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 .
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed 01/07/2026 in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 05/06/2026 has been entered.
The following action is in response to the amendment/remarks of 05/06/2026.
By the amendment of 05/06/2026, claims 1-7 and 15-17 have been amended. Claims 8-14 and 18-20 have been canceled. Claims 21-30 have been newly added.
Claims 1-7, 15-17 and 21-30 are pending and have been considered below.
Response to Arguments/Amendment
Applicant remarks (Remarks 05/06/2026 pages 6-8) that the claim amendment overcomes the 35 USC 101 rejection of claims 1-20 (Final Rejection 10/07/2025 pages 5-21). The Examiner respectfully disagrees and the arguments are not persuasive.
Applicant argues I. that the amended claims are not directed to an abstract idea, II. that the amended claims recite a technical solution to a technical problem, and III. that the amended claims recite significantly more than an abstract idea. The Examiner notes that the analysis under 35 USC 101 as to whether a claimed invention is directed to an abstract idea without reciting significantly more is a multistep process (Step 1, Step 2A and Step 2B).
Step 2A is a two-prong inquiry (MPEP 2106.04). In Prong One, the claim is analyzed to determine whether or not an abstract idea is recited, and not whether the steps involved are directed to a specific, practical improvement in computer-implemented communication systems as alleged by Applicant. As previously discussed and currently maintained in light of the amendment, the amended independent claims clearly recite limitations directed to the abstract idea of certain methods of organizing human activity such as fundamental economic principles or managing interactions between people (MPEP 2106.04(a)(2)(II)), such as an iterative process of constructing a survey to better gather information from people in the context of marketing.
Once an abstract idea is established, Step 2A continues to Prong Two, wherein a determination is made on whether the claim recites additional elements that integrate the judicial exception into a practical application; herein, the additional elements of the claim are analyzed. While Applicant has highlighted potential solutions to the problem of ineffective information gather in conversational interfaces due to closed-ended or biased queries, the Examiner notes that the additional elements relied on remain generic. Simply adding a chat interface or a trained machine learning model that provides outputs based on inputs, without providing additional details on how the chat interface or trained machine learning model operate to provide a technical improvement over prior art systems, is insufficient to overcome the analysis provided. Further, as in the case of the dependent claims, reciting a solution without providing required essential technical details also amounts to generic recitation.
After performing the analysis required in Steps 1 and 2A, analysis continues to Step 2B (MPEP 2106.05), wherein a determination is made on whether the claim amounts to significantly more. As presented in the 35 USC 101 rejections below (updated for the amendment), the use of chat interfaces and input/output of the machine learning model and generation of communications do not amount to more than data gathering steps, which have been determined to be insignificant extra-solution activities (MPEP 2106.05(g)). Further, the recitations of a computer and the trained machine learning model do not amount to more than instructions to implement the abstract idea using generic computing tools (MPEP 2106.05(f)).
Applicant’s arguments with respect to claims 1-7, 15-17 and 21-30 have been considered but are moot because the new ground of rejection does not rely on any reference applied in the prior rejection of record for any teaching or matter specifically challenged in the argument.
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 21-23 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.
Regarding claims 21-23, each claim recites the limitation "the query is generated by" in line 1. There is insufficient antecedent basis for this limitation in the claim.
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 1-7, 15-17, 21-22 and 24-28 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1, 3, 19 and 20 of U.S. Patent No. 11,615,144 [‘144] in view of Burningham et al., US 2016/0370954 A1 [Burningham].
Regarding instant claim 1, claim 1 of ‘144 discloses a computer implemented method of generating query suggestions, the method comprising:
identifying a query from an interface (“classifies closed ended queries”), the query including a question (“defining an intent for detecting closed ended queries”);
receiving an indication that identifies whether the question is closed ended by providing the query to a machine learning model (“classifies closed ended queries”), the machine learning model being configured to classify closed ended queries based on queries provided to the machine learning model (“providing a plurality of queries that are closed ended queries to a machine learning model generator, said plurality of queries comprising training data;” .. ”generating a model that classifies closed ended queries as a function of the training data”).
Claim 1 of ‘144 fails to disclose wherein the identified query requests information from a first user, and that in response to the indication: generating a new query that includes a new question that is open-ended, the new query generated based on the question in the query; and presenting the new query to the first user in the interface.
