DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Claim Interpretation
As per Claim 1 (and similarly Claim 11):
“the query” in line 12 of claim 1, in line 13 of claim 1, and in the 3rd to last line of claim 1 is interpreted as referring to “a query” in line 6 of claim 1 (not to any of “exemplary queries”)
“the updated query” in the last line of claim 1 is interpreted as referring to the product of “adjust the query” in the 3rd to last line of claim 1.
As per Claims 2-6 and 12-16:
“the contextual data” is interpreted as referring to “contextual data” in “receive contextual data” in line 5 of claim 1 and “receiving… contextual data” in line 2 of claim 11 (not to “exemplary contextual data”)
Claim Rejections - 35 USC § 112
The following is a quotation of the first paragraph of 35 U.S.C. 112(a):
(a) IN GENERAL.—The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor or joint inventor of carrying out the invention.
The following is a quotation of the first paragraph of pre-AIA 35 U.S.C. 112:
The specification shall contain a written description of the invention, and of the manner and process of making and using it, in such full, clear, concise, and exact terms as to enable any person skilled in the art to which it pertains, or with which it is most nearly connected, to make and use the same, and shall set forth the best mode contemplated by the inventor of carrying out his invention.
Claims 1-20 are rejected under 35 U.S.C. 112(a) or 35 U.S.C. 112 (pre-AIA ), first paragraph, as failing to comply with the written description requirement. The claim(s) contains subject matter which was not described in the specification in such a way as to reasonably convey to one skilled in the relevant art that the inventor or a joint inventor, or for applications subject to pre-AIA 35 U.S.C. 112, the inventor(s), at the time the application was filed, had possession of the claimed invention.
As per Claim 1 (and similarly claim 11):
The original Specification (i.e. the original Specification of Parent Application 18/426,617, where this application is a continuation and not a continuation-in-part) does not have written description for:
adjust the query as a function of the query machine learning model trained with the updated query training data; and display the return and the updated query using a display device (The original Specification [see e.g. col. 11, line 62 – col. 12, line 23 of US Patent 12,124,966] describes where query training data may be updated as a function of input and output results of “any other machine-learning model mentioned throughout this disclosure” [which includes the “tonal adjustment machine learning model 128”], and where a query is generated using a query machine learning model, but the original Specification does not describe where any generated query is updated/adjusted based on “the query machine learning model trained with the updated query training data” and where any updated/adjusted query is also displayed).
The dependent claims include the issues of their respective parent claims.
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 1-10 and 18 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 applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
As per Claim 1:
“the output of the tonal adjustment machine learning model” in the 5th to last line to the 4th to last line of claim 1 lacks antecedent basis.
As per Claim 8 (and similarly claim 18):
“the output of the emotional analysis machine learning model” lacks antecedent basis.
The dependent claims include the issues of their respective parent claims.
Allowable Subject Matter
The following is a statement of reasons for the indication of allowable subject matter:
As per Claim(s) 1 (and similarly claim[s] 11, and consequently claim[s] 2-10 and 12-20 which depend on claim[s] 1 and 11), the prior art of record does not teach or suggest the combination of all limitations in claim(s) 1, including (i.e. in combination with the remaining limitations in claim[s] 1) An apparatus for generating a text output, the apparatus comprising: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive contextual data; generate a query as a function of the contextual data using a query machine learning model, wherein generating the query comprises: creating query training data, wherein the query training data comprises exemplary contextual data correlated to exemplary queries; training the query machine learning model using the query training data; and generating the query using the query machine learning model; receive a query response as a function of the query; generate a return as a function of the query response; generate a tonal adjustment engine, wherein generating the tonal adjustment engine comprises: creating tonal adjustment training data, wherein the tonal adjustment training data comprises exemplary contextual data correlated to exemplary queries; training a tonal adjustment machine learning model using the tonal adjustment training data; and generating the tonal adjustment engine as a function of the tonal adjustment machine learning model; update the query training data as a function of the output of the tonal adjustment machine learning model; adjust the query as a function of the query machine learning model trained with the updated query training data; and display the return and the updated query using a display device.
2006/0047635 teaches “The method includes analyzing an event associated with the user to determine a contextual setting, dynamically generating a search query based on the contextual setting, and searching at least one information source using the search query to generate a search result. Additionally, the method includes calculating an importance value for each item of the search result, sorting the items of the search result according the importance value, and displaying the sorted search result to the user” (paragraph 5). This reference suggests receiving contextual data (determining a contextual setting) generating a query as a function of the contextual data (generating a search query based on the contextual setting) receiving a query response as a function of the query (receiving, from a generating of a search result, the search result based on searching at least one information source using the query) generating a return as a function of the query response (generating a sorted search result based on the search result) and displaying the return using a display device (displaying the sorted search result).
9031845 teaches “receive a natural language utterance associated with a user; perform speech recognition on the natural language utterance; parse and interpret the speech recognized natural language utterance; determine a domain and a context that are associated with the parsed and interpreted natural language utterance; formulate a command or query based on the domain and the context” (claim 1).
