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 .
Claims 1, 3-10, 12-16, and 18-23 are pending for examination. Claims 1, 9 and 16 are independent.
Response to Amendment
The office action is responsive to the amendments filed on 05/08/2026. As
directed by the amendments claims 1, 3-5, 9, 12-13, 16, and 18 are amended. Claims 2, 11, and 17 are canceled. Claims 21-23 are new
Response to Arguments
Applicant's arguments filed 05/08/2026 have been fully considered but they are not fully persuasive.
Applicant arguments regarding 35 U.S.C. § 101:
Step 2A / Prong One - The Claims Are Not Directed to an Abstract Idea
[…] These are specific computational operations performed by specific machine learning components interacting in a defined sequence.
The specification confirms that this combination is not amenable to mental performance: "such machine learning based classifiers cannot be performed by hand or using a mental process." Spec., ,i 48. Generating word embeddings and sentence embeddings leverages numerical vector representations produced by trained neural network models. Applying an unsupervised clustering algorithm to those embedding vectors, having a language model characterize the resulting clusters, and then training a supervised classifier on those characterizations are all computational operations that depend on specific hardware and software infrastructure and are not operations that a human could perform mentally or by hand, as a practical matter. Accordingly, the amended claims are not directed to a mental process or any other abstract idea under Step 2A / Prong One. The§ 101 rejection should be withdrawn on this basis alone.
Step 2A / Prong Two - Any Alleged Abstract Idea Is Integrated Into a Practical Application
[…]
The claimed embodiments resolve this problem through a specific combination of components. As the specification explains, "the use of word embedding with unsupervised machine learning clustering classifier 106 and the use of sentence embedding with supervised machine learning classifier 104 is a specific combination that can improve the functionality of the known techniques by using supervised machine learning classifier 104 or unsupervised machine learning clustering classifier 106 alone." Spec., i]50. The specification further confirms that the claimed architecture improves computer functionality: "[s]upervised machine learning clustering classifier 104, unsupervised machine learning clustering classifier 106, word embedding 320, and sentence embedding 310 are combined in a specific way to improve the functioning of the computer, particularly the accuracy of classifying user response statement 143." Spec., i]55.
The Office Action characterizes the step of generating the first data set as "practically performable in the human mind and ... a recitation of a mental process (i.e., evaluation/judgment)," and the providing step as "directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering." Office Action at 3-4. But these characterizations evaluate individual steps in isolation and ignore the integrated technical architecture of the claim as a whole. Evaluated as a whole, the claim reflects a specific technical solution to a specific technical problem in machine learning classification systems, not the application of an abstract idea to generic computer components. See MPEP § 2106.05(a).
Examiner response: Examiner respectfully disagrees, the machine learning components such as supervised machine learning classifier, language model, and unsupervised machine learning classifier are all described at a high level and describe a black box model applied to perform an abstract idea. This is understood as adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.05(f). Some of the models described broadly can be interpreted as being entirely a mental process such as determining training labels by a language model which is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment). Generating clusters can also be seen as a recitation of a mental process. Training is broadly described in the claims as providing training labels to a model without any specific training process.
MPEP 2106.04(d)(1) states the specification must describe the invention such that the improvement would be apparent to one of ordinary skill in the art. Conversely, if the specification explicitly sets forth an improvement but in a conclusory manner (i.e., a bare assertion of an improvement without the detail necessary to be apparent to a person of ordinary skill in the art), the examiner should not determine the claim improves technology. Second, if the specification sets forth an improvement in technology, the claim must be evaluated to ensure that the claim itself reflects the disclosed improvement. Applicants claimed improvement is describing an improvement to an abstract idea and not to an improvement to a computer or technical field. MPEP 2106.05(a) says an improvement in the abstract idea itself is not an improvement in technology. The claim limitations are a combination of mental steps under step 2A Prong 1, and additional elements under steps 2A Prong 2 & 2B as detailed in the 101 rejection below.