Burningham discloses methods for identifying a question type of a query for a first user (¶35). Particularly, Burningham discloses identifying whether the question is closed-ended and generating a new query including a new open-ended question and presenting it to the first user (¶35, 55). Burningham further discloses that the first user may be a testing user (¶98-99). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of claim 1 of ‘144 and Burningham before them before the effective filing of the claimed invention to combine the generating a new open-ended question query and presenting to a user from whom a closed-ended question was queried, as taught by Burningham, with the identification and classification of a test query using the model of claim 1 of ‘144. One would have been motivated to make this combination in order to present more appropriate questions to users, eliciting additional more accurate information, as suggested by Burningham (¶55).
Regarding instant claim 2, claim 1 of ‘144 and Burningham disclose method of instant claim 1, and Burningham further discloses wherein the new query is selected from a set of queries in a table (¶42, ¶54-55, ¶51: results database).
Regarding instant claim 3, claim 1 of ‘144 and Burningham discloses the method of instant claim 2, and Burningham further discloses wherein the new query is generated to elicit more information than information received responsive to the query associated with the received indication (¶55: add open ended question after closed ended question to get open ended response).
Regarding instant claim 4, claim 1 of ‘144 and Burningham disclose the method of instant claim 3, and Burningham further discloses wherein the new query is generated as a function of received responses to the query associated with the received indication, wherein the new query is provided to a user as a next query in the identified query (¶55: add open ended question after closed ended question to get open ended response).
Regarding instant claim 5, claim 1 of ‘144 and Burningham disclose the method of claim 1, and claim 3 of ‘144 further discloses herein the indication includes a confidence level value representative of the confidence that a query is a closed ended query. (“wherein the machine learning model generator provides a confidence level with a classification of each query.”).
Regarding instant claim 6, claim 1 of ‘144 and Burningham disclose the method of claim 1, and claim 1 of ‘144 further discloses wherein the machine learning model has been seeded with a phrase list of terms to increase a weight of terms in the identified query that are in the phrase list for generating the indication (“seeding a model with a phrase list comprising terms; and generating a model that classifies closed ended queries as a function of the training data and phrase lists by increasing a weight of terms in the phrase list”).
Regarding instant claim 7, claim 1 of ‘144 discloses a computer implemented method of generating query suggestions, the method comprising:
identifying a query from an interface (“classifies closed ended queries”), the query including a question (“defining an intent for detecting closed ended queries”);
receiving an intent of the question that identifies whether the question is closed ended by providing the query to a language model (“classifies closed ended queries”), trained on closed-ended intent labeled utterances, representative of an intent of a received query (“providing a plurality of queries that are closed ended queries to a machine learning model generator, said plurality of queries comprising training data;” .. ”generating a model that classifies closed ended queries as a function of the training data”);
determining that the question is closed ended based on the intent of the question (“classifies closed ended queries”)
Claim 1 of ‘144 fails to disclose wherein the identified query requests information from a first user, and that in response to the determination that the question is closed ended: generating a new communication based on the question and replacing the new query with a new communication in the chat interface.
Burningham discloses methods for identifying a question type of a query for a first user (¶35). Particularly, Burningham discloses identifying whether the question is closed-ended and generating a new query including a new open-ended question and presenting it to the first user (¶35, 55). Burningham further discloses that the first user may be a testing user (¶98-99). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of claim 1 of ‘144 and Burningham before them before the effective filing of the claimed invention to combine the generating a new open-ended question query and presenting to a user from whom a closed-ended question was queried as a new communication replacing a previous query, as taught by Burningham, with the identification and classification of a test query using the model of claim 1 of ‘144. One would have been motivated to make this combination in order to present more appropriate questions to users, eliciting additional more accurate information, as suggested by Burningham (¶55).
Regarding instant claim 15, claim 19 of ‘144 discloses a device comprising:
a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
identifying a query from an interface (“classifies closed ended queries”), the query including a question (“defining an intent for detecting closed ended queries”);
receiving a probability on whether the question is closed ended by providing the query to a machine learning model (“classifies closed ended queries as a function”), the machine learning model being configured to classify closed ended queries based on queries provided to the machine learning model (“providing a plurality of queries that are closed ended queries to a machine learning model generator, said plurality of queries comprising training data;” .. ”generating a model that classifies closed ended queries as a function of the training data”).
