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 .
DETAILED ACTION
The Action is responsive to the Application filed on 6/27/2024. Claims 1-20 are pending claims. Claims 1, 19, and 20 are written in independent form.
Priority
Applicant's claim for benefit of prior-filed provisional application 63/523,588 under 35. U.S.C. 119(e) or under 35 U.S.C. 120, 121, or 365(c) is acknowledged.
Claim Objections
Claims 1, 19, and 20 are objected to because of the following informalities:
Claims 1, 19, and 20 recite a typographical error of “functionality using a least a portion of the structured data of the base”. The language is understood as reciting “functionality using [[a]] at least a portion of the structured data of the base”.
Claims 1, 19, and 20 recite a typographical error of “the portion of the structured data used in the natural language request,”. The language is understood as reciting “the portion of the structured data used in the natural language request;[[,]]”.
Appropriate correction is required.
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 non-patentable subject matter. The claimed invention is directed to one or more abstract ideas without significantly more. The judicial exception is not integrated into a practical application. The claims do not include additional elements that are sufficient to amount to significantly more than judicial exception. The eligibility analysis in support of these findings is provided below.
As per Claim 1,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed method (claims 1-18), system (claim 19), and non-transitory computer readable storage medium (claim 20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The independent claims 1, 19, and 20 recite the following limitations directed to an abstract idea:
Determining a prompt for a large language model to generate the set of data providing the functionality,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating an LLM and a desired data providing functionality, and based on the observation and evaluation, making a judgement and/or opinion of a prompt for the LLM to generate a set of data providing the functionality.
STEP 2A Prong Two:Claim 1 recites that the steps are performed using “a computing device”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Claim 19 recites that the steps are performed using “one or more processors”, “a non-transitory computer readable storage medium”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
Claim 20 recites that the steps are performed using “a non-transitory computer readable storage medium”, “one or more processors” and “a computing device”, which is a high-level recitation of generic computer components and represents mere instructions to apply on a computer as in MPEP 2106.05(f), which does not provide integration into a practical application.
The claim recites the following additional elements:
Receiving a request to generate a set of data providing a functionality in a base comprising structured data,
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
the request comprising a natural language request for the functionality using at least a portion of the structured data of the base;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data comprised in the request as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
wherein the prompt comprising a representation of:
the natural language request for the functionality;
the portion of the structured data used in the natural language request,
a structure of the base;
structural relationships in the base; and
data types of the structured data in the base;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data represented in the prompt as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
in response to transmitting the prompt to the large language model, receiving the set of data providing the functionality from the large language model; and
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
transmitting the set of data for display as structured data in the base.
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to “Receiving a request to generate a set of data providing a functionality in a base comprising structured data,” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “the request comprising a natural language request for the functionality using at least a portion of the structured data of the base;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “wherein the prompt comprising a representation of: the natural language request for the functionality; the portion of the structured data used in the natural language request; a structure of the base; structural relationships in the base; and data types of the structured data in the base;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to “in response to transmitting the prompt to the large language model, receiving the set of data providing the functionality from the large language model;” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
With respect to “transmitting the set of data for display as structured data in the base.” identified as insignificant extra-solution activity above this is also WURC as court-identified see MPEP 2106.05(d)(II)(i).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
As per Dependent Claims 2-18,
STEP 1:In accordance with Step 1 of the eligibility inquiry (as explained in MPEP 2106), the claimed method (claims 1-18), system (claim 19), and non-transitory computer readable storage medium (claim 20) are directed to one of the eligible categories of subject matter and therefore satisfies Step 1.
STEP 2A Prong One:The dependent claims 2-18 recite the following limitations directed to an abstract idea:
The limitation of Dependent Claim 2 includes the step(s) of:
determining, using the prompt at a network system hosting the large language model, the set of data providing the functionality,
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the prompt, and based on the observation and evaluation, making a judgement and/or opinion of the set of data providing the functionality.
the large language model configured to:
interpret the natural language request for the functionality using at least the structured data,
identify contextual relationships between the functionality and one or more of the structure of the structured data, the structural relationships of data in the structured data, and data types of the structured data, and
determine the set of data providing the functionality based on the contextual relationships.
