DETAILED ACTION
The following is a Non-Final, First Office Action on the Merits in response to communications filed on May 26, 2025. Currently, claims 1–19 are pending.
Claim Objections
Claims 1, 3–5, 8–9, and 13 are objected to because of the following informalities:
Claim 1 recites “a plurality of unstructured survey parameters”. However, claim 1 subsequently recites “the plurality of survey parameters in the elements to “instantiate” (twice recited) and “adaptively modify”. Examiner recommends amending the claim to recite “the plurality of unstructured survey parameters in the elements to “instantiate” (twice recited) and “adaptively modify” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 3, 5, 8, and 13 recite “the prompts” in line 1. However, claims 1, 4, and 9, from which claims 3, 5, 8, and 13 depend, previously recite “a series of prompts”. Examiner recommends amending claims 3, 5, and 13 to recite “the series of prompts” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Claims 4 and 9 recite “the survey parameters” in the element for “modifying”. However, claims 4 and 9 previously recite “the plurality of unstructured survey parameters”. Examiner recommends amending the claims to recite “the plurality of unstructured survey parameters” in the element for “modifying” in order to avoid issues of clarity under 35 U.S.C. 112(b).
Appropriate correction is required.
Claim Rejections - 35 USC § 112(b)
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 1–19 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.
Claim 1 recites an element for “upon receiving input … convert”. However, claim 1 previously recites “a first natural language input”, “a second natural language input” and “a third input”. Examiner submits that the scope of the claim is indefinite because it is unclear whether Applicant intends for the recited “input” to reference the first, second, or third inputs or intends to introduce a fourth “input”.
For purposes of examination, claim 1 is interpreted as reciting “upon receiving the third input … converting”.
Claim 1 further includes two recitations of “the modified plurality of survey parameters” in the element for “upon receiving input … convert”. There is insufficient antecedent basis for these limitations in the claim.
For purposes of examination, claim 1 is interpreted as reciting “adaptively modify the instance of the survey by modifying the plurality of survey parameters with the one or more unstructured survey parameter modifications to generate a modified plurality of survey parameters”.
In view of the above, claim 1 is rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 2–3, which depend from claim 1, inherit the deficiencies described above. As a result, claims 2–3 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 3 recites the “method of claim 1”. However, claim 1 recites a system claim. As a result, the scope of claim 3 is indefinite because the statutory class of claim 3 it is unclear. For purposes of examination, claim 3 is interpreted as reciting the “system of claim 1”.
Claims 4 and 9 recite an element for “upon receiving input relating to the completion of the survey … converting”. However, claims 4 and 9 previously recite “input relating to completion of the survey”. Examiner submits that the scope of the claims is indefinite because it is unclear whether Applicant intends for the second recitation of “input” to reference the first recitation or intends to introduce a second, different “input”.
For purposes of examination, claims 4 and 9 are interpreted as reciting an element for “upon receiving the input relating to the completion of the survey … converting”.
Claims 4 and 9 further include two recitations of “the modified survey parameters” in the element for “upon receiving input … converting”. There is insufficient antecedent basis for these limitations in the claims.
For purposes of examination, claims 4 and 9 are interpreted as reciting “modifying, at the processor, the survey parameters based on the one or more modifications to the survey to generate modified survey parameters”.
In view of the above, claims 4 and 9 are rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claims 5–8 and 10–19, which depend from claims 4 and 9, inherit the deficiencies described above. As a result, claims 5–8 and 10–19 are similarly rejected under 35 U.S.C. 112(b) as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor regards as the invention.
Claim 8 recites “the selected large language model” in line 2. Although claim 7, from which claim 8 depends, previously recites “selecting the first language model or the second language model”, there is insufficient antecedent basis for “the selected large language model” in claim 8.
For purposes of examination, claim 8 is interpreted as reciting “designing and customizing the series of prompts for the first language model or the second language model.”
Claim 18 recites “the collected data” in line 5. There is insufficient antecedent basis for this limitation in the claim.
For purposes of examination, claim 18 is interpreted as reciting “[[the]] collected data.”
In view of the above, Examiner respectfully requests that Applicant thoroughly review the claims for compliance with the requirements set forth under 35 U.S.C. 112(b).
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 1–19 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Specifically, claims 1–19 are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea.