Claim 19 of ‘144 fails to disclose wherein the identified query requests information from a first user, and that in response to the probability meeting a threshold: generating a new communication.
Burningham discloses methods for identifying a question type of a query for a first user (¶35). Particularly, Burningham discloses identifying a probability whether the question is closed-ended and generating a new query including a new open-ended question and presenting it to the first user (¶35, 55). Burningham further discloses that the first user may be a testing user (¶98-99). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of claim 1 of ‘144 and Burningham before them before the effective filing of the claimed invention to combine the generating a new open-ended question communication and presenting to a user from whom a closed-ended question was determined based on a probability meeting a threshold, as taught by Burningham, with the identification and classification of a test query using the model of claim 1 of ‘144. One would have been motivated to make this combination in order to present more appropriate questions to users, eliciting additional more accurate information, as suggested by Burningham (¶55).
Regarding instant claim 16, claim 19 of ‘144 and Burningham disclose method of instant claim 16, and Burningham further discloses wherein the new query is selected from a set of queries in a table (¶42, ¶54-55, ¶51: results database) and claim 20 of ‘144 further discloses wherein the machine learning model includes a confidence value higher than a threshold (“the machine learning model generator comprises a language understanding machine learning service that provides a confidence level with a classification of each query”).
Regarding instant claim 17, claim 20 of ‘144 and Burningham discloses the method of instant claim 16, and Burningham further discloses wherein the new query is generated to elicit more information than information received responsive to the query associated with the received indication (¶55: add open ended question after closed ended question to get open ended response).
Regarding claim 21, claim 1 of ‘144 and Burningham disclose the method of claim 7,and Burningham further discloses wherein the query is generated by a second user interacting with the chat interface (¶98: test mode, second user is survey designer which then transmits the recomposed query to the respondent).
Regarding claim 22, claim 1 of ‘144 and Burningham disclose the method of claim 7, and Burningham further discloses wherein the query is generated by a software machine learning model agent configured to autonomously provide queries in the chat interface (¶55: survey manager).
Regarding claim 24, claim 1 of ‘144 and Burningham disclose the method of claim 7, and Burningham further discloses wherein the new communication comprises a request for the first user to accept a new query, and in response to the first user accepting the new query, generating the new query based on the identified query (¶98-99: test mode wherein survey designer is testing themselves with previews and acknowledgement).
Regarding claim 25, claim 1 of ‘144 and Burningham disclose the method of claim 7, and Burningham further discloses wherein the new communication comprises a follow-up query related to the intent of the identified query (¶55: add open ended question after closed ended question to get open ended response).
Regarding claim 26, claim 1 of ‘144 and Burningham disclose the method of claim 7, and Burningham further discloses wherein the new communication replaces the identified query in the chat interface with a new query (¶55: add open ended question after closed ended question to get open ended response, effectively replacing the closed ended query).
Regarding claim 27, claim 1 of ‘144 and Burningham disclose the method of claim 7, and Burningham further discloses wherein determining that the question is closed ended comprises determining whether the question requests a non-binary explanation to a question (¶87: determine closed ended questions by answer type).
Regarding claim 28, claim 19 of ‘144 and Burningham disclose the device of claim 15, and claim 20 of ‘144 further discloses wherein the machine learning model classifies closed ended queries by evaluating the question against a phrase list comprising who, what, where, when, why, and how (wherein the machine learning model generator comprises a language understanding machine learning service that provides a confidence level with a classification of each query, and wherein the phrase list comprise words representative of emotions).
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Regarding claim 1, claim 1 recites:
“A computer implemented method of generating query suggestions, the method comprising:
identifying a query from an interface with a first user, the query including a question requesting information from the first user;
receiving an indication that identifies whether the question is closed ended by providing the query to a machine learning model, the machine learning model being configured to classify closed ended queries based on queries provided to the machine learning model; and
in response to the indication:
generating a new query that includes a new question that is open-ended, the new query generated based on the question in the query; and
presenting the new query to the first user in the interface.”
Step 1 MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
A .. method of generating query suggestions, the method comprising:
Step 2A Prong One, MPEP 2106.04:
These limitations represent, under Step 2A Prong One, certain methods of organizing human activity such as fundamental economic principals or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people, MPEP 2106.04(a)(2)(III), for example forming and asking more open-ended questions than previously asked to elicit more informative responses during an interview:
identifying a query .. with a first user, the query including a question requesting information from the first user;
receiving an indication that identifies whether the question is closed ended by providing the query .. to classify closed ended queries based on queries provided ..;
in response to the indication:
generating a new query that includes a new question that is open-ended, the new query generated based on the question in the query; and
presenting the new query to the first user ..