The limitation recites a mathematical concept of executing a mathematical function that is configured to take as input the prompt comprising a representation of the natural language request for the functionality, the portion of the structured data used in the natural language request, a structure of the base, structural relationships in the base, and data types of the structured data in the base, interprets the natural language request, identifies contextual relationships, and determines the set of data providing the functionality.
The limitation of Dependent Claim 3 includes the step(s) of:
receiving, by the computing device, a selection of the large language model from a plurality of large language models, wherein the plurality of large language models is provided to a client device generating the prompt; and
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the plurality of large language models, and based on the observation and evaluation, making a judgement and/or opinion of a selection of the large language model for which the prompt is to be generated for.
wherein generating the prompt for the large language model accounts for a configuration of the selected large language model.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating at least a configuration of the selected large language model, and based at least on observation and evaluation, making a judgement and/or opinion of a prompt for the large language model.
The limitation of Dependent Claim 5 includes the step(s) of:
generating, at the computing device, a flag for the set of data as manipulated data based on the edit;
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating the set of data manipulated based on the edit, and based on the observation and evaluation, making a judgement and/or opinion to flag or tag the set of data as manipulated data based on the edit.
responsive to receiving an additional request to modify the set of data, determining an additional prompt and modifying the set of data based on the generated flag.
The limitation recites a mental process of observation, evaluation, judgement, and/or opinion capable of being performed by the human mind, or by a human using a pen and paper, by observing and evaluating an additional request to modify the set of data and the flag, and based on the observation and evaluation, making a judgement and/or opinion of an additional prompt and carrying out the modification of the set of data.
STEP 2A Prong Two:The claim(s) recite the following additional elements:
The limitation of Dependent Claim 4 includes the step(s) of:
wherein the natural language request comprises one or more data objects representing the portion of the structured data of the base.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the natural language request as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 5 includes the step(s) of:
receiving, at the computing device, an edit to the set of data displayed as structured data of the base;
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 6 includes the step(s) of:
wherein the structure of the structured data comprises one or more of:
a plurality of elements in the base, each element comprising structured data;
a set of rows of the base, the set of rows comprising one or more of the plurality of elements;
a set of fields in the base, the set of fields comprising one or more of the plurality of elements;
a label for each element of the plurality of elements, each row of the set of rows, and each field of the set of fields; and
a size of the base.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the structure of the structured data as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 7 includes the step(s) of:
wherein the base comprises a plurality of elements in a set of fields and a set of rows, and
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the base as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
the structural relationships of data in the structured data comprise one or more of:
a dependency of a first field in the set of fields on a second field in the set of fields;
a dependency of a first row in the set of rows on a second row in the set of rows;
a dependency of a first element of the plurality of elements on a second element of the plurality of elements; and
one or more logical functions governing dependencies in the structural data.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the structural relationships of data in the structured data as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 8 includes the step(s) of:
wherein the prompt comprises a relationship between the base and one or more additional bases;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the prompt as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
wherein each of the one or more additional bases depend on structured data of the base.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the relationship/dependency between bases and the structured data of the base as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 9 includes the step(s) of:
wherein the set of data is propagated to the one or more additional bases that depend on the structured data of the base.
The limitation recites an insignificant extra solution activity as sending/receiving/propagating data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 10 includes the step(s) of:
wherein the prompt comprises a representation of a relationship between the base and one or more additional bases;
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the prompt as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
wherein the structured data of the base depend on structured data of the one or more additional bases.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the relationship/dependency between the structured data of the base and the structured data of the one or more additional bases as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 11 includes the step(s) of:
wherein the set of data is propagated to the one or more additional bases that depend on the structured data of the base.