With respect to Step 2A Prong One of the framework, claim 1 recites an abstract idea. Claim 1 includes elements to “receive a first natural language input, the first natural language input relating to: one or more survey goals; a selection of one or more survey templates; a set of guardrails; and/or an initial question”; “translate the first natural language input into a plurality of unstructured survey parameters”; “an editable version of a survey based on the plurality of survey parameters, said editable version of the survey comprising the plurality of survey parameters”; “receive a second natural language input relating to one or more modifications to the survey”; “translate the one or more modifications to one or more unstructured survey parameter modifications”; “adaptively modify the instance of the survey by modifying the plurality of survey parameters with the one or more unstructured survey parameter modifications”; “receive a third input relating to completion of generation of the survey”; and “upon receiving input relating to the completion of generation of the survey, convert, the survey into a conversational survey using the modified plurality of survey parameters by rendering the modified plurality of survey parameters into a series of prompts for use.”
The limitations above recite an abstract idea. More particularly, the elements above recite certain methods of organizing human activity for commercial advertising, marketing or sales activities or behaviors because the elements describe a process for generating and editing a survey. Further, the elements recite mental processes because the elements describe observations or evaluations that can be practically performed in the mind or by a human using pen and paper. As a result, claim 1 recites an abstract idea under Step 2A Prong One.
Claims 4 and 9 include substantially similar limitations to those included with respect to claim 1. As a result, claims 4 and 9 recite an abstract idea under Step 2A Prong One for the same reasons as stated above with respect to claim 1.
Claims 2–3, 5–8, and 10–19 further describe the process for generating and editing a survey and further recite certain methods of organizing human activity and/or mental processes for the same reasons as stated above. As a result, claims 2–3, 5–8, and 10–19 recite an abstract idea under Step 2A Prong One.
With respect to Step 2A Prong Two of the framework, claim 1 does not include additional elements that integrate the abstract idea into a practical application. Claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a processor, a large language model, a graphical user interface, and a function to instantiate. When considered in view of the claim as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional computer elements are generic computer components that are merely used as a tool to perform the recited abstract idea, and the large language model and function to instantiate do no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claim 1 does not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
As noted above, claims 4 and 9 include substantially similar limitations to those included with respect to claim 1. Although claims 4 and 9 additionally recite an element for “displaying” on the graphical user interface, the additional element does no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 4 and 9 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 15–19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include an artificial intelligence engine. When considered in view of the claims as a whole, the additional elements do not integrate the abstract idea into a practical application because the additional element does no more than generally link the use of the recited abstract idea to a particular technological environment. As a result, claims 15–19 do not include additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two.
Claims 2–3, 5–8, and 10–14 do not include any additional elements beyond those included with respect to the claims from which claims 2–3, 5–8, and 10–14 depend. As a result, claims 2–3, 5–8, and 10–14 do not include any additional elements that integrate the abstract idea into a practical application under Step 2A Prong Two for the same reasons as stated above.
With respect to Step 2B of the framework, claim 1 does not include additional elements amounting to significantly more than the abstract idea. As noted above, claim 1 includes additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include a processor, a large language model, a graphical user interface, and a function to instantiate. The additional elements do not amount to significantly more than the recited abstract idea because the additional computer elements are generic computer components that are merely used as a tool to perform the recited abstract idea, and the large language model and function to instantiate do no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claim 1 does not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
As noted above, claims 4 and 9 include substantially similar limitations to those included with respect to claim 1. Although claims 4 and 9 additionally recite an element for “displaying” on the graphical user interface, the additional element does no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 4 and 9 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 15–19 include additional elements that do not recite an abstract idea under Step 2A Prong One. The additional elements include an artificial intelligence engine. The additional elements do not amount to significantly more than the recited abstract idea because the additional element does no more than generally link the use of the recited abstract idea to a particular technological environment. Further, looking at the additional elements as an ordered combination adds nothing that is not already present when considering the additional elements individually. As a result, claims 15–19 do not include additional elements that amount to significantly more than the recited abstract idea under Step 2B.
Claims 2–3, 5–8, and 10–14 do not include any additional elements beyond those included with respect to the claims from which claims 2–3, 5–8, and 10–14 depend. As a result, claims 2–3, 5–8, and 10–14 do not include any additional elements that amount to significantly more than the recited abstract idea under Step 2B for the same reasons as stated above.
Therefore, the claims are directed to an abstract idea without additional elements amounting to significantly more than the abstract idea. Accordingly, claims 1–19 are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
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, 3–5, 9–13, and 15–16 are rejected under 35 U.S.C. 103 as being unpatentable over Long et al. (U.S. 2020/0074294) in view of LEVITAN et al. (U.S. 2024/0403904).