Applicant arguments regarding 35 U.S.C. § 103:
Applicant’s arguments with respect to claim(s) have been considered but are moot because the new ground of rejection.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1, 3-10, 12-16, and 18-23 rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Step 1
According to the first part of the analysis, in the instant case, claims 1, 3-8 are directed to a method, claims 9-10, 12-15, and 21-22 are directed to a system, and claims 16, 18-20, and 23 is directed to a non-transitory computer-readable medium. Thus, each of the claims falls within one of the four statutory categories (i.e., process, machine, manufacture, or composition of matter).
Regarding Claim 1
2A Prong 1:
generating, sentence embedding based on a question statement and a user response statement provided by a user in response to the question statement; (This step for generating a sentence embedding is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
the training labels are determined by a language model and characterize content of respective clusters of prior user response statements (This determining training labels by a language model is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).), and wherein the clusters are generated by an unsupervised machine learning clustering classifier based on a word embeddings of prior user response statements provided by a group of users to a set of prior question statements; (This generating clusters is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
generating, output labels selected from the set of training labels for the user response statement, wherein the set of output labels includes a first label with a first probability and a second label with a second probability. (This step for generating a set of output labels is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2: This judicial exception is not integrated into a practical application.
Additional elements:
A computer-implemented method for classifying a user response statement, comprising: (The computer-implemented method comprising processor is understood to be generic computer elements - See MPEP 2106.05(f).)
by at least one computer processor, (The computer-implemented method comprising processor is understood to be generic computer elements - See MPEP 2106.05(f).)
providing a set of training labels to a supervised machine learning classifier to train the supervised machine learning classifier, (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and data gathering. See MPEP 2106.05(g).)
by inputting the sentence embedding to the supervised machine learning classifier (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic machine learning as a tool to perform the abstract idea (e.g., generate labels) - see MPEP 2106.05(f).)
The additional elements as disclosed above alone or in combination do not integrate the judicial exception into practical application as they are insignificant extra solution activity in combination of generic computer functions that are implemented to perform the disclosed abstract idea above.
2B: The claim does not include additional elements that are sufficient to amount to significantly more than the judicial exception.
Additional elements:
A computer-implemented method for classifying a user response statement, comprising: (The computer-implemented method comprising processor is understood to be generic computer elements - See MPEP 2106.05(f).)
by at least one computer processor, (The computer-implemented method comprising processor is understood to be generic computer elements - See MPEP 2106.05(f).)
providing a set of training labels to a supervised machine learning classifier to train the supervised machine learning classifier, (This step is directed to transmitting or receiving information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity of transmitting and receiving data as identified by the court (MPEP2106.05(d)(ll)(i))))
by inputting the sentence embedding to the supervised machine learning classifier (This step is adding the words “apply it” (or an equivalent) with the judicial exception, or merely applying a generic machine learning as a tool to perform the abstract idea (e.g., generate labels) - see MPEP 2106.05(f).)
The additional elements as disclosed above in combination of the abstract idea
are not sufficient to amount to significantly more than the judicial exception as they are
well, understood, routine and conventional activity as disclosed in combination
of generic computer functions that are implemented to perform the disclosed abstract idea above.
Regarding Claim 9: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2: The claim recites another additional element “A system, comprising: one or more memories configured to store a question statement and a user response statement provided by a user in response to the question statement (The memory is understood to be a generic computer element - See MPEP 2106.05(f). The step directed to storing information, is understood to be insignificant extra- solution activity and data gathering. See MPEP 2106.05(g).); and at least one processor each coupled to at least one of the memories and configured to perform operations comprising: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))”
2B:
A system, comprising: one or more memories configured to store a question statement and a user response statement provided by a user in response to the question statement (The memory is understood to be a generic computer element - See MPEP 2106.05(f). This step is directed to storing information, which is understood to be insignificant extra-solution activity and is well understood, routine and conventional activity as identified by the court (MPEP 2106.05(d)(ll)(IV))))); and at least one processor each coupled to at least one of the memories and configured to perform operations comprising: (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))”
Regarding Claim 16: see the rejection of claim 1 above. Same rationale applies.
2A Prong 2 & 2B: The claim recites another additional element “A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least a computing device, cause the computing device to perform operations comprising:” (mere instructions to apply the exception using a generic computer component - see MPEP 2106.05(f))
Regarding Claims 3, 21, and 23
2A Prong 1:
wherein the word embeddings further include truncated embeddings to reduce a dimensionality of the word embeddings. (This step for is practically performable in the human mind and is understood to be a recitation of a mental process (i.e., evaluation/judgment).)
