the user. The system stores the compilation video and associates the compilation video with the user.)
Regarding claim 20:
Claim 20 recites an article of manufacture that performs the same process as described in Claim 1. Therefore claim 20 is rejected under the same reasons mention for claim 1. The additional elements of claim 20 is addressed below by Castro:
“A non-transitory computer readable medium comprising instructions to be executed by a stateful advice system comprising one or more processors, wherein the instructions executed in the one or processors perform a method comprising” ([col. 2, lines 20-38], The system includes a computer that consists of a processor and a computer readable medium that stores instructions.)
Claims 4-6 and 12-13 are rejected under 35 U.S.C. 103 as being unpatentable over Castro (US11847707B1) in view of Gandhi (US20240378399A1) and White, “A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT”.
Regarding claims 4 and 12, Castro in view of Gandhi teaches:
“obtaining, by the one or more processors, data indicative of a ” ([Castro, col. 2, lines 48-67], The user provided data can include user data related to a psychological profile of the user that is used in risk tolerance evaluation of the user.)
“providing, by the one or more processors, the data indicative of the ” ([Castro, col. 2, lines 39-67], The user provided data can include user data related to a psychological profile of the user that is used in risk tolerance evaluation of the user. The input is provided to the AI model to identify factual patterns in the inputs to generate a tax solution.)
Castro in view of Gandhi does not explicitly disclose an implementation of “data indicative of a persona of the user”. However, White discloses in the same field of endeavor:
“obtaining, by the one or more processors, data indicative of a persona of the user” ([pg. 7-8, Section E, par. 1-8], A prompt can be generated for a LLM based on the intention that user would like the output of the LLM to take a certain perspective. An input instruction can be provided to the LLM to prompt the LLM to evaluate the code as a security reviewer.)
It would be obvious to one of the ordinary skill in the art before the effective filing date of the claimed invention to incorporate the teaching of “data indicative of a persona of the user” from White into the teaching of Castro in view of Gandhi. Doing so can improve the performance of a LLM by implementing prompt engineering to generate instructions to a LLM based on rules and specific qualities of the generated output (White, abstract).
Regarding claims 5 and 13, Castro in view of Gandhi and White teaches:
“wherein the content automatically generated by the generative AI model is formatted based, at least in part, on the data indicative of the persona of the user” ([White, pg. 7, Section E, par. 1-4], The intent of the persona pattern is to help the LLM select what type of output to generate and what details to focus on by providing a persona prompt. For example, a persona of a teacher may guide the LLM to output assignments, reading lists, and lectures.)
Regarding claim 6, Castro in view of Gandhi and White teaches:
“when the data indicative of the persona of the user indicates the user has a first persona, the content automatically generated by the generative AI model has a first format when the persona of the user corresponds to a first persona” ([White, pg. 7, Section E, par. 1-4], The intent of the persona pattern is to help the LLM select what type of output to generate and what details to focus on by providing a persona prompt. For example, a persona of a teacher may guide the LLM to output assignments, reading lists, and lectures.)
“when the data indicative of the persona of the user indicates the user has a second persona that is different from the first persona, the content automatically generated by the generative AI model has a second format that is different from the first format” ([White, pg. 7-8, Section E, par. 1-8], An input instruction can be provided to the LLM to prompt the LLM to evaluate the code as a security reviewer. The LLM would generate output that a “security reviewer” would regarding the code. This output would be different from the output of the LLM if the persona was a teacher because the represent user from different domains.)
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to GARY MAC whose telephone number is (703)756-1517. The examiner can normally be reached Monday - Friday 8:00 AM - 5:00 PM.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Abdullah Kawsar can be reached at (571) 270-3169. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
/GARY MAC/Examiner, Art Unit 2127
/JEREMY L STANLEY/Examiner, Art Unit 2127
Prosecution Insights