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. DETAILED ACTION 1. This Office Action is in response to the application filed on 0 9/22/2023 . Claims 1- 20 are pending. Information Disclosure Statement 2. The information disclosure statement (IDS) filed on 0 9/22/2023 complies with the provisions of M.P.E.P. 609. The examiner has considered it. Claim Rejections - 35 USC § 101 3 . 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. 4 . Claims 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter. Claims 8-14 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because claim 15 directs to “computer-readable medium” or “computer readable storage medium” in which, when using the broadest reasonable interpretation would include non-statutory subject matter. That is, l anguage such as “physical”, “tangible” and “storage” do not make an otherwise non-statutory computer-readable medium claim statutory, since a data signal per se is considered a physical and tangible medium that temporarily stores data (it’s a transitory storage medium, but it’s still a storage medium). Examiner’s Note 5 . A survey sequence (According to Google): “ A survey sequence refers to the strategic ordering and flow of questions within a questionnaire, designed to guide respondents logically through topics, reduce bias, and minimize drop-out rates. Effective sequencing often follows a "funnel" structure, moving from broad, simple questions to specific, complex, or sensitive ones . ” A sensed survey sequence (According to Google): “ A sensed survey sequence generally refers to the systematic, step-by-step process of collecting, analyzing, and interpreting data acquired through non-contact, remote sensing instruments (e.g., satellites, aircraft, sonar). It represents the chronological order of operations required to transform raw electromagnetic radiation or reflected signals into actionable information . ” A Hidden Markov Model (According to Google): “ A Hidden Markov Model (HMM) is a statistical model used to predict a sequence of unknown (hidden) states based on a series of observed, visible events. It assumes the system's current state depends only on the previous state, making it ideal for modeling temporal data like speech recognition, bioinformatics, and finance . ” Giaconi et al, US 20230168668, [ Giaconi : Abstract and paragraph 6 (“ identifying anomalous data obtained by at least one sensor of a plurality of sensors located within an environment. The method includes identifying, based on sensor data obtained from the plurality of sensors ” and “ a sequence of events that occurred within the environment ” , i.e., ‘sensed survey sequence’)] [ Giaconi : Paragraphs 8-9 (“ the probability is obtained using a trained statistical model. The trained statistical model may be trained using training data comprising further sensor data obtained from the plurality of sensors under the non-anomalous conditions, the further sensor data different from the sensor data ” AND “ the probability is obtained using a Markov model of the sequence of events. The Markov model may be a first order Markov model, a second order Markov model or a third order Markov model ”)] . Subraveti et al, US 20240012844, [ Subraveti : Paragraph 39 (“ Survey questions also may be related to other types of phenotypes such as appearance traits of the users. A survey regarding appearance traits and characteristics may include questions related to eye color, iris pattern, freckles, chin types, finger length, dimple chin, earlobe types, hair color, hair curl, skin pigmentation, susceptibility to skin burn, bitter taste, male baldness, baldness pattern, presence of unibrow, presence of wisdom teeth, height, and weight. A survey regarding other traits also may include questions related to users' taste and smell such as the ability to taste bitterness, asparagus smell, cilantro aversion, etc. A survey regarding traits may further include questions related to users' body conditions such as lactose tolerance, caffeine consumption, malaria resistance, norovirus resistance, muscle performance, alcohol flush, etc. Other survey questions regarding a person's physiological or psychological traits may include vitamin traits and sensory traits such as the ability to sense an asparagus metabolite. Traits may also be collected from historical records, electronic health records and electronic medical records ” , i.e., ‘sensed survey sequence’ )] [ Subraveti : Paragraphs 49, 54 and 60 (“ the phasing engine 220 may use a directed acyclic graph model such as a hidden Markov model (HMM) to perform the phasing of a target genotype dataset. The directed acyclic graph may include multiple levels, each level having multiple nodes representing different possibilities of haplotype clusters. An emission probability of a node, which may represent the probability of having a particular haplotype cluster given an observation of the genotypes may be determined based on the probability distribution of the haplotype-cluster model. A transition probability from one node to another may be initially assigned to a non-zero value and be adjusted as the directed acyclic graph model and the haplotype-cluster model are