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 .
This communication is responsive to the amendment filed on 12/10/2025.
Status of claims:
Claims 2, 11 and 18 are canceled
Claims 1, 6, 10, 14 and 17 are amended.
claims 1, 3-10, 12-17 and 19-20 are presented for examination.
Response to Arguments
Applicant’s argument regarding to the amended claims have been considered but are moot because the new ground of rejection.
The amendment to the specification filed on 12/10/2025 has been considered as to the merits.
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, 3-10, 12-17 and 19-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Claims 1, 10 and 17:Step 1: Statutory Category
The claims are directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter.
Step 2A, Prong One:
The limitations “determine …; generating…; generating…; generating…; generating…; generating…”, as drafted, are processes that, under its broadest reasonable interpretation, covers a mental process as a form of evaluation or judgement, but for the recitation of generic computer components. That is nothing in the claim element precludes the steps from practically being performed in a human mind. For example, the limitations “determine …; generating…; generating…; generating…; generating…; generating…”, in the context of the claim encompasses one can manually or mentally with the aid of pen and paper.
If a claim limitation, under its broadest reasonable interpretation, covers mental processes but for the recitation of generic computer components, then it falls within the "Mental Processes" grouping of abstract ideas (concepts performed in the human mind including an observation, evaluation, judgement, and opinion). Accordingly, the claim recites an abstract idea.
Step 2A, Prong Two: Integrated into a Practical Application
The claim recites the additional elements “processing a query; storing…; parsing …”, represent(s) an extra solution activity because it is a mere nominal or tangential addition to the claim, a mere generic transmission and presenting of collected and analyzed data. (See MPEP 2106.05(g)).
“a query resolution model; a query resolution engine, processor, non-transitory computer readable medium” are recited at a high level of generality such that they amount to on more than mere instructions to apply the exception using a generic component. (see MPEP 2106.05(f)). These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer (see MPEP 2106.05(h)).
Step 2B: Claim provides an Inventive Concept
“processing a query; parsing …”. These are identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "i. Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350, 1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network);" and thus remains insignificant extra-solution activity that does not provide significantly more.
“storing…;a query resolution model; a query resolution engine”. This is identified as insignificant extra-solution activity above when re-evaluated this element is well-understood, routine, and conventional as evidenced by the court cases in MPEP 2106.05(d)(II), "iv. Storing and retrieving information in memory, Versata Dev. Group, Inc. v. SAP Am., Inc., 793 F.3d 1306, 1334; i. … transmitting data over a network, …Symantec, 838 F.3d at 1321, 120 USPQ2d at 1362 (utilizing an intermediary computer to forward information); … OIP Techs., Inc., v. Amazon.com, Inc., 788 F.3d 1359, 1363, 115 USPQ2d 1090, 1093 (Fed. Cir. 2015) (sending messages over a network); buySAFE, Inc. v. Google, Inc., 765 F.3d 1350,
1355, 112 USPQ2d 1093, 1096 (Fed. Cir. 2014) (computer receives and sends information over a network)”.
“processor, non-transitory computer readable medium”, amount to elements that have been recognized as well-understood, routine, and conventional activity in particular fields, as demonstrate by: relevant court decision: the followings are example of the court decisions demonstrating well-understood, routine and conventional activities, See e.g., MPEP 2106.05(d)(II) and MPEP 2106.05(f)(2): computer readable storage media comprising instructions to implement a method, e.g., see versata Dev. Group, Inc. v SAP Am., Inc., 793 F.3d 1306, 1334, 115 USPQ2d 1681, 1701 (Fed. Cir. 2015).
The conclusions for the mere implementation using a computer are carried over and does not provide significantly more.
The claims as a whole, does not amount to significantly more than the abstract
idea itself. This is because the claims do not affect an improvement to the functioning
of a computer itself; and the claims do not move beyond a general link of the use of an
abstract idea to a particular technological environment.
Accordingly, claims are directed to an abstract idea.
Claim 3 recites the additional limitation. This additional limitation is recited at a high level of generality and would function in its ordinary capacity for parsing the third combined prompt, this additional element does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more. The same rationale applies claim 19.
Claims 4-7, recite the limitations. There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly.
Claim 8 recites the limitation. There is no additional elements recited so the claim does not provide a practical application and is not considered to be significantly. The same rationale applies claim 20.
Claim 9 recites the limitations. This additional element is recited at a high level of generality and does not integrate the integrate the judicial exception into a practical application and does not amount to significantly more.
