DETAILED ACTION
Remarks
The instant application having Application Number 19/473,859 filed on October 9, 2025 has a total of 20 claims pending in the application; there are 3 independent claims and 17 dependent claims, all of which are presented for examination by the examiner.
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
Examiner Notes
Examiner cites particular columns and line numbers in the references as applied to the claims below for the convenience of the applicant. Although the specified citations are representative of the teachings in the art and are applied to the specific limitations within the individual claim, other passages and figures may apply as well. It is respectfully requested that, in preparing responses, the applicant fully consider the references in entirety as potentially teaching all or part of the claimed invention, as well as the context of the passage as taught by the prior art or disclosed by the examiner.
The examiner requests, in response to this Office action, supports are shown for language added to any original claims on amendment and any new claims. That is, indicate support for newly added claim language by specifically pointing to page(s) and line no(s) in the specification and/or drawing figure(s). This will assist the examiner in prosecuting the application.
When responding to this office action, Applicant is advised to clearly point out the patentable novelty which he or she thinks the claims present, in view of the state of the art disclosed by the references cited or the objections made. He or she must also show how the amendments avoid such references or objections See 37 CFR 1.111(c).
Information Disclosure Statement
As required by M.P.E.P. 609(C), the applicant’s submissions of the Information Disclosure Statements dated January 22, 2026 is acknowledged by the examiner and the cited references have been considered in the examination of the claims now pending. As required by M.P.E.P 609 C (2), a copy of the PTOL-1449 initialed and dated by the examiner is attached to the instant office action.
Drawings
The applicant’s drawings submitted are acceptable for examination purposes.
Objection to Abstract
The abstract of the disclosure is objected to because there should not have any information other than a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. Correction is required. See MPEP § 608.01(b) for guidelines for the preparation of patent abstracts.
Applicant is reminded of the proper language and format for an abstract of the disclosure.
A patent abstract is a concise statement of the technical disclosure of the patent and should include that which is new in the art to which the invention pertains. The abstract should not refer to purported merits or speculative applications of the invention and should not compare the invention with the prior art.
The abstract should be in narrative form and generally limited to a single paragraph on a separate sheet within the range of 50 to 150 words in length. The abstract should describe the disclosure sufficiently to assist readers in deciding whether there is a need for consulting the full patent text for details.
CLAIM INTERPRETATION
The following is a quotation of 35 U.S.C. 112(f):
(f) Element in Claim for a Combination. – An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The following is a quotation of pre-AIA 35 U.S.C. 112, sixth paragraph:
An element in a claim for a combination may be expressed as a means or step for performing a specified function without the recital of structure, material, or acts in support thereof, and such claim shall be construed to cover the corresponding structure, material, or acts described in the specification and equivalents thereof.
The claims in this application are given their broadest reasonable interpretation using the plain meaning of the claim language in light of the specification as it would be understood by one of ordinary skill in the art. The broadest reasonable interpretation of a claim element (also commonly referred to as a claim limitation) is limited by the description in the specification when 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is invoked.
As explained in MPEP § 2181, subsection I, claim limitations that meet the following three-prong test will be interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph:
(A) the claim limitation uses the term “means” or “step” or a term used as a substitute for “means” that is a generic placeholder (also called a nonce term or a non-structural term having no specific structural meaning) for performing the claimed function;
(B) the term “means” or “step” or the generic placeholder is modified by functional language, typically, but not always linked by the transition word “for” (e.g., “means for”) or another linking word or phrase, such as “configured to” or “so that”; and
(C) the term “means” or “step” or the generic placeholder is not modified by sufficient structure, material, or acts for performing the claimed function.
Use of the word “means” (or “step”) in a claim with functional language creates a rebuttable presumption that the claim limitation is to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites sufficient structure, material, or acts to entirely perform the recited function.
Absence of the word “means” (or “step”) in a claim creates a rebuttable presumption that the claim limitation is not to be treated in accordance with 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. The presumption that the claim limitation is not interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, is rebutted when the claim limitation recites function without reciting sufficient structure, material or acts to entirely perform the recited function.
Claim limitations in this application that use the word “means” (or “step”) are being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action. Conversely, claim limitations in this application that do not use the word “means” (or “step”) are not being interpreted under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph, except as otherwise indicated in an Office action.
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.