trained ”, i.e., ‘training a hidden Markov model’)] [ Subraveti : Paragraph 100 (“ A machine learning model may be associated with an objective function, which generates a metric value that describes the objective goal of the training process. The training process may intend to reduce the error rate of the model in generating predictions ”)] [ Subraveti : Paragraph 49 (“ a directed acyclic graph model such as a hidden Markov model (HMM) to perform the phasing of a target genotype dataset. The directed acyclic graph may include multiple levels, each level having multiple nodes representing different possibilities of haplotype clusters. An emission probability of a node, which may represent the probability of having a particular haplotype cluster given an observation of the genotypes may be determined based on the probability distribution of the haplotype-cluster model. ”)] . Adi et al, US 20170344893, [Adi: Abstract and paragraphs 118-19 (“ a method for detecting an event may comprise receiving data representing measurement or detection of physical parameters, conditions, or actions, quantizing the received data and selecting a number of samples from the quantized data, generating a hidden Markov model representing events to be detected using initial model values based on ideal conditions ”)] . Claim Rejections - 35 USC § 102 6 . 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 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. 7 . 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)(2) the claimed invention was described in a patent issued under section 151, or in an application for patent published or deemed published under section 122(b), in which the patent or application, as the case may be, names another inventor and was effectively filed before the effective filing date of the claimed invention. 8 . Claims 1-2 0 are rejected under 35 U.S.C. 102(a)(2) as being anticipated by Subraveti et al (US 20240012844) . Claim 1 : Subraveti suggests a computer-implemented method comprising: sensing a survey sequence, responsive to the sensed survey sequence, inputting the sensed survey sequence into a prediction model wherein the prediction model comprises [ Subraveti : Paragraph 39 (“Survey questions also may be related to other types of phenotypes such as appearance traits of the users. A survey regarding appearance traits and characteristics may include questions related to eye color, iris pattern, freckles, chin types, finger length, dimple chin, earlobe types, hair color, hair curl, skin pigmentation, susceptibility to skin burn, bitter taste, male baldness, baldness pattern, presence of unibrow, presence of wisdom teeth, height, and weight. A survey regarding other traits also may include questions related to users' taste and smell such as the ability to taste bitterness, asparagus smell, cilantro aversion, etc. A survey regarding traits may further include questions related to users' body conditions such as lactose tolerance, caffeine consumption, malaria resistance, norovirus resistance, muscle performance, alcohol flush, etc. Other survey questions regarding a person's physiological or psychological traits may include vitamin traits and sensory traits such as the ability to sense an asparagus metabolite. Traits may also be collected from historical records, electronic health records and electronic medical records” , i.e., ‘sensed survey sequence’ )] . Subraveti suggests training a hidden Markov model on a previously administered survey with a corresponding response status as a hidden status and a corresponding linguistic metric as an observation [ Subraveti : Paragraph 100 (“ A machine learning model may be associated with an objective function, which generates a metric value that describes the objective goal of the training process. The training process may intend to reduce the error rate of the model in generating predictions ”)] [ Subraveti : Paragraph 49 (“ a directed acyclic graph model such as a hidden Markov model (HMM) to perform the phasing of a target genotype dataset. The directed acyclic graph may include multiple levels, each level having multiple nodes representing different possibilities of haplotype clusters. An emission probability of a node, which may represent the probability of having a particular haplotype cluster given an observation of the genotypes may be determined based on the probability distribution of the haplotype-cluster model. ”)] . Subraveti suggests computing a probability of a response status of the sensed survey sequence as an output of the prediction model based at least in part on the previously administered survey [ Subraveti : Paragraph s 56 and 61 (“ Reference panel samples may be used to train various machine learning models in classifying whether a target genetic dataset belongs to a community, determining the ethnic composition of an individual, and determining the accuracy of any genetic data analysis, such as by computing a posterior probability of a classification result from a classifier ”)] . Claim 2 : Subraveti suggests wherein training the hidden Markov model further comprises executing an analysis of variance to determine a highest covariate relationship