Claims 12-16, recite the limitations. There are no additional elements recited so the claims do not provide a practical application and is not considered to be significantly.
Claims 1 and 3-9 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter.
- Claims 1 and 3-9 are system claims, wherein the claims do not positive recited a memory/computer. It is at best, for use with the system claims, where all of the elements would reasonably be interpreted by one of ordinary skill in light of the disclosure as software, such the system is software, per se. Therefor, renders the system at most software per se, failing to fall within a statutory category. Thus, in order to overcome this 35 USC § 101 rejection the claim needs to be amended to include physical computer hardware (i.e. a memory, a computer) to execute the software components. See MPEP § 2106.01
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1, 3-10, 12-17 and 19-20 are rejected under 35 U.S.C. 103 as being unpatentable over Fabian et a., (US 2024/0303423), hereinafter “Fabian”, in view of Nallapati et al., (US 12,007,988), hereinafter “Nallapati”, and further in view of Nikos et al., (hereinafter “Nikos”) article entitled “Query Resolution for Conversational Search with Limited Supervision” and Gaskill et al., (US 2018/0052913), hereinafter “Gaskill”.
Claim 1, Fabian discloses a method comprising:
- processing a query to determine an application field of the query based on a context of the query (par. [0023], receives a natural language (NL) input from a user in the context of a spreadsheet);
- generating a first prompt having a specific set of requirements, wherein the specific set of requirements defines a customizable format to be used for delivering a response to a user in reply to the query (par. [0007], [0023], [0027], [0032], [0116] and [0134], generates a prompt based on the natural language input and at least a portion of the spreadsheet, wherein the first prompt includes include prompt parameters such as a scope of the output and formatting rules for the output to control or direct the how the LLM can or should reply to the prompt, and wherein the application includes in its prompt to the LLM an instruction to interpret the inquiry in multiple ways and to generate suggestions based on the interpretations, wherein a general-purpose computing system into a special-purpose computing system customized to support an application service);
- generating a second prompt defining a customizable application specific workflow, wherein the customizable application specific workflow is associated with the application field of the query (par. [0007], [0023], [0027], [0032], [0124], generates a prompt based on the natural language input and at least a portion of the spreadsheet, wherein the second prompt includes a request for the LLM service to output a description of the formula; and preprocess the user input);
- generating a first combined prompt by combining context of the first prompt and context of the second prompt (par. [0007], [0023], [0027], [0032] and [0102], generates a prompt based on the natural language input and at least a portion of the spreadsheet, wherein the third prompt (combined prompt) sent if/when the application service receives user input indicating that the user would like to view an explanation of the formula, such as clicking a “Explain formula” hyperlink presented in the user interface).
- parsing the stored second combined prompt (par. [0056], [0114] and [0124], the prompt directs the LLM service to provide a suggestions in a parse-able output format, that is, in a format which facilitates extracting the components of the reply based on information type, wherein the application parses the suggestions to generate cards for display in task pane, LLM interface layer parses the input to generate an internal representation. LLM interface layer performs other steps, including evaluating the input against a content moderation engine via an application programming interface (API) and creating a column mapping by which substitute column headers are created and mapped to original column headers to replace the original column headers in the prompt to LLM, preprocessing also includes selecting a prompt or prompt format based on the type of input received and inserting the internal representation of the input into the prompt, the prompt generated based on preprocessing the input is submitted to LLM).
However, Fabian does not explicitly disclose a query resolution model for generating the response to the query.
Meanwhile, Nallapati discloses a query resolution model for generating the response to the query (col.25, lines 50-57, the natural language query is executed using the resolution, wherein an ambiguity detection is performed as part of a natural language query processing pipeline, where resolution is prompted and received for the natural language query before continuing to process the natural language query and return a result).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Fabian to use a query resolution model for generating the response to the query, in order to provide performance and analysis benefits for the data to result in different data sets being spread across different locations and types of storage systems as different data storage technologies offer different performance benefits and features.
On the other hand, Nikos discloses a query resolution model for generating the response to the query (page 1, col.1, par [1] and page 2, col.1-col.2, perform query resolution using the context from the conversational history is used to arrive at a better expression of the current turn query, defined as the task of query resolution).
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the system of Fabian. Since both Nallapati and Nikos are directed to query resolution model for generating the response to the query. It would be motivated to use such query resolution model of Nallapati and Nikos to provide Fabian with competitive performance.