Claims 18-20 are 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 pre-AIA the applicant regards as the invention.
Claim limitations “chat engine”, “a communication tool”, “LLM tool”, recited in claims 18-20 respectively invokes 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph. However, the written description fails to disclose the corresponding structure, material, or acts for performing the entire claimed function and to clearly link the structure, material, or acts to the function. Therefore, the claim is indefinite and is rejected under 35 U.S.C. 112(b) or pre-AIA 35 U.S.C. 112, second paragraph.
Applicant may:
(a) Amend the claim so that the claim limitation will no longer be interpreted as a limitation under 35 U.S.C. 112(f) or pre-AIA 35 U.S.C. 112, sixth paragraph;
(b) Amend the written description of the specification such that it expressly recites what structure, material, or acts perform the entire claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(c) Amend the written description of the specification such that it clearly links the structure, material, or acts disclosed therein to the function recited in the claim, without introducing any new matter (35 U.S.C. 132(a)).
If applicant is of the opinion that the written description of the specification already implicitly or inherently discloses the corresponding structure, material, or acts and clearly links them to the function so that one of ordinary skill in the art would recognize what structure, material, or acts perform the claimed function, applicant should clarify the record by either:
(a) Amending the written description of the specification such that it expressly recites the corresponding structure, material, or acts for performing the claimed function and clearly links or associates the structure, material, or acts to the claimed function, without introducing any new matter (35 U.S.C. 132(a)); or
(b) Stating on the record what the corresponding structure, material, or acts, which are implicitly or inherently set forth in the written description of the specification, perform the claimed function. For more information, see 37 CFR 1.75(d) and MPEP §§ 608.01(o) and 2181.
Claim Rejections - 35 USC § 102
The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale or otherwise available to the public before the effective filing date of the claimed invention.
Claims 1-10, 12-14, 16 and 17 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by US 2021/0365630 A1 to Sara Itani (hereinafter 'Itani').
Regarding Claim 1, Itani discloses a computer implemented method, comprising:
receiving, in a machine-user chat interface, a task request from a user (Para [0059]-"ln various embodiments, the domain-specific language is processed through an environment or tool such as the rapid screening system illustrated in FIG. 1, which includes an interactive multiline editor configured to accept user input (e.g., from a keyboard, voice recognition or natural language processing, on-screen buttons, a dropdown menu, and/or a contextual menu item selection, among various input options, some of which may be displayed in lieu of the editor); a parser and an interpreter configured to interpret the input according to the specification defining the domain-specific language; a server engine configured to obtain structured and/or unstructured data related to the domain, according to the input; and a visualization tool configured to provide a live updating results display or grid view as the user enters input into the interactive editor"; Para [0088]-"ln block 285, the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiments, the operational routine 200 may process additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous input to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any lime.". Please also para [0144], [0147]);
identifying, from the task request, a first online resource to perform a first operation in a task (Itani Para [0081]-"For example, if the user enters an expression, the operational routine 200 identifies the data values to which the parsed symbols refer, determines what operations should be performed, and executes the required operations.");
invoking the first online resource and requesting performance of the first operation; receiving, from the first online resource, a result of the first operation; and providing the result of the first operation to the user (Itani Para [0191]-"A user interacts with a rapid screening system, such as via a web application or other client device 281O (e.g., running local software or accessing software-as-a-service (SaaS)). The user requests a backtest 2805, e.g., as detailed above with reference to FIG. 17, para [0137-0138]. The rapid screening system may operate similarly for less complex results such as modifying screening criteria and other portfolio analyses. The client web application or device 2810 sends a request 2815 to a session server 2820, e.g., a host device managing a user database on a remote server. The request 2815 may contain, e.g., subscriber authentication or session information to ensure that the subscriber is authorized (and, e.g., charged for the backtesting-service) and/or dates and codes that specify the parameters of the backtest such as the criteria to test and the benchmark to test against. In various embodiments, the parameters are already associated with a user's session and available on the server, so identification of the current session and the backtesting request can be accomplished with minimal data transmission. In some embodiments, the client interface device or web application 2810 sends the session server 2820 one or more triggers for when new screens, backtests, or other portfolio analytics requests occur, and subscribes for updates to be received when the analysis is completed.").