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
.. computer-implemented ..
.. from an interface ..
.. to a machine learning model, the machine learning model being configured to classify closed ended queries based on queries provide to the machine learning model ..
.. in the user interface ..
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
.. computer-implemented ..
.. from an interface ..
.. in the user interface ..
.. to a machine learning model ..
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
.. the machine learning model being configured to classify closed ended queries based on queries provide to the machine learning model ..
Regarding claim 7, claim 7 recites:
“A computer implemented method of generating query suggestions, the method comprising:
identifying a query from a chat interface from a first user, the query including a question requesting information from the first user;
receiving a intent of the question by providing the question to a language model trained on closed-ended intent labeled utterances, representative of an intent of a received query; and
in response to determining that the question is closed ended:
generating a new communication based on the question, and
replacing the query with a new communication in the chat interface.”
Step 1 MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
A .. method of generating query suggestions, the method comprising:
Step 2A Prong One, MPEP 2106.04:
These limitations represent, under Step 2A Prong One, certain methods of organizing human activity such as fundamental economic principals or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people, MPEP 2106.04(a)(2)(III), for example forming and asking more open-ended questions than previously asked to elicit more informative responses during an interview:
identifying a query .. with a first user, the query including a question requesting information from the first user;
receiving a intent of the question by providing the question .., representative of an intent of a received query;
in response to determining that the question is closed ended:
generating a new communication based on the question; and
replacing the query with a new communication ..
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
.. computer-implemented ..
.. from a chat interface ..
.. to a language model trained on closed-ended intent labeled utterances ..
.. in the chat interface ..
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
.. computer-implemented ..
.. from a chat interface ..
.. to a language model ..
.. in the chat interface ..
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
.. language model trained on closed-ended intent labeled utterances ..
Regarding claim 15, claim 1 recites:
“A device comprising:
a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
identifying a query from an interface with a first user, the query including a question requesting information from the first user;
receiving a probability on whether the question is closed ended by providing the query to a machine learning model, the machine learning model being configured to classify closed ended queries based on queries provided to the machine learning model; and
in response to the probability meeting a probability threshold:
generating a new communication based on the query.”
Step 1 MPEP 2106.03:
These limitations have been determined, under Step 1, to be statutory categories of invention:
A device comprising:
a processor; and
a memory device coupled to the processor
Step 2A Prong One, MPEP 2106.04:
These limitations represent, under Step 2A Prong One, certain methods of organizing human activity such as fundamental economic principals or practices, commercial or legal interactions, or managing personal behavior or relationships or interactions between people, MPEP 2106.04(a)(2)(III), for example forming and asking more open-ended questions than previously asked to elicit more informative responses during an interview:
identifying a query .. with a first user, the query including a question requesting information from the first user;
receiving an probability on whether the question is closed ended by providing the query .. to classify closed ended queries based on queries provided ..;
in response to the probability meeting a probability threshold:
generating a new communication based on the query.
Step 2A Prong Two, MPEP 2106.04(d):
These limitations represent, under Step 2A Prong Two, mere instructions to implement the abstract idea using generic computing tools, MPEP 2106.05(f):
.. a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations ..
.. from an interface ..
.. to a machine learning model, the machine learning model being configured to classify closed ended queries based on queries provide to the machine learning model ..
Step 2B, MPEP 2106.05:
These limitations are considered, under Step 2B, insignificant extra-solution activity as being recited at a high level of generality, MPEP 2106.05(d):
.. a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations ..
.. from an interface ..
.. to a machine learning model ..
These limitations are considered, under Step 2B, mere instructions to apply to obtain a solution/outcome, MPEP 2106.05(f):
.. the machine learning model being configured to classify closed ended queries based on queries provide to the machine learning model ..
Regarding dependent claims 3-5, 17, 24-25 and 27, each of these dependent claims further recite limitations of generating further queries in a dialog (such as in claims 3-4, 17, 24-25, 27) and how the determination of a closed-ended query is evaluated (such as in claims 5 and 27). The analysis incorporates the Step 1 and Step 2A Prong One analysis of their respective parents. These limitations of each claim each represent, under Step 2A Prong One, additional steps in the abstract idea of their respective parents.