2A Prong 2 & 2B: The claim does not recite any additional elements.
Regarding Claims 4, 12, and 18
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies sentence embedding - See MPEP 2106.05(h).)
Regarding Claim 5, 13, and 19
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the supervised machine learning classifier includes a supervised neural network, a support vector machine (SVM) classifier, a random forest classifier, or a K nearest neighbors supervised machine learning classifier. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the supervised machine learning classifier - See MPEP 2106.05(h).)
Regarding Claims 6 and 22
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the language model includes a probabilistic language model or a neural network based language model. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the language model - See MPEP 2106.05(h).)
Regarding Claims 7, 14, and 20
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the unsupervised machine learning clustering classifier includes an Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, a density-based cluster ordering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the unsupervised machine learning clustering classifier - See MPEP 2106.05(h).)
Regarding Claims 8 and 15
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the unsupervised machine learning clustering classifier includes the OPTICS algorithm, and further includes an Agglomerative clustering algorithm to classify noises generated by the OPTICS clustering algorithm. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the unsupervised machine learning clustering classifier - See MPEP 2106.05(h).)
Regarding Claim 10
2A Prong 1: The claim does not recite any Abstract idea.
2A Prong 2 & 2B:
wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements. (The specification of data to be stored is understood to be a field of use limitation. The limitation further specifies the user response statement - See MPEP 2106.05(h).)
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.
Claim(s) 1, 5-6, 9, 13, 16, and 22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani et al. (US 20240370662 A1, hereinafter "Khani") in view of Cakir et al. (US 20240086445 A1, hereinafter "Cakir") and Wilson et al. (US 20240037583 A1, hereinafter "Wilson") .
Regarding Claim 1
Khani discloses: A computer-implemented method for classifying a user response statement, comprising: ([Para 0021, 0050-0067, Fig 1 and Fig 6-7] disclose a computer implement method for classifying.)
generating, by at least one computer processor, a sentence embedding based on ([Para 0026] describes training datasets for an NLP model (i.e. a first data set). [Para 0001, 0029, and claim 7] disclose sentence embeddings.)
providing a set of training labels to a supervised machine learning classifier to train the supervised machine learning classifier, , and wherein the clusters are generated by an unsupervised machine learning clustering classifier based on a word embeddings of prior user response statements provided by a ([Para 0001, 0026-0029, 0045-0049, Fig 1-2 and Fig 5] describes providing labeled training data to a NLP model (i.e. supervised model) determined by a LLM (i.e. language model) for a cluster based. [Para 0028-0029, 0033, 0044] describes clustering (i.e. unsupervised machine learning). [Para 0042, 0049] describes labeled datasets to train the NPL model (i.e. supervised).) and
generating, by inputting the sentence embedding to the supervised machine learning classifier, a set of output labels selected from the set of training labels for the user response statement, wherein the set of output labels includes a first label with a first probability and a second label with a second probability. ([Para 001, 0023, 0026, and 0029] describes the NLP model generating language related predictions (i.e. probabilities) represented by the training data (i.e. first dataset).)
Khani does not explicitly disclose: wherein the training labels are determined by a language model and characterize content of respective clusters of prior
However, Cakir discloses in the same field of endeavor: providing a set of training labels to a supervised machine learning classifier to train the supervised machine learning classifier, wherein the training labels are determined by a language model and characterize content of respective clusters of prior statements, and wherein the clusters are generated by an unsupervised machine learning clustering classifier based on a word embeddings of prior user response statements provided by a group of users ([Para 0067-0070, and Fig 8] describes providing an output from trained model 808 and the annotated database 810 (i.e. training labels) to recommender system 818 (i.e. supervised machine learning classifier), wherein the labels determined characterize the cluster of tickets (i.e. prior statements). [Para 0079-0081 and Fig 11] also describes providing labeled data to a Supervised SVM model.) and
generating, by inputting the the supervised machine learning classifier, a set of output labels selected from the set of training labels for the user response statement, wherein the set of output labels includes a first label with a first probability and a second label with a second probability. ([Para 0067-0070, and Fig 8] describes the recommender system 818 (i.e. supervised machine learning classifier) inputting embeddings and generating multiple output label. [Para 0079-0081 and Fig 11] also describes providing a Supervised SVM model inputting embeddings and generating output.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Machine learning and Natural language processing disclosed by Cakir into the method of Natural Language Processing Models disclosed by Khani to provide training labels to a supervised model that characterize clusters. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Machine learning and Natural language processing disclosed by Cakir as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to further train and improve a supervised machine learning model.