Regarding Claim 2, Itani discloses the computer implemented method of claim 1, further comprising receiving a user directive responsive to the result of the first operation (Para [0088]-"ln block 285, the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiments, the operational routine 200 may process additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous input to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any time. If additional input is received, the operational routine 200 loops back to input parsing block 225 to process the next input symbol, if any.").
Regarding Claim 3, Itani discloses the computer implemented method of claim 2, further comprising requesting the first online resource an adjustment to the first operation based on the user directive (Para [0038]-"The technology includes an input interface such as a multiline editor that allows the user to enter, amend, add, insert, subtract, change, and otherwise freely edit the entered symbols and operators at any time"; Para [0149]-"These classifiers can either be utilized individually, or as part of an ensemble. Ensembles enable us to reduce overfitting for the predictive context. And some models enable us to introspect further into the specific weights. Other implementations include combining classifiers generated from different "runs" of this same algorithm. The resultant prediction module (a combination of classifiers) can be applied in a number of different ways, or can enable a user to adjust or iterate with the machine on feature weights, for instance.").
Regarding Claim 4, Itani discloses the computer implemented method of claim 1, wherein the task request comprises one or more parameters for a task, and a goal, and wherein the first operation comprises using a first parameter from the one or more parameters (Para [0170]-"Because we have a strong semantic understanding of the goal the user is trying to accomplish, we can statically and/or dynamically analyze to make suggestions that improve their strategy performance."; Para [0191]-"ln various embodiments, the parameters are already associated with a user's session and available on the server, so identification of the current session and the backtesting request can be accomplished with minimal data transmission.").
Regarding Claim 5, Itani discloses the computer implemented method of claim 4, further comprising identifying a second online resource to perform a second operation and invoking a second online resource to request performance of the second operation including a second parameter from the one or more parameters (Para [0145]-"The disclosed technology allows inference of a screener's feature selection parameters from the user's use of the domain-specific language in constructing the existing screens. Analyzing use of such a domain-specific language would have not been possible in any existing screener because no other screener that is similarly accessible is sufficiently expressive for the user to say what they're looking for, nor in an existing language that is not structured or constrained enough that we're able to infer semantic meaning from the user's use of the tool."; Para [0151]-"A regime change model is typically defined as a time-series model in which parameters are allowed to take on different values in each of some fixed number of "regimes". Typically, a regime change model includes some set of predefined factors that they look for, and some fixed definition of a regime. Usually, significant thought goes into defining "what are the regimes" and ''what are the features that influence the regimes").
Regarding Claim 6, Itani discloses the computer implemented method of claim 4, wherein invoking the fir�! online resource comprises:
making an application programming interface (API) call to a plugin for a server hosting the first online resource (Para [0193]-" In various embodiments, the screening and/or backtest functions can plug into many types of datastores, and are not limited to databases 2850; for example, application programming interfaces (APls) may also be used effectively."); and
interacting with an associated large language model (LLM) to provide the one or more parameters for the task (Para [0104]-"For example, a natural language request to "find me all companies that mention China tariffs in 1OK filings" could be processed by a language model (e.g., GPT-3) to generate expressions in the domain-specific language. Because the domain-specific language is semantically meaningful, a human can understand the output, verify that the expression is doing what the natural language statement requested, and edit it if desired. Because expressions in the domain-specific language are so expressive within a short space, they are easily verifiable by the user, providing a more meaningful and readily trusted mode of interaction.").
Regarding Claim 7, Itani discloses the computer implemented method of claim 1, further comprising providing, within the machine-user chat interface, a widget for the user to enable a task stop and a task pause (Para [0086]-"Adding complete or mostly compete results to the display.at once when the values are ready rather than piecemeal as they are loaded can help the operational routine 200 present results in a way that improves the user's sense of receiving immediate results. In general, results presented within several seconds .of the user completing an expression or line of input is perceived as "immediate." A user may discount a startup delay, e.g., for initial loading of a complex data set, when judging whether an interface thereafter provides immediate responsiveness in presenting results."; Para [0088]-"ln block 285,' the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiments, the operational routine 200 may process additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous i11put to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any time. If additional input is received, the operational routine 200 loops back to input parsing block 225 to process the next input symbol, if any.").