The combination of Fabian, Nallapati and Nikos fails the claimed “generating a third prompt indicating a user specific requirement having an additional context specifying the user specific requirement; generating a second combined prompt by combining context of the first combined prompt and the context of the third prompt; and storing the second combined prompt in a memory”.
Meanwhile, Gaskill discloses generating a third prompt indicating a user specific requirement having an additional context specifying the user specific requirement (par. [0052], technical solutions provided by the present inventive subject matter allow users to communicate with an intelligent online personal assistant in a natural conversation. The assistant is efficient as over time it increasingly understands specific user preferences and is knowledgeable about a wide range of products; par. [0048]- [0049], the context manager 218 operates to manage the context and communication of a given user towards the bot and its AI. The context manager 218 comprises two parts: a long term history and a short term memory. Each context manager entry may describe the relevant intent and all parameters and all related results. The context is towards the inventory, as well as towards other, future sources of knowledge. The NLG component 212 operates to compose a natural language utterance out of an AI message to present to a user interacting with the intelligent bot; par. [0050] and [0077]- [0078], a user in response to machine-generated prompts from the dialog manager 216 in a multi-turn interactive dialog. This user-machine interaction may improve the efficiency and accuracy of one or more automated searches for the most relevant items available for purchase in an electronic marketplace. …If the user intent is shopping, it could relate to the pursuit of a specific shopping mission, gifting an item for a target recipient other than the user, or just to browse an inventory of items available for purchase. Once the high level intent is identified, the artificial intelligence framework 128 is tasked with determining what the user is looking for; that is, is the need broad (e.g., shoes, dresses) or more specific (e.g., two pairs of new black Nike™ size 10 sneakers) or somewhere in between (e.g., black sneakers); generating a second combined prompt by combining context of the first combined prompt and the context of the third prompt; and storing the second combined prompt in a memory par. [0087], generation of prompts that may educate the bot but annoy the user. Focus group studies have shown that some users do not want to provide more than a predetermined number, e.g., three, of replies to prompts, so each of those prompts should be as incisive as possible; par. [0088] The knowledge graph may be updated dynamically in some embodiments, for example by the AI orchestrator; par. [0095], generate a more relevant item recommendation without requiring a user prompt to acquire the same information; par. [0118] In one prompt generation strategy, the dialog manager 216 may proceed from the broadest category to a sub-category or attribute and then to an attribute value to determine a sequence of prompt topics, in that order. …This hierarchically guided search approach may appeal to users who do not want to answer more than a limited number of prompts to zero in on a relevant item; par. [0119], another prompt generation strategy, the dialog manager choose prompt topics more randomly from all unspecified attributes and attribute values that appear in the knowledge graph. Although this approach is somewhat undirected, it may be appropriate when a user is browsing an inventory, versus pursuing a specific shopping mission. Users who are not annoyed by chatting with an intelligent personal assistant system may prefer this more exploratory or conversational approach that in a sense wanders through the possibilities of the knowledge graph; par. [0124], the dialog manager may generate a prompt for additional user input that also states alternatives that are available in the knowledge graph and/or may have association values available in the knowledge graph; and par. [0133], the methodology may aggregate the analysis results into a formal query for searching. At 1312, the methodology may optionally generate a user prompt or prompts for additional input data from the user.)
Therefore, it would have been obvious to one having ordinary skill in the art before the effective filing date of the claimed invention to have modified the combined system of Fabian, Nallapati and Nikos to provide a distinct user-specific instruction set, merges it with previously combined contextual instructions to form a second-level composite, stores that composite in memory, and parses the stored composite with the query in a single pass through a resolution model, in order to improve operation of the intelligent personal assistant system overall by reducing mistakes, increasing the likelihood of correct divination of user intent underlying a user query and yielding faster and better targeted searches and item recommendations.
Claim 3, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Fabian discloses generating one or more intermediate prompts after the generation of the second prompt, wherein the one or more intermediate prompts are to specify additional context for the customizable application specific workflow, wherein the additional context is not specified in sub-modules of the customizable application specific workflow (par. [0007], [0023], [0027], [0032], generates a prompt based on the natural language input and at least a portion of the spreadsheet, wherein the second prompt includes a request for the LLM service to output a description of the formula);
generating a third combined prompt by combining context of the first combined prompt and the context of the one or more intermediate prompts (par. [0007], [0023], [0027], [0032] and [0102], generates a prompt based on the natural language input and at least a portion of the spreadsheet, wherein the third prompt (combined prompt) sent if/when the application service receives user input indicating that the user would like to view an explanation of the formula, such as clicking a “Explain formula” hyperlink presented in the user interface); and
parsing the third combined prompt (par. [0056] and [0114], the prompt directs the LLM service to provide a suggestions in a parse-able output format, that is, in a format which facilitates extracting the components of the reply based on information type, wherein the application parses the suggestions to generate cards for display in task pane).