Claims 1, 4, and 9: Long discloses an artificially intelligent system for adaptively generating a customized natural language survey (See Abstract), the system comprising:
a processor operating in tandem with the model (See paragraph 40, in view of paragraphs 28 and 49, wherein the processor operates with respect to an AI-based neural network);
a graphical user interface operable to receive a first natural language input, the first natural language input relating to: one or more survey goals; a selection of one or more survey templates; a set of guardrails; and/or an initial question (See FIG. 4 and paragraphs 47 and 52, wherein an initial question is input into the interface; paragraphs 26 and 47, wherein a question intent is determined from the textual input, such that the initial textual input is related to one or more survey goals; and paragraphs 53–54, wherein the initial survey question textual input relates to survey questions in a correlation database, and wherein the stored questions are a source document);
the processor operable to: translate the first natural language input into a plurality of unstructured survey parameters (See FIG. 4 and paragraphs 48–49, in view of paragraphs 79 and 84, wherein the textual inputs are translated into an editable survey template and rendered on an interface);
instantiate an instance of an editable version of a survey based on the plurality of survey parameters, said instance of the editable version of the survey comprising the plurality of survey parameters (See FIG. 4 and paragraphs 48–49, in view of paragraphs 79 and 84, wherein the textual inputs are translated into an editable survey template and rendered on an interface);
receive, at the graphical user interface, a second natural language input relating to one or more modifications to the survey (See paragraph 71, wherein the system operates for any created question within the survey creation process; see also paragraphs 83–84, wherein the system detects any user edits to suggested survey questions or answer choices);
translate the one or more modifications to one or more unstructured survey parameter modifications (See paragraph 71, wherein the system operates for any created question within the survey creation process to suggest questions and design modifications; see also paragraphs 83–84, wherein the system detects any user edits to suggested survey questions or answer choices);
adaptively modify the instance of the survey by modifying the plurality of survey parameters with the one or more unstructured survey parameter modifications (See paragraph 71, wherein the system operates for any created question within the survey creation process to suggest questions and design modifications; see also paragraphs 83–84, wherein the system detects any user edits to suggested survey questions or answer choices);
receive, at the graphical user interface, a third input relating to completion of generation of the survey (See paragraph 153, wherein a survey draft is finished, and wherein detecting inputs until a draft is finished implicitly discloses an indication of completion); and
upon receiving input relating to the completion of generation of the survey, convert the instance of the survey into a survey using the modified plurality of survey parameters by rendering the modified plurality of survey parameters into a series of prompts (See paragraph 44, in view of paragraph 153, wherein the created survey is distributed to recipients). Long does not expressly disclose the remaining claim elements.
Levitan discloses a processor operating in tandem with the large language model (See FIG. 5); and
upon receiving input relating to the generation of the survey, convert, at the large language model, the instance of the survey into a conversational survey using the modified plurality of survey parameters by rendering the modified plurality of survey parameters into a series of prompts for use with the large language model (See FIG. 4–5 and paragraph 14, wherein an LLM survey is trained, such that survey parameters are modified, and wherein the LLM survey generates a conversational survey).
Long discloses a system directed to creating a survey using machine learning techniques. Levitan discloses a system directed to generating LLM-based survey prompts. Each reference discloses a system directed to creating survey prompts. The technique of utilizing a large language model is applicable to the system of Long as they each share characteristics and capabilities; namely, they are directed to creating survey prompts.
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Levitan to the teachings of Long would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate survey prompt creation into similar systems. Further, applying a large language model to Long would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
With respect to claims 4 and 9, Long further discloses displaying, at the graphical user interface, the editable version of the survey (See FIG. 4).
Claims 3, 5, and 13: Although Long discloses designing and/or customizing the prompts (See paragraphs 45–46 and 71, wherein prompts are designed/customized). Long does not expressly disclose the remaining claim elements.
Levitan discloses wherein the prompts are designed and/or customized for the large language model (See paragraph 20, in view of paragraph 43, wherein the LLM is trained to provide prompts and determine subsequent prompts).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 10: Long discloses the method of claim 9 wherein the source document is a source text (See paragraphs 53–54, wherein the initial survey question textual input relates to survey questions in a correlation database).
Claim 11: Long discloses the method of claim 9 wherein the source document is an informational text (See paragraphs 53–54, wherein the initial survey question textual input relates to survey questions in a correlation database).