Regarding Claim 8, Itani discloses the computer implemented method of claim 1, wherein providing the result of the first operation to the user comprises providing a graphic display of the result in a graphical user interface of a kiosk located in a proximity of the user at a retail store (Para [0187]-"The computer system 2700 can include input components 271O that receive input from user interactions and provide input to the processor 2730, typically mediated by a hardware controller that interprets the raw signals received from the input device and communicates the information to the processor 2730 using a known communication protocol. Examples of an input component 271O include a keyboard 2712 (with physical or virtual keys), a pointing device (such as a mouse 2714, joystick, dial, or eye tracking device), a touchscreen 2715 that detects contact _events when ii is touched by a user, a microphone 2716 that receives audio input, and a camera 2718 for still photograph and/or video capture. The computer system 2700 can also include various other input components 2710 such as GPS or other location determination sensors, motion sensors, wearable input devices with accelerometers (e.g., wearable glove-type input devices), biometric sensors (e.g., a fingerprint sensor), light sensors (e.g., an infrared sensor), card readers (e.g., a magnetic stripe reader or a memory card reader), and so on.").
Regarding Claim 9, Itani discloses the computer implemented method of claim 1, further comprising instructing the first online resource to stop the first operation upon a first request from the user after providing the result of the first operation (Para (0086]-"Adding complete or mostly complete results to the display at once when the values are ready rather than piecemeal as they are loaded can help the operational routine 200 present results in a way that improves the user's sense of receiving immediate results. In general, results presented within several seconds of the user completing an expression or line of input is perceived as "immediate." A user may discount a startup delay, e.g., for initial loading of a complex data set, when judging whether an interface thereafter provides immediate responsiveness in presenting results."; Para [0088]-"ln block 285, the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiments, the operational routine 200 may process additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous input to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any time. If additional input is received, the operational routine 200 loops back to input parsing block 225 to process the next input symbol, if any.").
Regarding Claim 10, Itani discloses the computer implemented method of claim 9, further comprising instructing the first online resource to pause the first operation upon a second request from the user after providing the result of the first operation (Para (0086]-"Adding complete or mostly compete results to the display at once when the values are ready rather than piecemeal as they are loaded can help the operational routine 200 present results in a way that improves the user's sense of receiving immediate results. In general, • results presented within several seconds of the user completing an expression or line of input is perceived' as "immediate." A user may discount a startup delay, e.g., for initial loading of a complex data set, when judging whether an interface thereafter provides immediate responsiveness in presenting results."; Para [0088]-"ln block 285, the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiments, the operational routine 200 may process, additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous input to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any time. If additional input is received, the operational routine 200 loops back to input parsing block 225 to process the next input symbol, if any.").
Regarding Claim 12, Itani discloses a computer implemented method, comprising:
training a model for task execution including identifying, from a user input in a machine-user chat interface, a request to execute a task (Para [0059]-"ln various embodiments, the domain-specific language is processed through an environment or tool such as the rapid screening system illustrated in FIG. 1, which includes an interactive multiline editor configured to accept user input (e.g., from a keyboard, voice recognition or natural language processing, on-screen buttons, a dropdown menu, and/or a contextual menu item selection, among various input options, some of which may be displayed in lieu of the editor); a parser and an interpreter configured to interpret the input according to the specification defining the domain-specific language; a server engine configured to-obtain structured and/or unstructured data related to the domain, according to the input; and a visualization tool configured to provide a live updating results display or grid view as the user enters input into the interactive editor"; Para (0088]-"ln block 285, the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiment, the operational routine 200 may process additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous input to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any time.". Please also para [0144], [0147]);
selecting, using the model for task execution, one or more operations for execution in the task; identifying at least one online resource to execute each of the one or more operations (Para (0081]-"For example, if the user enters an expression, the operational ,routine 200 identifies the data values to which the parsed symbols refer, determines what operations should be performed, and executes the required operations.");