Regarding dependent claims 2, 16 and 28-30, each of the dependent claims further recite limitations for producing the new query (claim 2) and how the determination of a closed ended query is evaluated (, 16 and 28-30). The analysis incorporates the Step 1 and Step 2A Prong One analysis of their respective parents; limitations of each claim (the set of queries, confidence value/score and probability score and respective threshold, phrase list for evaluating) represent, under Step 2A Prong One, additional steps in the abstract idea of the respective parent. These additional limitations of each claim (query table, machine learning model) represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality (MPEP 2106.05) and mere data gathering (MPEP 2106.05); under Step 2B, these limitations are considered mere instructions to apply to obtain a solution/outcome (MPEP 2106.05(f)) and insignificant extra-solution activities of data gathering such as selecting a particular type of data (MPEP 2106.05(g)).
Regarding dependent claims 6, 21-23 and 26, each of the dependent claims further recite limitations for determining the closed ended query for generating the indication (claim 6) and how a query is generated (claims 21-23 and 26). The analysis incorporates the Step 1 and Step 2A Prong One analysis of their respective parents. These additional limitations of each claim (seeding to increase a weight of terms in the query, machine learning model, software model agent, interact and provide by a chat interface or transcribed audio) represent, under Step 2A Prong Two, mere instructions to apply at a high level of generality (MPEP 2106.05) and mere data gathering (MPEP 2106.05); under Step 2B, these limitations are considered mere instructions to apply to obtain a solution/outcome (MPEP 2106.05(f)) and insignificant extra-solution activities of data gathering such as selecting a particular type of data (MPEP 2106.05(g)).
Claim Rejections - 35 USC § 102
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 (i.e., changing from AIA to pre-AIA ) 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.
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 1-4, 7, 15, 21-22 and 24-28 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Burningham et al., US 2016/0370954 A1 [Burningham].
Regarding claim 1, Burningham discloses a computer implemented method of generating query suggestions, the method comprising:
identifying a query from an interface with a first user, the query including a question requesting information from the first user (¶35: identify query/questions in a survey, ¶55: respondent receives a query including a question to rank satisfaction, ¶41-42: survey is distributed to and utilized by a first user, ¶44: via one or more distribution channels or interfaces such as a chat interface, ¶75-77);
receiving an indication that identifies whether the question is closed ended (¶35: identify the type of question as multiple choice, i.e. closed ended, ¶55: identify the closed-ended satisfaction question) by providing the query to a machine learning model (¶51, ¶53: survey system, ¶62, 83: composition manager), the machine learning model being configured to classify closed ended queries based on queries provided to the machine learning model (¶54: survey system classifies questions as closed or open ended, ¶42, ¶57, ¶84: recomposing a question includes composition manager, ¶86-87: includes question type detector that analyzes words to determine type of question); and
in response to the indication:
generating a new query that includes a new question that is open-ended, the new query generated based on the question in the query (¶35: recompose the question into a more suitable format, ¶54-55, ¶89); and
presenting the new query to the first user in the interface (¶98: present the recomposed question as it would appear in the survey, ¶101: distribute the recomposed question to the respondent).
Regarding claim 2, Burningham discloses method of claim 1 wherein the new query is selected from a set of queries in a table (¶42, ¶54-55, ¶51: results database).
Regarding claim 3, Burningham discloses the method of claim 2 wherein the new query is generated to elicit more information than information received responsive to the query associated with the received indication (¶55: add open ended question after closed ended question to get open ended response).
Regarding claim 4, Burningham discloses the claim 3 wherein the new query is generated as a function of received responses to the query associated with the received indication, wherein the new query is provided to a user as a next query in the identified query (¶55: add open ended question after closed ended question to get open ended response).