2021/0103606 teaches “For example, the training data set may comprise a first pair with a first query “What is the weather like in New York?” and a second query “How about in D.C.?” and a corresponding replace flag (e.g., because the user's intent is to ask “What is the weather like in D.C.?” by replacing New York with D.C. in the first query), and a second pair with a first query “What are some Tom Cruise movies?” and a second query “Are any action movies?” and a corresponding merge flag (e.g., because the user's intent is to ask “What are some Tom Cruise action movies?” and therefore the queries should be merged to update the context)” (paragraph 8). This reference teaches queries as part of pairs but does not appear to teach contextual data-query example pairs.
2023/0259713 teaches “The tone modification engine 116 may provide intelligent rewrite suggestions that modify the tone of the original segment. The tone modification service provided by the tone modification engine 116 116 may be provided within an enterprise and/or globally for a group of users. The tone modification engine 116 may operate to receive one or more detected and/or desired tones for a content segment as well as a detected content environment, examine the segment, examine the remining content of the document and/or examine context and non-linguistic features of the document to intelligently suggest one or more rewrite suggestions that change the tone of the segment from the detected tone to a different tone. The tone modification service may be provided by one or more rephasing ML models, as further discussed below with regards to FIG. 1B” (paragraph 30) and “One or more ML models used by the environment detection engine 136, tone detection engine 114 and/or the tone modification engine 116 may be trained by a training mechanism 118” (paragraph 34) and “In addition to the improper tone detection model 154, the tone modification engine 116 may include one or more rephrasing models 160. Each rephrasing model 160 may include one or more ML models that enable rephrasing the segment to modify the tone from a detected tone to a desired tone. For example, the rephrasing models 160 may include one rephrasing model for rephrasing the segment in a manner that modifies the tone of the segment from informal to formal. Another rephrasing model may rephrase the segment from angry to neutral. Yet another rephrasing model may rephrase the segment from impolite to polite. In some implementations, a rephrasing model can modify the segment to convey a desired tone regardless of its detected current tone(s). For example, one model may be used to rephrases all segments having a variety of tones to convey a formal tone. Another model may be used to rephrase all segments such that they convey a neutral tone, and the like. Thus, rephrasing models may provide one or more suggested rephrases that modify a segment to convey a desired tone (e.g. polite, neutral, formal, etc.)” (paragraph 55). This reference does not appear to describe training a tone modification engine with training data that comprises exemplary contextual data correlated to exemplary queries.
Upon further search (in response to the amendment filed 5/30/2024 for Application 18/426,617):
2021/0034705 teaches “Example 18. The system of any of examples 15-17, wherein: the tone modification system is trained using non-parallel training data that includes a plurality of input digital content and a corresponding plurality of input tones, such that each digital content of the plurality of digital contents has a corresponding tone of the plurality of tones; and the training data is non-parallel in that it lacks, for a first input digital content of the plurality of digital contents of the training data, any corresponding first output digital content” (paragraph 128)
2021/0397793 teaches “To provide ongoing training, the training mechanism 118 may also use training data sets received from each of the trained ML models (models included in the tone detection service 114 and the tone modification service 116). Furthermore, data may be provided from the training mechanism 118 to the data store 132 to update one or more of the training data sets in order to provide updated and ongoing training” (paragraph 53).
2022/0300716 teaches “The training data 408 is provided as the tabular representation 400A for training of classification-based machine learning model (also referred as first pre-trained machine learning model) used in AI based hierarchical multi-conversation system 102. For example, the user query “Please provide hyperparameter names for linear regression model?” in training data 408 is mapped to a category/sub topic 406 of “regression models” under topic or node name 404 of “machine learning models”” (paragraph 74).
Upon further search (in response to the filing of Application 18/887,380):
No additional references were found that taught or suggested where tonal adjustment training data comprising exemplary contextual data (e.g. age, gender, personal information, weight, height, geographic location, insurance, or history) correlated to exemplary queries/questions/requests/commands/utterances/user-inputs/customer-inputs/client-inputs are used to train a tonal adjustment machine learning model.
Double Patenting
For clarity of the record, NO Double Patenting rejections are required between the claims of Parent Patent 12,124,966, hereafter Parent Patent 1, and the claims of this application because the claims of Parent Patent 1 do not teach or suggest wherein generating the query comprises: creating query training data, wherein the query training data comprises exemplary contextual data correlated to exemplary queries; training the query machine learning model using the query training data; and generating the query using the query machine learning model; and generating the tonal adjustment engine as a function of the tonal adjustment machine learning model and update the query training data as a function of the output of the tonal adjustment machine learning model; adjust the query as a function of the query machine learning model trained with the updated query training data; and display the return and the updated query using a display device (where the underlined portion is new matter as discussed in the 112[a] rejections, above)
Conclusion
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EY 5/29/2026
/ERIC YEN/ Primary Examiner, Art Unit 2658
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