collecting, from the at least one online resource, a result of the task; and training the model for task execution based on the result of the task (Para (0147]-"The approach in this case is to use machine learning to train a set of classifiers that take as input a universe of companies at a point in time, and a selected subset of example companies. The example companies may be a list of individual identifiers chosen individually or may be those satisfying some filtering criteria (e.g., ROE above a threshold, or company names containing ·"hotels"). The example companies in the subset may be "preferred" targets, companies having characteristics that the screener wishes to avoid, or some other category. Approaches to developing the ML model may include bootstrapping, random forest, AutoML, or other ML models, preferably a model where the trainer can infer relative feature weights. The trainer can exert some control over the resulting screening models, e.g., by training more models and averaging. The classifiers are trained to produce the example companies; they all achieve the same solution, but in different ways. Thus, they can predict filters that would have found those example companies. This can reveal the user's preferences by showing what data tags, and weights correlate or anti-correlate. In some implementations, the result is generation of a probability distribution and feature weights."; Para (0191]-"A user interacts with a rapid screening system, such as via a web application or other client device 2810 (e.g., running local software or accessing software-as-a-service (SaaS)). The user requests a backtest 2805, e.g., as detailed above with reference to FIG. 17. The rapid screening system may operate similarly for less complex results such as modifying screening criteria and other portfolio analysis, The client web application or device 2810 sends a request 2815 to a session server 2820, e.g., a host device managing a user database on a remote server. The request 2815 may contain, e.g., subscriber authentication or session information to ensure that the subscriber is authorized (and, e.g., charged for the backtesting service) and/or dates and codes that specify the parameters of the backtest such as the criteria to test and the benchmark to test against. In various embodiments, the parameters are already associated with a user'� session and available on the server, so identification of the current session and the backtesting request can be accomplished with minimal data transmission. In some embodiments, the client interface device or web application 2810 sends the session server 2820 one or more triggers for when new screens, backtests, or other portfolio analytics requests occur, and subscribes for updates to be received when the analysis is completed.").
Regarding Claim 13, Itani discloses the computer implemented method of claim 12, wherein the request comprises one or more parameters, and a goal, and wherein the task execution is based on the goal (Para [0170]-"Because we have a strong semantic understanding of the goal the user is trying to accomplish, we can statically and/or dynamically analyze to make suggestions that improve their strategy performance."; Para [0191]-"ln various embodiments, the parameters are already associated with a user's session and available on the server, so identification of the current session and the backtesting request can be accomplished with minimal data transmission.").
Regarding Claim 14, Itani discloses the computer implemented method of claim 13, wherein the training is based on a comparison between the result of the task and the goal (Para [0122]-"Another way to apply the disclosed technology to identify a positive stability trend is to compare stability trends over two time periods. For example, the following expressions establish a custom data tag named "RoeStabilityPrevious" that represents trend stability for a return on equity percentage from a year ago until four months ago, and a custom data tag named "RoeStabilityRecent" that represents trend stability for a return of equity percentage from four months ago until today:"; Para [0139]-"ln various embodiments, the backtesting results 1700B include report generation including various approaches to charting and listing the results of the backtest. For example, the backtest can provide statistics for how investments chosen according to the selected screening criteria would have performed over the selected period, and/or accounting for returns over time) in comparison to one or more benchmarks. The disclosed technology provides unprecedented accessibility to execution of concurrent programming, which users simply could not take advantage of with conventional technology.").
Regarding Claim 16, Itani discloses the computer implemented method of claim 14, wherein the model for task execution is a neural network and selecting the one or more operations for execution comprises identifying a classifier for each of the one or more operations (Para [0149]-"These classifiers can either be utilized individually, or as part of an ensemble. Ensembles enable us to reduce overfilling for the predictive context. And some models enable us to introspect further into the specific weights. Other implementations include combining classifiers generated from different "runs" of this same algorithm. The resultant prediction module (a combination of classifiers) can be applied in a number of different ways, or can enable a user to adjust or iterate with the machine on feature weights, for instance.").
Regarding Claim 17, Itani discloses the computer implemented method of claim 16, wherein the classifier includes a semantic context of a keyword caption of the task, the one or more parameters, and the goal (Para [0051]-"ln some embodiments, the domain-specific language contains no non-domain-specific keywords. In that case, operators are all symbols (such as "+" or brackets"["... "]").Asa result, all alphabetic text that a user enters in the multiline editor 101 can be understood to be meaningful, e.g., a data tag. In some embodiments, the domain-specific language contains no non-domain-specific keywords other than transformation functions."; Para [0138]-"ln some embodiments, backtesling is implemented using a subset of the same code as the system uses for screening, which is made possible by the domain-specific context."; Para [0211]-"Views, for example, can be passed back to the web application 2910 as HTML or in a data structure, which allows data to be rendered specially based on its content or context").