Regarding claim 7, Burningham discloses a computer implemented method of generating query suggestions, the method comprising:
identifying a query from a chat interface from a first user, the query including a question requesting information from the first user (¶35: identify query/questions in a survey, ¶55: respondent receives a query including a question to rank satisfaction, ¶41-42: survey is distributed to and utilized by a first user, ¶44: via one or more distribution channels or interfaces such as a chat interface, ¶75-77);
receiving a intent of the question (¶35: identify the type of question as multiple choice, i.e. closed ended, ¶55: identify the closed-ended satisfaction question) by providing the question to a language model (¶51, ¶53: survey system, ¶62, 83: composition manager) trained on closed-ended intent labeled utterances, representative of an intent of a received query (¶54: survey system classifies questions as closed or open ended, ¶42, ¶57, ¶84: recomposing a question includes composition manager, ¶86-87: includes question type detector that analyzes words to determine type of question); and
determining that the question is closed ended based on the intent of the question (¶55); and
in response to determining that the question is closed ended:
generating a new communication based on the question (¶35: recompose the question into a more suitable format, ¶54-55, ¶89), and
replacing the query with a new communication in the chat interface (¶98: present the recomposed question as it would appear in the survey, ¶101: distribute the recomposed question to the respondent).
Regarding claim 15, Burningham discloses a device comprising:
a processor; and
a memory device coupled to the processor and having a program stored thereon for execution by the processor to perform operations comprising:
identifying a query from an interface with a first user, the query including a question requesting information from the first user (¶35: identify query/questions in a survey, ¶55: respondent receives a query including a question to rank satisfaction, ¶41-42: survey is distributed to and utilized by a first user, ¶44: via one or more distribution channels or interfaces such as a chat interface, ¶75-77);
receiving a probability on whether the question is closed ended by providing the query to a machine learning model (¶85-86), the machine learning model being configured to classify closed ended queries based on queries provided to the machine learning model (¶54: survey system classifies questions as closed or open ended, ¶42, ¶57, ¶84: recomposing a question includes composition manager, ¶86-87: includes question type detector that analyzes words to determine type of question); and
in response to the probability meeting a probability threshold (¶85-87: broadly, determining the type of question based on meeting a certainty):
generating a new communication based on the query (¶98, ¶101, ¶55: add an open ended question).
Regarding claim 21, Burningham discloses the method of claim 7, wherein the query is generated by a second user interacting with the chat interface (¶98: test mode, second user is survey designer which then transmits the recomposed query to the respondent).
Regarding claim 22, Burningham discloses the method of claim 7, wherein the query is generated by a software machine learning model agent configured to autonomously provide queries in the chat interface (¶55: survey manager).
Regarding claim 24, Burningham discloses the method of claim 7, wherein the new communication comprises a request for the first user to accept a new query, and in response to the first user accepting the new query, generating the new query based on the identified query (¶98-99: test mode wherein survey designer is testing themselves with previews and acknowledgement).
Regarding claim 25, Burningham discloses the method of claim 7, wherein the new communication comprises a follow-up query related to the intent of the identified query (¶55: add open ended question after closed ended question to get open ended response).
Regarding claim 26, Burningham discloses the method of claim 7, wherein the new communication replaces the identified query in the chat interface with a new query (¶55: add open ended question after closed ended question to get open ended response, effectively replacing the closed ended query).
Regarding claim 27, Burningham discloses the method of claim 7, wherein determining that the question is closed ended comprises determining whether the question requests a non-binary explanation to a question (¶87: determine closed ended questions by answer type).
Regarding claim 28, Burningham discloses the device of claim 15, wherein the machine learning model classifies closed ended queries by evaluating the question against a phrase list comprising who, what, where, when, why, and how (¶86: compares question against words list to determine closed/open).
Claim Rejections - 35 USC § 103
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 (i.e., changing from AIA to pre-AIA ) 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.
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.
The factual inquiries 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.
This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
Claims 5, 16, 17, 29 and 30 are rejected under 35 U.S.C. 103 as being unpatentable over Burningham in view of Edell, “How to use AI to detect open-ended questions for non-datascientists”, from https://medium.com/data-science/how-to-use-ai-to-detect-open-ended-questions-for-non-datascientists-e2ef02427422, May 18, 2018, pages 1-7 (See parent 15/994,907 IDS 09/03/2019) [Edell].
Regarding claim 5, Burningham discloses he method of claim 1 wherein the indication includes a confidence that a query is a closed ended query.
Burningham fails to disclose wherein the confidence is an explicit confidence level value representative of the confidence.