Claim 12: Long discloses the method of claim 9 wherein the survey includes one or more discussion questions (See paragraph 46, wherein open-ended questions are disclosed).
Claim 15: Long discloses the method of claim 9 further comprising: adaptively detecting, at an artificial intelligence engine, one or more recommendations and/or suggestions to a design of the survey (See paragraphs 25–26, in view of paragraph 49, wherein suggested survey questions are displayed on the administrator interface using an AI neural network); and
displaying, on the graphical user interface, the one or more recommendations and/or suggestions to the design of the survey (See paragraphs 25–26, in view of paragraph 49, wherein suggested survey questions are displayed on the administrator interface using an AI neural network). Long does not expressly disclose the remaining claim elements.
Levitan discloses a large language model (See Abstract).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claim 16: Long discloses the method of claim 9, further comprising: adaptively detecting, at an artificial intelligence engine, one or more suggestions, said one or more suggestions comprising one or more additional survey questions, said one or more additional survey questions designed to obtain targeted results from the survey, said targeted results that align with the one or more survey goals (See paragraphs 25–26, in view of paragraph 49, wherein suggested survey questions determined based on an intent and are displayed on the administrator interface using an AI neural network); and
displaying, on the graphical user interface, the one or more suggestions (See paragraphs 25–26, in view of paragraph 49, wherein suggested survey questions determined based on an intent and are displayed on the administrator interface using an AI neural network). Long does not expressly disclose the remaining claim elements.
Levitan discloses a large language model (See Abstract).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 2, 6–8, and 14 are rejected under 35 U.S.C. 103 as being unpatentable over Long et al. (U.S. 2020/0074294) in view of LEVITAN et al. (U.S. 2024/0403904), and in further view of Shan et al. (U.S. 12,050,854).
Claims 2, 6, and 14: As disclosed above, Long and Levitan disclose the elements of claim 1.
Long further discloses the system of claim 1 further comprising a first model and a second model (See paragraph 28, wherein the system may use several models). Long does not expressly disclose the remaining claim elements.
Levitan discloses a large language model (See Abstract).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Long and Levitan do not expressly disclose the remaining claim elements.
Shan discloses a test harness, said test harness operable to compare a quality of survey results obtained using a first model to a quality of survey results obtained using a second model (See col. 7, l. 57–col. 8, l. 14, in view of col. 7, ll. 15–29, wherein versions of model techniques are compared to compare survey results).
As disclosed above, Long discloses a system directed to creating a survey using machine learning techniques, and Levitan discloses a system directed to generating LLM-based survey prompts. Shan discloses a system directed to administering conversational surveys. Each reference discloses a system directed to managing survey prompts. The technique of utilizing a test harness is applicable to the systems of Long and Levitan as they each share characteristics and capabilities; namely, they are directed to managing survey prompts.
One of ordinary skill in the art would have recognized that applying the known technique of Shan would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Shan to the teachings of Long and Levitan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate survey prompt management into similar systems. Further, applying a test harness to Long and Levitan would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 7: Although Long discloses first and second models (See citations above) and Levitan discloses a large language model (See citations above), Long and Levitan do not expressly disclose the remaining claim elements.
Shan discloses selecting the first model or the second model (See col. 7, l. 57–col. 8, l. 14, in view of col. 7, ll. 15–29, wherein versions of model techniques are compared, and wherein the better performing technique is implicitly selected).
One of ordinary skill in the art would have recognized that applying the known technique of Shan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 2.
Claim 8: Although Long discloses designing and/or customizing the prompts (See paragraphs 45–46 and 71, wherein prompts are designed/customized). Long does not expressly disclose the remaining claim elements.
Levitan discloses designing and customizing the prompts for the selected large language model (See paragraph 20, in view of paragraph 43, wherein the LLM is trained to provide prompts and determine subsequent prompts).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1.
Claims 17–19 are rejected under 35 U.S.C. 103 as being unpatentable over Long et al. (U.S. 2020/0074294) in view of LEVITAN et al. (U.S. 2024/0403904), and in further view of Choi et al. (KR 20230084635).
Claim 17: As disclosed above, Long and Levitan disclose the elements of independent claim 9.
Long discloses the method of claim 9, further comprising: perusing, by an artificially intelligent engine, a design of the survey (See paragraphs 25–26, in view of paragraph 49, wherein the survey design is evaluated using an AI neural network). Long does not expressly disclose the remaining claim elements.
Levitan discloses a large language model (See Abstract).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Long and Levitan do not expressly disclose the remaining claim elements.