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102 of this title, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 11, 15, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over US 2021/0365630 A1 to Sara Itani (hereinafter 'Itani') in view of US 2015/0161529 A1 to Kondaji et al. (hereinafter ' Kondaji').
Regarding Claim 11, Itani in view of Kondaji discloses the system of claim 9. In particular, Kondaji discloses further comprising posting the result of the first operation in a third-party social network upon a third request from the user.
However, Kondaji discloses further comprising posting the result of the first operation in a third-party social network upon a third request from the user (Para [0055]-"Additionally, the event-management system 170 may determine the event categories that should be associated with the user 101 based on the user 101's tastes, event history information, social networking information, other suitable information, or any combination thereof, Para [0085]-"As an example and not by way of limitation, the advertisement may be included in a webpage, text, graphic image, email, instant message, text message, audio message, page, MMS text, social network message, another suitable format, or any combination thereof.").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Itani and Kondaji before him/her, to construct the system of Itani to account for social media alerts as taught in Kondaji in order to optimize automated task generation (Kondaji Para [0087]-"ln particular embodiments, a master server may receive a job from a client and then assigns tasks resulting from that job to one or more slave servers or nodes, which do the actual work of executing the assigned tasks upon instruction from the master and which move data between tasks.").
Regarding Claim 15, Itani in view of Kondaji discloses the system of claim 14. In particular, Kondaji discloses wherein training the model for task execution comprises embedding the result of the task and the goal as two vectors in a multi-dimensional space and finding a cosine distance between the two vectors (Kondaji Para [0068]- "In particular embodiments, event-vector comparison 400 may include calculating a distance between each of the corresponding sub-vectors of event-vectors {right arrow over (A)} 330A and {right arrow over (B)} 330B, where a distance may refer to a Euclidean distance, a cosine similarity, a Pearson correlation, or any other suitable measure of distance between two vectors."). It would have been obvious to one of ordinary skill in the art to construct the system of Itani to account for vector comparison as taught in Kondaji in order to optimize automated task generation (Kondaji Para [0087]-"ln particular embodiments, a master server may receive a job from a client and then assigns tasks resulting from that job to one or more slave servers or nodes, which do the actual work of executing the assigned tasks upon instruction from the master and which move data between tasks.").
Regarding Claim 18, Itani discloses a system, comprising:
a first server including a chat engine configured to receive a user input from a user; and a communication module configured to communicate with one or more other servers (Itani Para [0059]-"ln various embodiments, the domain-specific language is processed through an environment or tool such as the rapid screening system illustrated in FIG. 1, which includes an interactive multiline editor configured to accept user input (e.g., from a keyboard, voice recognition or natural language processing, on-screen buttons, a dropdown menu, and/or a contextual menu item selection, among various input options, some of which may be displayed in lieu of the editor); a parser and an interpreter configured to interpret the input according to the specification defining the domain-specific language; a server engine configured to obtain structured and/or unstructured data related to the domain, according to the input; and a visualization tool configured to provide a live updating results display or grid view as the user enters input into the interactive editor"; Itani Para [0088]-"ln block 285, the operational routine 200 determines whether additional input is received, e.g., via the interface for user input. In some embodiments, the operational routine 200 may process additional input (including modification or deletion of previous input, at any location in the previous input) asynchronously and without waiting for other blocks (e.g., obtaining data 245) to be complete, or may cancel the processing of previous input to respond to new or changed input to ensure rapid display of the most recently requested data. In other words, the operational routine may determine whether additional input is received at any time."); and
wherein: the chat engine includes a large language model (LLM) tool configured to receive the user input from a user interface of a client device, the user input includes a defined task to be completed by the first server (Itani Para [0104]-"For example, a natural language request to "find me all companies that mention China tariffs in 10K filings" could be processed by a language model (e.g., GPT-3) to generate expressions in the domain-specific language. Because the domain-specific language is semantically meaningful, a human can understand the output, verify that the expression is doing what the natural language statement requested, and edit it if desired. Because expressions in the domain-specific language are so expressive within a short space, they are easily verifiable by the user, providing a more meaningful and readily trusted mode of interaction.").
Itani does not explicitly disclose the LLM tool is configured to use the one or more other servers as slave servers to completer the defined task.