Edell discloses a model for determining whether questions are open-ended or closed-ended (page 2 ¶1: “Thinking about chat bots, messaging services, and other use cases for machines interacting with humans, it became clear to me that being able to ask (or at least be able to detect) open-ended questions will be important.”, ¶2: “But let’s focus on just the single use case for now; detecting question type.”). In particular, Edell discloses providing a confidence indication from a machine model that a text query includes a question that is open ended or closed ended by providing a confidence level value representative of the confidence (page 6 ¶2-3: “It will train on the tens of thousands of examples pretty quickly (less than a minute for me), then go through a process of validation (it checks the known answers against what the model is predicting). On my first run through, I got an accuracy of 84% . Not amazing, but it definitely qualifies as significant. An accuracy above 80% means you’re onto something, and that optimization can most likely get you a higher accuracy.” - classifying each query by checking known answers against what the model is predicting output is an indication of confidence level of prediction). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of Burningham and Edell before them before the effective filing of the claimed invention to combine the use of an explicit confidence level value indication regarding whether a question is open or closed-ended, as taught by Edell, with the confident indication that a query is closed ended of Burningham. One would have been motivated to make this combination in order to provide output that leads to greater optimization steps, allowing the model to achieve greater accuracy, as suggested by Edell (page 6 ¶3: "An accuracy above 80% means you’re onto something, and that optimization can most likely get you a higher accuracy. That optimization usually means to doing more data cleaning, balancing, or simply just having more samples.").
Regarding claim 16, Burningham discloses the device of claim 15 wherein the machine learning model outputs a confidence that a query is a closed ended query (¶35, ¶55) and wherein the new query is selected from a set of queries in a table in response to the confidence (¶42, ¶54-55, ¶51).
Burningham fails to disclose wherein the confidence includes an explicit confidence value higher than a threshold.
Edell discloses a model for determining whether questions are open-ended or closed-ended (page 2 ¶1: “Thinking about chat bots, messaging services, and other use cases for machines interacting with humans, it became clear to me that being able to ask (or at least be able to detect) open-ended questions will be important.”, ¶2: “But let’s focus on just the single use case for now; detecting question type.”). In particular, Edell discloses providing a confidence indication from a machine model that a text query includes a question that is open ended or closed ended by providing a confidence level value higher or lower than thresholds representative of the confidence (page 6 ¶2-3: “It will train on the tens of thousands of examples pretty quickly (less than a minute for me), then go through a process of validation (it checks the known answers against what the model is predicting). On my first run through, I got an accuracy of 84% . Not amazing, but it definitely qualifies as significant. An accuracy above 80% means you’re onto something, and that optimization can most likely get you a higher accuracy.” - classifying each query by checking known answers against what the model is predicting output is an indication of confidence level of prediction). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of Burningham and Edell before them before the effective filing of the claimed invention to combine the use of an explicit confidence level value higher or lower than a threshold indicating whether a question is open or closed-ended, as taught by Edell, with the confident indication that a query is closed ended of Burningham. One would have been motivated to make this combination in order to provide output that leads to greater optimization steps, allowing the model to achieve greater accuracy, as suggested by Edell (page 6 ¶3-4: " That optimization usually means to doing more data cleaning, balancing, or simply just having more samples. I went through a step of balancing the data first, which boosted the accuracy a couple of points, then I went back to the source and downloaded a couple more datasets to add to the first one. After two rounds of adding data and balancing, I was able to get the accuracy up to 87%.").
Regarding claim 17, Burningham and Edell disclose the device of claim 16, and Burningham further discloses wherein the new communication is generated to elicit more information than information received responsive to the query associated with the received indication (¶55).
Regarding claim 29, Burningham discloses the method of claim 1, wherein the machine learning model outputs a probability indicative that the question is closed ended, and the method further comprises using the probability to determine whether to generate the new query (¶35, ¶55).
Burningham fails to disclose wherein the probability is a score indicative of a likelihood compared to a threshold.