The limitation recites an insignificant extra solution activity as sending/receiving/propagating data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 12 includes the step(s) of:
the base comprises a field, and
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the base as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
the request to generate the set of data providing the functionality in the base is received as input to the field; and
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
the generated prompt is associated with the field.
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the prompt as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 13 includes the step(s) of:
wherein the received set of data is displayed in the field before being displayed as structured data in the base.
The limitation recites an additional element that does not amount to significantly more because it
recites sending or displaying data and MPEP 2106.05(d) states that performing basic computer
functions such as sending and receiving data as being well-understood, routine, and conventional
activity.
The limitation of Dependent Claim 14 includes the step(s) of:
wherein the base comprises a data generation assistant function, and
The limitation recites an insignificant extra-solution activity as selecting a particular type of data being used to represent the base as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
the request to generate the set of data providing the functionality in the base is received at the data generation assistant.
The limitation recites an insignificant extra solution activity as sending/receiving data (ie. Mere data gathering) such as ‘obtaining information’ as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 15 includes the step(s) of:
wherein the received set of data is displayed by the data generation assistant function before being displayed as structured data in the base.
The limitation recites an additional element that does not amount to significantly more because it
recites sending or displaying data and MPEP 2106.05(d) states that performing basic computer
functions such as sending and receiving data as being well-understood, routine, and conventional
activity.
The limitation of Dependent Claim 16 includes the step(s) of:
wherein the functionality is categorizing data input into the database.
The limitation recites an insignificant extra-solution activity as selecting a particular type of functionality being used to represent the functionality as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 17 includes the step(s) of:
wherein the functionality is generating a function that manipulates a first portion of the structured data in the base based on a second portion of the structured data in the base.
The limitation recites an insignificant extra-solution activity as selecting a particular type of functionality being used to represent the functionality as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
The limitation of Dependent Claim 18 includes the step(s) of:
wherein the functionality is translating a first portion of the structured data in the base.
The limitation recites an insignificant extra-solution activity as selecting a particular type of functionality being used to represent the functionality as identified in MPEP 2106.05(g) and does not provide integration into a practical application.
Viewing the additional limitations together and the claim as a whole, nothing provides integration into a practical application.
STEP 2B:
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
With respect to Claim 4 reciting “wherein the natural language request comprises one or more data objects representing the portion of the structured data of the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 5 reciting “receiving, at the computing device, an edit to the set of data displayed as structured data of the base;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 6 reciting “wherein the structure of the structured data comprises one or more of: a plurality of elements in the base, each element comprising structured data; a set of rows of the base, the set of rows comprising one or more of the plurality of elements; a set of fields in the base, the set of fields comprising one or more of the plurality of elements; a label for each element of the plurality of elements, each row of the set of rows, and each field of the set of fields; and a size of the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 7 reciting “wherein the base comprises a plurality of elements in a set of fields and a set of rows,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 7 reciting “the structural relationships of data in the structured data comprise one or more of: a dependency of a first field in the set of fields on a second field in the set of fields; a dependency of a first row in the set of rows on a second row in the set of rows; a dependency of a first element of the plurality of elements on a second element of the plurality of elements; and one or more logical functions governing dependencies in the structural data.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 8 reciting “wherein the prompt comprises a relationship between the base and one or more additional bases;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 8 reciting “wherein each of the one or more additional bases depend on structured data of the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 9 reciting “wherein the set of data is propagated to the one or more additional bases that depend on the structured data of the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 10 reciting “wherein the prompt comprises a representation of a relationship between the base and one or more additional bases;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 10 reciting “wherein the structured data of the base depend on structured data of the one or more additional bases.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 11 reciting “wherein the set of data is propagated to the one or more additional bases that depend on the structured data of the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 12 reciting “the base comprises a field,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 12 reciting “the request to generate the set of data providing the functionality in the base is received as input to the field;” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 12 reciting “the generated prompt is associated with the field.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 13 reciting “wherein the received set of data is displayed in the field before being displayed as structured data in the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 14 reciting “wherein the base comprises a data generation assistant function,” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 14 reciting “the request to generate the set of data providing the functionality in the base is received at the data generation assistant.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 15 reciting “wherein the received set of data is displayed by the data generation assistant function before being displayed as structured data in the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(i).