Nallapati discloses query through the query resolution model for generating the response to the query (col.25, lines 50-57, the natural language query may be executed using the resolution, wherein an ambiguity detection is performed as part of a natural language query processing pipeline, where resolution is prompted and received for the natural language query before continuing to process the natural language query and return a result).
Claim 4, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Fabian discloses wherein the specific set of requirements comprises one or more of a specific length of the response, a specific purpose, specific regulatory requirements, disclaimers, and a general tone in which a response is to be delivered to the user (par. [0003], Given the broad range of functions and capabilities available in spreadsheet applications).
Claim 5, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Fabian discloses the query resolution model is an open artificial intelligence based model (par. [0003], special-purpose artificial intelligence (AI) models have been developed to aid users in figuring out how to accomplish particular tasks).
Claim 6, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Fabian discloses each of the first prompt, the second prompt, and the third prompt corresponds to a layer of the query resolution model (par. [0007], a first prompt includes a request for the LLM service to output a formula for the spreadsheet, and second prompt, immediately after the first prompt, includes a request for the LLM service to output a description of the formula. In some implementations, a third prompt, immediately after the second prompt, includes a request for the LLM service to output an explanation of the formula).
Claim 7, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Fabian discloses the response is one of an answer to the query, a newly generated text, a summarized text, and an analysis report (par. [0028], submit a request in the user interface of the application to summarize sales data which is listed in multiple columns of a data table of the user's spreadsheet).
Claim 8, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Fabian discloses evaluating the generated response by measuring one or more of coherence, relevance to context of the query, grammatical correctness, and factual accuracy against a prespecified standard (par. [0035], application requests the LLM categorize the multiple suggestions that it generates according to correctness, accuracy, appropriateness).
Claim 9, the combination of Fabian, Nallapati, Nikos and Gaskill discloses the invention as claimed. In addition, Nallapati discloses transforming context of the first combined prompt and the context of the query to a high-dimensional vector representing semantic and syntactic characteristics of the contexts (col.2, lines 28-56 and col.3, line 54-col.4, line 4, data sets are made up of large fact/dimension tables and many reporting views that aggregate and transform their data across various dimensions, wherein query assistance is provide nested ambiguity resolution by providing assistance prompt(s) to resolve complex queries and wherein the nested ambiguity resolution is transformed an initial natural language query); and
Fabian discloses parsing the high-dimensional vector through a vector space of the query resolution model for searching vector embeddings in the vector space close to the high-dimensional vector (par. [0056] and [0114], the prompt directs the LLM service to provide a suggestions in a parse-able output format, that is, in a format which facilitates extracting the components of the reply based on information type, wherein the application parses the suggestions to generate cards for display in task pane).
Claims 10 and 12-16, claims 10 and 12-16 are system for performing the method of claims 1 and 3-9. Therefore, they are rejected under the same rationale as applied to claims 1 and 3-9 above. In addition, Fabian discloses wherein the query resolution model is a Large Language Model (par. [0004]-[0007], a Large Language Model)
Claims 17 and 19-20, claims 17 and 19-20 are non-transitory computer readable medium claims having stored therein instructions for executing the method of claims 1 and 3-9. Therefore, they are rejected under the same rationale as applied to claims 1 and 3-9 above.
Conclusion
Applicant's amendment necessitated the new ground(s) of rejection presented in this Office action. Accordingly, THIS ACTION IS MADE FINAL. See MPEP § 706.07(a). Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any extension fee pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the date of this final action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to LOAN T NGUYEN whose telephone number is (571)-270-3103. The examiner can normally be reached on Monday from 10:00 am - 6:00 pm, Thursday-Friday from 10:00 am - 2:00 pm. If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Aleksandr Kerzhner can be reached on (571) 270-1760. The fax phone number for the organization where this application or proceeding is assigned is 571-270-4103. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the automated information system, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
06/02/2026
/LOAN T NGUYEN/Examiner, Art Unit 2165
/ALEKSANDR KERZHNER/Supervisory Patent Examiner, Art Unit 2165
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