Khani in view of Cakir does not explicitly disclose: a question statement and a user response statement provided by a user in response to the question statement; prior user response statements provided by a group of users to a set of prior question statements;
However, Wilson discloses in the same field of endeavor: a question statement and a user response statement provided by a user in response to the question statement; ([Para 0009, 0110-0112] describes training data (i.e. first dataset) comprising personal data set of each of the plurality of the first users including a data entry regarding a response given by each respective first user to a survey.)
wherein the clusters are generated by an unsupervised machine learning clustering classifier based on a of prior user response statements provided by a group of users to a set of prior question statements; ([Para 0114] describes clustering users with similar response to one of the queries of a survey. [Para 0085-0086, 0173, and Fig 6-9] describes test/validation data. [Para 0008 and Para 0010] describes performing cluster analysis via unsupervised learning.)
generating, by inputting the the supervised machine learning classifier, a set of output labels selected from the set of training labels for the user response statement, wherein the set of output labels includes a first label with a first probability and a second label with a second probability. ([Para 0070, 0119-0123, 0128-0130, and Fig 7] describes a supervised model for predicting survey responses.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of training machine learning models based on survey datasets disclosed by Wilson into Khani in view of Cakir to analyze datasets-based question statements and user response statements. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of training machine learning models based on survey datasets disclosed by Wilson as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to use of machine learning processes to predict survey related data.
Regarding Claim 9
Khani in view of Cakir and Wilson discloses: A system, comprising: one or more memories configured to store a question statement and a user response statement provided by a user in response to the question statement; and at least one processor each coupled to at least one of the memories and configured to perform operations ([Para 0055, 0089-0100, and Fig 2], Wilson describes survey data in storage device 224 (i.e. memory).) comprising: (Claim 9 is a system claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 16
Khani in view of Cakir and Wilson discloses: A non-transitory computer-readable medium having instructions stored thereon that, when executed by at least a computing device, cause the computing device to perform operations ([Para 0006, 0061, and Fig 6-7], Khani) comprising: (Claim 16 is a non-transitory computer-readable medium claim that corresponds to claim 1 and the rest of the limitations are rejected on the same ground)
Regarding Claim 5
Khani in view of Cakir and Wilson discloses: The computer-implemented method of claim 1, wherein the supervised machine learning classifier includes a supervised neural network, a support vector machine (SVM) classifier, a random forest classifier, or a K nearest neighbors supervised machine learning classifier. ([Para 0026], Khani “the training mechanism 170 uses labeled training data to train the NLP model 120 via deep neural network(s)”. [Para 0070], Wilson describes a supervised machine learning classifier.)
Regarding Claim 6
Khani in view of Cakir and Wilson discloses: The computer-implemented method of claim 1, wherein the language model includes a probabilistic language model or a neural network based language model. ([Para 0019, 0024 and Fig 1], Khani describe probabilistic LLM models.)
Regarding Claim 13
(Claim 13 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 19
(Claim 19 recites analogous limitations to claim 5 and therefore is rejected on the same ground as claim 5.)
Regarding Claim 22
(Claim 22 recites analogous limitations to claim 6 and therefore is rejected on the same ground as claim 6.)
Claim(s) 3-4, 12, 18, 21, and 23 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani in view of Cakir, Wilson and Pouran Ben Veyseh et al. (US 20230252237 A1, hereinafter "Pouran").
Regarding Claim 3
Khani in view of Cakir and Wilson discloses: The computer-implemented method of claim 1,
Khani in view of Cakir and Wilson does not explicitly discloses: wherein the word embeddings further include truncated embeddings to reduce a dimensionality of the word embeddings.