With respect to Claim 16 reciting “wherein the functionality is categorizing data input into the database.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 17 reciting “wherein the functionality is generating a function that manipulates a first portion of the structured data in the base based on a second portion of the structured data in the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
With respect to Claim 18 reciting “wherein the functionality is translating a first portion of the structured data in the base.” identified as insignificant extra-solution activity above this is also WURC when claimed in a merely generic manner as court-identified see MPEP 2106.05(d)(II)(iv).
Looking at the claim as a whole does not change this conclusion and the claim is ineligible.
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) 1-20 are rejected under 35 U.S.C. 103 as being unpatentable over Clement et al. (U.S. Pre-Grant Publication No. 2024/0419917, hereinafter referred to as Clement) and further in view of Sethi et al. (U.S. Pre-Grant Publication No. 2022/0083565, hereinafter referred to as Sethi).
Regarding Claim 1:
Clement teaches a computer-implemented method for generating data for a base, the computer-implemented method comprising:
Receiving, by a computing device, a request to generate a set of data providing a functionality in a base comprising structured data,
Clement teaches “The customized prompt generation service 102 receives a request. The request is initiated from the user interface 144 through a chat box or through a user menu selection.” (Para. [0040]) and “The user interface 144 directs the request to the intended service. The request includes a query, context and intent 112. The query is a natural language description of the action the developer wants to perform. The intent is the particular software engineering task. At times, the request may not include the intent and the user interface uses a set of rules to determine the intent and forwards the request to the intended service.” (Para. [0041]).
the request comprising a natural language request for the functionality using at least a portion of the structured data of the base;
Clement teaches “The request includes a query, context and intent 112. The query is a natural language description of the action the developer wants to perform. The intent is the particular software engineering task.” (Para. [0041]) thereby teaching the request comprising a natural language request which is for a task/functionality that uses particular software and structured data of the base related to the task/action.
Determining, by the computing device, a prompt for a large language model to generate the set of data providing the functionality,
Clements teaches “The intended service generates a prompt based on a respective prompt template and transmits the prompt to a respective large language model (block 208). Each prompt includes retrieval-augmented examples of the task associated with the client.” (Para. [0042]).
Clements further teaches that the prompt for the LLM is “to generate the set of data providing the functionality” by teaching that “The customized prompt generation service 102 receives a request.” ( where “the request includes a query, context and intent 112. The query is a natural language description of the action the developer wants to perform. The intent is the particular software engineering task.” (Para. [0041])
wherein the prompt comprising a representation of:
the natural language request for the functionality;
Clement teaches using the request, including the “natural language description of the action the developer wants to perform” (Para. [0041] and Fig. 2 Elements 204-206) to generate the prompt (Para. [0042] and Fig. 2 Element 208) representing the natural language request for the action/functionality.
the portion of the structured data used in the natural language request,
Clement teaches “Each prompt includes retrieval-augmented examples of the task associated with the client.” (Para. [0042]).
a structure of the base;
Sethi teaches “configuring 505 a periodic synchronization between a first database and a second database. This may be prompted by the server 110 receiving a request to do so. The server 110 receives 510 a request to update a first table in a first database. The server 110 updates 520 the first table as requested.” (Para. [0075]). Sethi further teaches “each synchronization relationship between a source table and a target table may share a different subset of data selected from either the synchronized portion 325, the enriched portion 329, or both” (Para. [0064]) thereby teaching the structure and structural relationships.
structural relationships in the base; and
Sethi teaches “each synchronization relationship between a source table and a target table may share a different subset of data selected from either the synchronized portion 325, the enriched portion 329, or both” (Para. [0064]).