Choi discloses adaptively generating one or more analysis processes to execute on survey data to be collected (See elements 151–153, wherein a questionnaire similarity analyzer 151 is used to compare questionnaires and extract properties of the questionnaire, an AI verification questionnaire generation unit 152 identifies questionnaire answers of similar questionnaire items, and a reliability evaluation unit 153 “performs verification on the corresponding survey responder terminal 10 using the verification questions generated by the AI verification questionnaire generator 152,” thereby analyzing survey data according to identified properties).
As disclosed above, Long discloses a system directed to creating a survey using machine learning techniques, and Levitan discloses a system directed to generating LLM-based survey prompts. Choi discloses a system directed to constructing questionnaires and verifying answer reliability. Each reference discloses a system directed to constructing surveys. The technique of generating an analysis process for collected data is applicable to the systems of Long and Levitan as they each share characteristics and capabilities; namely, they are directed to constructing surveys.
One of ordinary skill in the art would have recognized that applying the known technique of Choi would have yielded predictable results and resulted in an improved system. It would have been recognized that applying the technique of Choi to the teachings of Long and Levitan would have yielded predictable results because the level of ordinary skill in the art demonstrated by the references applied shows the ability to incorporate survey construction into similar systems. Further, applying a technique for generating analysis process for collected data to Long and Levitan would have been recognized by those of ordinary skill in the art as resulting in an improved system that would allow more detailed analysis and more reliable results.
Claim 18: Long discloses the method of claim 9, further comprising: perusing, by an artificially intelligent engine operating on the large language model, a design of the survey (See paragraphs 25–26, in view of paragraph 49, wherein the survey design is evaluated using an AI neural network). Long does not expressly disclose the remaining claim elements.
Levitan discloses a large language model (See Abstract).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Long and Levitan do not expressly disclose the remaining claim elements.
Choi discloses adaptively generating one or more research queries, said one or more research queries operable to extract themes from the collected data (See elements 151–153, wherein a questionnaire similarity analyzer 151 is used to compare questionnaires and extract properties of the questionnaire, an AI verification questionnaire generation unit 152 identifies questionnaire answers of similar questionnaire items, and a reliability evaluation unit 153 “performs verification on the corresponding survey responder terminal 10 using the verification questions generated by the AI verification questionnaire generator 152,” thereby querying the collected data to match themes from valid questionnaires).
One of ordinary skill in the art would have recognized that applying the known technique of Choi would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 17.
Claim 19: Long discloses the method of claim 9, further comprising: perusing, prior to the survey being communicated to one or more survey participants, by an artificially intelligent engine operating on the large language model, a design of the survey (See paragraphs 25–26, in view of paragraph 49, wherein the survey design is evaluated using an AI neural network). Long does not expressly disclose the remaining claim elements.
Levitan discloses a large language model (See Abstract).
One of ordinary skill in the art would have recognized that applying the known technique of Levitan would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 1. Long and Levitan do not expressly disclose the remaining claim elements.
Choi discloses adaptively generating one or more research queries, said one or more research queries operable to extract themes from data to be collected upon communication of the survey to the one or more survey participants (See elements 151–153, wherein a questionnaire similarity analyzer 151 is used to compare questionnaires and extract properties of the questionnaire, an AI verification questionnaire generation unit 152 identifies questionnaire answers of similar questionnaire items, and a reliability evaluation unit 153 “performs verification on the corresponding survey responder terminal 10 using the verification questions generated by the AI verification questionnaire generator 152,” thereby querying the collected data to match themes from valid questionnaires).
One of ordinary skill in the art would have recognized that applying the known technique of Choi would have yielded predictable results and resulted in an improved system for the same reasons as stated above with respect to claim 17.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1–19 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1–3 and 10–25 of U.S. Patent No. 12,314,969. Although the claims at issue are not identical, they are not patentably distinct from each other because claims 1–3 and 10–25 of U.S. Patent No. 12,314,969 anticipate every element of pending claims 1–19.
Pending Claims
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Conclusion
The following prior art is made of record and not relied upon but is considered pertinent to applicant's disclosure:
CHAUDHRY et al. (U.S. 2022/0300993) discloses a system directed to conducting a survey using a survey bot; and
BOWER et al. (U.S. 2023/0206262) discloses a system directed to using AI to create a customer survey and evaluation survey results.
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/WILLIAM S BROCKINGTON III/Primary Examiner, Art Unit 3623
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