However, Pouran discloses in the same field of endeavor: wherein the word embeddings further include truncated embeddings to reduce a dimensionality of the word embeddings. ([para 0112, 0119-0120], Pouran)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of natural language processing disclosed by Pouran into the method of Khani in view of Cakir and Wilson to generate word embeddings. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of natural language processing disclosed by Pouran as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to evaluate text at a word level for a more nuanced understanding of language.
Regarding Claim 4
Khani in view of Wilson Cakir and Pouran discloses: The computer-implemented method of claim 1, wherein the sentence embedding includes SentenceBERT, Universal Sentence Encoder, FastText, or a conditional masked language modelling. ([Para 0029 and Claim 7], Khani discloses sentence embeddings. [Para 0109 0090], Pouran describes sentence embeddings input to a pre-trained BERT.)
Regarding Claim 12
(Claim 12 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 18
(Claim 18 recites analogous limitations to claim 4 and therefore is rejected on the same ground as claim 4.)
Regarding Claim 21
(Claim 21 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Regarding Claim 23
(Claim 23 recites analogous limitations to claim 3 and therefore is rejected on the same ground as claim 3.)
Claim(s) 7-8, 14-15, and 20 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani in view of Cakir, Wilson and Rizk (US 20230078260 A1, hereinafter "Rizk").
Regarding Claim 7
Khani in view of Cakir and Wilson discloses: The computer-implemented method of claim 1, wherein the unsupervised machine learning clustering classifier;
Khani in view of Wilson does not explicitly disclose: wherein the unsupervised machine learning clustering classifier includes an Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, a density-based cluster ordering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.
However, Rizk discloses in the same field of endeavor: wherein the unsupervised machine learning clustering classifier includes an Ordering Points To Identify the Clustering Structure (OPTICS) algorithm, a density-based cluster ordering algorithm, or a Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. ([Para 0007, 0045, and 0080])
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Clustering disclosed by Rizk into the method of Khani in view of Cakir and Wilson to the particular clustering methods. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Clustering disclosed by Rizk as all the references are in the field of machine learning. A person of ordinary skill of the art would have been motivated to perform the combination for being able to perform various clustering techniques.
Regarding Claim 8
Khani in view of Cakir, Wilson and Rizk discloses: The computer-implemented method of claim 7, wherein the unsupervised machine learning clustering classifier includes the OPTICS algorithm, and further includes an Agglomerative clustering algorithm to classify noises generated by the OPTICS clustering algorithm. ([Para 0007, 0045, and 0080], Rizk)
Regarding Claim 14
(Claim 14 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Regarding Claim 15
(Claim 15 recites analogous limitations to claim 8 and therefore is rejected on the same ground as claim 8.)
Regarding Claim 20
(Claim 20 recites analogous limitations to claim 7 and therefore is rejected on the same ground as claim 7.)
Claim(s) 10 is/are rejected under 35 U.S.C. 103 as being unpatentable over Khani in view of Cakir Wilson and Long et al. (US 20200074294 A1, hereinafter "Long").
Regarding Claim 10
Khani in view of Cakir, Wilson discloses: The system of claim 9, wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements.
Khani in view of Cakir, Wilson does not explicitly disclose: wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements.
However, Long discloses in the same field of endeavor: wherein the user response statement and the plurality of prior user response statements are user response statements to an open-ended survey question statement, and the question statement and the set of prior question statements are open-ended survey question statements. ([Para 0046, 0170, Fig 2, and Fig 7-8] describes open ended survey question.)
It would have been obvious to a person of ordinary skill in art before the effective filling date of the invention to implement the function of Survey Creation for Machine learning disclosed by Long into the method of Khani in view of Wilson to comprise open-ended survey question statements. The modification would have been obvious because one of the ordinary skills of the art would be motivated to utilize the feature of Survey Creation for Machine learning disclosed by Long as all the references are in the field of machine learning model. A person of ordinary skill of the art would have been motivated to perform the combination for being able to have survey questions in various formats.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure. Basel et al. (US 20200134510 A1) describes a cluster model wit a classifier model.
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
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