Sethi further teaches “The server 110 hosts multiple databases and performs synchronization between databases with a cross-base synchronize function. The cross-base synchronize function copies data from a shared source view to a target table. Data may be copied in one direction during a synchronization. When a synchronization completes, the target table contains all of the rows in the source view and cell data for all columns (alternatively, “fields”) selected to be synchronized. In one embodiment, only data (rows and columns) that are explicitly or implicitly set as ‘visible’ in the shared view can be copied. Users may determine what data is available to synchronize (and in what form) using a shared view interface (e.g., to designate one or more rows or columns as visible or not visible).” (Para.[0019])
data types of the structured data in the base;
Sethi teaches “the column synchronized from table one 412A to the column 427 may have a data type “text,” where the column synchronized from table two 412B to the column 427 may have a data type “date.” Because table one 412A is the primary source, the server 110 sets column 427 as having data type “text” and casts data from table two 412B for column 427 as “text.” (Para. [0069]) thereby teaching information about data types of the structured data in the base.
in response to transmitting the prompt to the large language model, receiving, by the computing device, the set of data providing the functionality from the large language model; and
Clements teaches “A response from the large language model is obtained (block 210).” (Para. [0043]) where the request was for “a query, context and intent 112. The query is a natural language description of the action the developer wants to perform. The intent is the particular software engineering task. At times, the request may not include the intent and the user interface uses a set of rules to determine the intent and forwards the request to the intended service.” (Para. [0041]) and therefore the response receive includes the requested set of data providing the requested functionality.
transmitting the set of data for display as structured data in the base.
Clements teaches “the service returns the response to the client (block 216). The client may continue the conversion by issuing further requests (block 218—yes) which are processed until there are no further requests” (Para. [0045]) thereby teaching transmitting the set of data and displaying the structured answer from the database to the client/user.
Clements explicitly teaches all of the elements of the claimed invention as recited above except:
wherein the prompt comprising a representation of:
a structure of the base;
structural relationships in the base; and
data types of the structured data in the base;
However, in the related field of endeavor of receiving a user request to perform a task, Sethi teaches:
wherein the prompt comprising a representation of:
a structure of the base;
Sethi teaches “configuring 505 a periodic synchronization between a first database and a second database. This may be prompted by the server 110 receiving a request to do so. The server 110 receives 510 a request to update a first table in a first database. The server 110 updates 520 the first table as requested.” (Para. [0075]). Sethi further teaches “each synchronization relationship between a source table and a target table may share a different subset of data selected from either the synchronized portion 325, the enriched portion 329, or both” (Para. [0064]) thereby teaching the structure and structural relationships.
structural relationships in the base; and
Sethi teaches “each synchronization relationship between a source table and a target table may share a different subset of data selected from either the synchronized portion 325, the enriched portion 329, or both” (Para. [0064]).
Sethi further teaches “The server 110 hosts multiple databases and performs synchronization between databases with a cross-base synchronize function. The cross-base synchronize function copies data from a shared source view to a target table. Data may be copied in one direction during a synchronization. When a synchronization completes, the target table contains all of the rows in the source view and cell data for all columns (alternatively, “fields”) selected to be synchronized. In one embodiment, only data (rows and columns) that are explicitly or implicitly set as ‘visible’ in the shared view can be copied. Users may determine what data is available to synchronize (and in what form) using a shared view interface (e.g., to designate one or more rows or columns as visible or not visible).” (Para.[0019])
data types of the structured data in the base;
Sethi teaches “the column synchronized from table one 412A to the column 427 may have a data type “text,” where the column synchronized from table two 412B to the column 427 may have a data type “date.” Because table one 412A is the primary source, the server 110 sets column 427 as having data type “text” and casts data from table two 412B for column 427 as “text.” (Para. [0069]) thereby teaching information about data types of the structured data in the base.