Edell discloses a model for determining whether questions are open-ended or closed-ended (page 2 ¶1: “Thinking about chat bots, messaging services, and other use cases for machines interacting with humans, it became clear to me that being able to ask (or at least be able to detect) open-ended questions will be important.”, ¶2: “But let’s focus on just the single use case for now; detecting question type.”). In particular, Edell discloses providing a probability score indication from a machine model that a text query includes a question that is open ended or closed ended by comparing the probability score to a threshold (page 6 ¶2-3: “It will train on the tens of thousands of examples pretty quickly (less than a minute for me), then go through a process of validation (it checks the known answers against what the model is predicting). On my first run through, I got an accuracy of 84% . Not amazing, but it definitely qualifies as significant. An accuracy above 80% means you’re onto something, and that optimization can most likely get you a higher accuracy.” - classifying each query by checking known answers against what the model is predicting output is an indication of probability level of prediction above a threshold, ex. 80%). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of Burningham and Edell before them before the effective filing of the claimed invention to combine the use of an explicit probability score level value higher than a threshold indicating whether a question is open or closed-ended, as taught by Edell, with the probability indication that a query is closed ended of Burningham. One would have been motivated to make this combination in order to provide output that leads to greater optimization steps, allowing the model to achieve greater accuracy, as suggested by Edell (page 6 ¶3-4: " That optimization usually means to doing more data cleaning, balancing, or simply just having more samples. I went through a step of balancing the data first, which boosted the accuracy a couple of points, then I went back to the source and downloaded a couple more datasets to add to the first one. After two rounds of adding data and balancing, I was able to get the accuracy up to 87%.").
Regarding claim 30, Burningham discloses the method of claim 1, wherein the machine learning model outputs a confidence associated with the classification that the question is closed ended, wherein generating the new communication is further conditioned on the confidence (¶35, ¶55).
Burningham fails to disclose wherein the confidence is a score meeting a threshold.
Edell discloses a model for determining whether questions are open-ended or closed-ended (page 2 ¶1: “Thinking about chat bots, messaging services, and other use cases for machines interacting with humans, it became clear to me that being able to ask (or at least be able to detect) open-ended questions will be important.”, ¶2: “But let’s focus on just the single use case for now; detecting question type.”). In particular, Edell discloses providing a confidence score indication from a machine model that a text query includes a question that is open ended or closed ended by comparing the confidence score to a threshold (page 6 ¶2-3: “It will train on the tens of thousands of examples pretty quickly (less than a minute for me), then go through a process of validation (it checks the known answers against what the model is predicting). On my first run through, I got an accuracy of 84% . Not amazing, but it definitely qualifies as significant. An accuracy above 80% means you’re onto something, and that optimization can most likely get you a higher accuracy.” - classifying each query by checking known answers against what the model is predicting output is an indication of confidence level of prediction above a threshold, ex. 80%). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of Burningham and Edell before them before the effective filing of the claimed invention to combine the use of an explicit confidence score level value meeting a threshold indicating whether a question is open or closed-ended, as taught by Edell, with the confidence indication that a query is closed ended of Burningham. One would have been motivated to make this combination in order to provide output that leads to greater optimization steps, allowing the model to achieve greater accuracy, as suggested by Edell (page 6 ¶3-4: " That optimization usually means to doing more data cleaning, balancing, or simply just having more samples. I went through a step of balancing the data first, which boosted the accuracy a couple of points, then I went back to the source and downloaded a couple more datasets to add to the first one. After two rounds of adding data and balancing, I was able to get the accuracy up to 87%.").
Claim 23 is rejected under 35 U.S.C. 103 as being unpatentable over Burningham in view of Horne et al., US 10,015,316 B1 [Horne].
Regarding claim 23, Burningham discloses the method of claim 7, wherein the query is generated based on a distribution modality including text received from the first user (¶44).
Burningham fails to disclosed wherein the distribution modality is transcribed live audio received from the first user.
Horne discloses methods for determining quality of service and determining dynamic responses for a machine-user chat during a chat between a user and a machine model (col 2 lines 49-60). In particular, Horne discloses that service sessions may take place over an email, text messages or during a telephone call (col 4 lines 3-12) and when during a telephone call, the audio of the user is transcribed and analyzed by the system (col 8 lines 16-22). Therefore it would have been obvious to one having ordinary skill in the art and the teachings of Burningham and Horne before them before the effective filing of the claimed invention to combine the use of audio transcription of user audio during a telephone exchange with an automated system, as suggested by Horne, with the use of text of the first user during chat exchange with the automated system of Burningham. One would have been motivated to make this combination in order to provide additional modalities to operate the system, increasing convenience to the user, as suggested by Horne (col 4 lines 3-12).
Allowable Subject Matter
Claim 6 would be allowable if rewritten to overcome the rejection under 35 U.S.C. 101 and double patenting rejection, set forth in this Office action and to include all of the limitations of the base claim and any intervening claims.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
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/ANDREW L TANK/Primary Examiner, Art Unit 2141
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