However, Kondaji discloses the LLM tool is configured to use the one or more other servers as slave servers to completer the defined task (Kondaji Para [0087]-"In particular embodiments, system 100 may comprise a high-availability cluster of commodity servers with one or more slave servers as nodes. Ordinarily, nodes in a high-availability cluster are redundant, so that if one node crashes while performing a particular application, the cluster software can restart the application on one or more other nodes. Multiple nodes may also facilitate parallel processing of large databases. In particular embodiments, a master server may receive a job from a client and then assigns tasks resulting from that job to one or more slave servers or nodes, which do the actual work of executing the assigned tasks upon instruction from the master and which move data between tasks. In particular embodiments, the client jobs will invoke Hadoop's MapReduce functionality. Likewise, in particular embodiments, a master server governs a distributed file system that supports parallel processing of large databases. As an example and not by way of limitation, a master server may manage· the file system's namespace and block mapping to nodes, as well as client access to files, which are actually stored on slave servers or nodes. In turn, in particular embodiments, the slave servers do the actual work of executing read and write requests from clients and perform block creation, deletion, and replication up6n instruction from the master server.").
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention was made having the teachings of Itani and Kondaji before him/her, to construct the system of Itani to account for social media alerts as taught in Kondaji in order to optimize automated. task generation (Kondaji Para [0087]-"ln particular embodiments, a master server may receive a job from a client and then assigns tasks resulting from that job to one or more slave servers or nodes, which do the actual work of executing the assigned tasks upon instruction from the master and which move data between tasks.").
Regarding Claim 19, Itani in view of Kondaji discloses the system of claim 18. In particular, Kondaji discloses wherein the LLM tool is configured to invoke LLM tools of the slave servers to use the slave servers for completion of the defined task either sequentially or parallelly (Kondaji Para [0087]-"ln particular embodiments, system 100 may comprise a high-availability cluster of commodity servers with one or more slave servers as nodes. Ordinarily, nodes in a high-availability cluster are ,redundant, so that if one node crashes while performing a particular application, the cluster software can restart the application on one or more other nodes. Multiple nodes may also facilitate parallel processing of large databases. In particular embodiments, a master server may receive a job from a client and then assigns tasks resulting from that job to one or more slave servers or nodes, which do the actual work of executing the assigned tasks upon instruction from the master and which move data between tasks. In particular embodiments, the client jobs will invoke Hadoop's MapReduce functionality. Likewise, in particular embodiments, a master server governs a distributed file system that supports parallel processing of large databases. As an example and not by way of limitation, a master server may manage the file system's namespace and block mapping to nodes, as well as client access to files, which are actually stored on slave servers or nodes. In turn, in particular embodiments, the slave servers do the actual work of executing read and write requests from clients and perform block creation, deletion, and replication upon instruction from the master server. Likewise, in particular embodiments, a master server governs a distributed file system that supports parallel processing of large databases.").
Regarding Claim 20, Itani in view of Kondaji discloses the system of claim 19. In particular, Itani discloses wherein the LLM tool is configured to receive status from the invoked LLM tools (Itani Para [0059]-"a visualization tool configured to provide a live updating results display or grid view as the user enters input into the interactive editor."), and
Kondaji discloses the slave servers (Kondaji Para [0087]-"In particular embodiments, a master server may receive a job from a client and then assigns tasks resulting from that job to one or more slave servers or nodes, which do the actual work of executing the assigned tasks upon instruction from the master and which move data between tasks."); and present a completed task to the user via the client device; and wherein the client device comprises at least one of a smart phone, a smart watch, a tablet, a smart television, a game console, a kiosk, a digital out-of-home display (Kondaji Para [0012]-"A client system 130 may be any suitable computing device, such as, for example, a personal computer, a laptop, a cellular phone, a smartphone, a personal digital assistant, an ultra-mobile PC, or a computing tablet."; Kondaji Para [0089]-"a desktop computer system, a laptop or notebook computer system, an interactive kiosk, a mainframe, a mesh of computer systems, a mobile telephone, a personal digital assistant (PDA), a server, a tablet computer system, or a combination of two or more of these.").
Conclusion
The prior art made of record, listed on form PTO-892, and not relied upon, if any, is considered pertinent to applicant’s disclosure.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to HASANUL MOBIN whose telephone number is (571)270-1289. The examiner can normally be reached on 9AM to 6:00PM EST M-F.
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/HASANUL MOBIN/
Primary Examiner, Art Unit 2168