Thus, it would have been obvious to one of ordinary skill in the art, having the teachings of Sethi and Clement at the time that the claimed invention was effectively filed, to have modified the systems and methods for automating prompts to an LLM to perform a specified software engineering task, as taught by Clement, with the additional requests for the task of automatic synchronization from one or more databases to a table, as taught by Sethi.
One would have been motivated to make such combination because Clement teaches “Developers not familiar with the nuances of a software engineering task and the idiosyncrasies of the large language model often need assistance in crafting a prompt to achieve the best results” (Para.[0016]) where “A request may include a query, a context, and/or an intent 112. The query is a request for an action, the context is the subject of the action…and the intent specifies the software engineering task related to the action” (Para.[0024]) and Sethi teaches additional software engineering tasks/actions (“synchronizing data from one or more sources to a data table” – Para. [0002]) that would benefit from similar assistance to Clement because “Enterprises and other entities often provide different users with access permission to different subsets of the data available to the entity. As a result, entities typically maintain multiple databases that include partially overlapping data. Maintaining consistency between the overlapping portions can be a time-consuming and error prone task. For example, if a human is responsible for entering new data into multiple databases, typographical and other errors may lead to discrepancies between different versions of the data” (Para. [0003]). It would have been obvious to a person having ordinary skill in the art that expanding the assistance taught by Clement to include assistance for the synchronization of data task/action taught by Sethi would create a more diverse and useful system for assisting developers not familiar with the nuances of a wider array of software engineering tasks.
Regarding Claim 2:
Sethi and Clement further teach:
determining, using the prompt at a network system hosting the large language model, the set of data providing the functionality,
Clement teaches “ a custom prompt 114 is created for the large language model to perform a specific software engineering task and generate a response 116.” (Para. [0022]) thereby teaching determining, using the prompt, the set of data providing the functionality for performing the specific software engineering task and generating the response 116.
the large language model configured to:
interpret the natural language request for the functionality using at least the structured data,
Clement teaches “a large language model 104 is a neural transformer model with attention. A neural transformer model with attention is one distinct type of machine learning model. Machine learning pertains to the use and development of computer systems that are able to learn and adapt without following explicit instructions by using algorithms and statistical models to analyze and draw inferences from patterns in data” (Para.[0026]) and “Deep learning differs from traditional machine learning since it uses multiple stages of data processing through many hidden layers of a neural network to learn and interpret the features and the relationships between the features” (Para. [0027]).
identify contextual relationships between the functionality and one or more of the structure of the structured data, the structural relationships of data in the structured data, and data types of the structured data, and
Clement teaches “a large language model 104 is a neural transformer model with attention. A neural transformer model with attention is one distinct type of machine learning model. Machine learning pertains to the use and development of computer systems that are able to learn and adapt without following explicit instructions by using algorithms and statistical models to analyze and draw inferences from patterns in data” (Para.[0026]) and “Deep learning differs from traditional machine learning since it uses multiple stages of data processing through many hidden layers of a neural network to learn and interpret the features and the relationships between the features” (Para. [0027]).
determine the set of data providing the functionality based on the contextual relationships.
Clement teaches “a large language model 104 is a neural transformer model with attention. A neural transformer model with attention is one distinct type of machine learning model. Machine learning pertains to the use and development of computer systems that are able to learn and adapt without following explicit instructions by using algorithms and statistical models to analyze and draw inferences from patterns in data” (Para.[0026]) and “Deep learning differs from traditional machine learning since it uses multiple stages of data processing through many hidden layers of a neural network to learn and interpret the features and the relationships between the features” (Para. [0027]).
Clement further teaches “The prompt includes a description of the task…and instructions describing the action the large language model is to perform and the output format of the response.” (Para. [0063]) and “The prompt is sent to the large language model (block 510) and the model returns a response which is output (block 512)” (Para. [0061]). Therefore, Clement teaches determining the set of data providing the functionality based on the contextual relationships made by the LLM so that a response can be formed.
Regarding Claim 3:
Sethi and Clement further teach:
receiving, by the computing device, a selection of the large language model from a plurality of large language models, wherein the plurality of large language models is provided to a client device generating the prompt; and
Clement teaches “The customized prompt generation service 102 receives a request. The request is initiated from the user interface 144 through a chat box or through a user menu selection. The user interface includes a menu that includes a button for each software engineering service.” (Para. [0040]) where “The user interface 144 directs the request to the intended service.” (Para.[0041]) and “the intended service generates a prompt based on a respective prompt template and transmits the prompt to a respective large language model (block 208)” (Para. [0042])
wherein generating the prompt for the large language model accounts for a configuration of the selected large language model.
Clement teaches “The user interface 144 directs the request to the intended service.” (Para.[0041]) and “the intended service generates a prompt based on a respective prompt template and transmits the prompt to a respective large language model (block 208)” (Para. [0042]) thereby teaching that the respective prompt for the LLM accounts for a configuration of the respective LLM.
Regarding Claim 4:
Sethi and Clement further teach:
wherein the natural language request comprises one or more data objects representing the portion of the structured data of the base.
Clement teaches “The user interface 144 directs the request to the intended service. The request includes a query, context and intent 112. The query is a natural language description of the action the developer wants to perform. The intent is the particular software engineering task.” (Para. [0041]) thereby teaching the natural language request comprising the data objects describing the action the developer wants to perform.Sethi teaches an example of a request as including “the data access module 220 receives a request from a client device 140 indicating an identifier of the requesting user (e.g., a username or user identifier) and data from a specified table in a specified database that the user wishes to view” (Para. [0058]) thereby teaching the specific portion of the structured data of the base being included in the request.
Regarding Claim 5:
Sethi and Clement further teach:
receiving, at the computing device, an edit to the set of data displayed as structured data of the base;
Sethi teaches “The data update module 230 provides a mechanism for creators and their collaborators to edit data in and add data to databases. In one embodiment, the data update module 230 receives a request from a client device 140 indicating an identifier of the requesting user and data to be added to or amended into a specified table in a specified database” (Para. [0059]).
generating, at the computing device, a flag for the set of data as manipulated data based on the edit;
Sethi teaches “if a target table is duplicated, the duplicate table has the same configuration as the original target table. If the user deletes a target table, then restores it, the target table may regain its original configuration from before its deletion” (Para. [0049]) thereby teaching flagging a set of data as manipulated based on an edit action.
responsive to receiving an additional request to modify the set of data, determining an additional prompt and modifying the set of data based on the generated flag.
Sethi teaches “A response from the large language model is obtained (block 210). A post-processing action may be performed on the response to ensure that the response addresses the query (block 212). If the response is not adequate (block 214—yes), the service may continue the conversation with the large language model for additional data (block 208). The service creates an additional prompt to alleviate any issues detected by the post processing actions (block 208). The additional prompt includes the previously-transmitted prompts since the large language model does not save context information from previous prompts of the conversation.” (Paras. [0043]-[0044]). Therefore Sethi teaches an additional request related to the particular response/previous request and determining an additional prompt related to the contents of the previous request.Sethi teaches subsequent requests related to the same data by teaching “if a target table is duplicated, the duplicate table has the same configuration as the original target table. If the user deletes a target table, then restores it, the target table may regain its original configuration from before its deletion” (Para. [0049])
Regarding Claim 6:
Sethi and Clement further teach:
wherein the structure of the structured data comprises one or more of:
a plurality of elements in the base, each element comprising structured data;
Sethi teaches “In practice, the bases data store 210 will likely include many more (e.g., hundreds, thousands, or even millions of) bases. Base one 310 includes table one 312, which has a synchronized portion 315 and an unsynchronized portion 317. Base two 320 includes table two 322, which includes a synchronized portion 325 (which mirrors the synchronized portion 315 of table one 312 except for any differences that arose since the previous synchronization operation) and an enriched portion 329. Th