between the corresponding response status and the corresponding linguistic metric of the previously administered survey [ Subraveti : Paragraph 118 (“ Another metric may be similar to the coverage metric described above, however the metric may also evaluate performance response by response (i.e. response coverage). For each response, the computing server 130 picks the keyphrase from the cluster that has a maximum cosine similarity and averages these similarity scores across clusters. ”)] . Claim 3 : Subraveti suggests wherein training the hidden Markov model further comprises executing a topic modeling on the previously administered survey [ Subraveti : Paragraph 39 (“Survey questions also may be related to other types of phenotypes such as appearance traits of the users. A survey regarding appearance traits and characteristics may include questions related to eye color, iris pattern, freckles, chin types, finger length, dimple chin, earlobe types, hair color, hair curl, skin pigmentation, susceptibility to skin burn, bitter taste, male baldness, baldness pattern, presence of unibrow, presence of wisdom teeth, height, and weight. A survey regarding other traits also may include questions related to users' taste and smell such as the ability to taste bitterness, asparagus smell, cilantro aversion, etc. A survey regarding traits may further include questions related to users' body conditions such as lactose tolerance, caffeine consumption, malaria resistance, norovirus resistance, muscle performance, alcohol flush, etc. Other survey questions regarding a person's physiological or psychological traits may include vitamin traits and sensory traits such as the ability to sense an asparagus metabolite. Traits may also be collected from historical records, electronic health records and electronic medical records” , i.e., ‘sensed survey sequence’ )] . Claim 4 : Subraveti suggests wherein training the hidden Markov model further comprises executing a cosine similarity on the previously administered survey [ Subraveti : Paragraph 69 (“Another metric may be similar to the coverage metric described above, however the metric may also evaluate performance response by response (i.e. response coverage). For each response, the computing server 130 picks the keyphrase from the cluster that has a maximum cosine similarity and averages these similarity scores across clusters.”)] . Claim 5 : Subraveti suggests wherein the corresponding linguistic metric comprises a length of the previously administered survey [ Subraveti : Paragraph 51 (“ determines the total length of matched segments, which may also be referred to as IBD segments. The length may be measured in the genetic distance in the unit of centimorgans ( cM ). A unit of centimorgan may be a genetic length ”)] . Claim 6 : Subraveti suggests wherein the corresponding linguistic metric comprises a word complexity of the previously administered survey [ Subraveti : Paragraph s 69 and 71 (“ may use different ways to achieve this as described below (e.g., TF-IDF, using word embeddings, large-scale language models (e.g., large language model LLM), etc.). ”)] . Claim 7 : Subraveti suggests wherein the output of the prediction model is caused in part by executing a Viterbi algorithm [ Subraveti : Paragraph s 49 and 6 1 (“ A suitable dynamic programming algorithm such as the Viterbi algorithm may be used to determine the path. ”)] . Claim 8 : Claim 8 is essentially the same as claim 1 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 9 : Claim 9 is essentially the same as claim 2 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 10 : Claim 10 is essentially the same as claim 3 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 1 1 : Claim 1 1 is essentially the same as claim 4 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 1 2 : Claim 1 2 is essentially the same as claim 5 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 1 3 : Claim 1 3 is essentially the same as claim 6 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 1 4 : Claim 1 4 is essentially the same as claim 7 except that it sets forth the claimed invention as a program product rather than a method and rejected under the same reasons as applied above . Claim 1 5 : Claim 1 5 is essentially the same as claim 1 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 1 6 : Claim 1 6 is essentially the same as claim 2 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 1 7 : Claim 1 7 is essentially the same as claim 3 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 1 8 : Claim 1 8 is essentially the same as claim 4 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 1 9 : Claim 1 9 is essentially the same as claim 6 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . Claim 20 : Claim 20 is essentially the same as claim 7 except that it sets forth the claimed invention as a system rather than a method and rejected under the same reasons as applied above . 9 . Any inquiry concerning this communication or earlier communications from the examiner should be directed to [Hung D. Le], whose telephone number is [571-270-1404] . The examiner can normally be communicated on [Monday to Friday: 9:00 A.M. to 5:00 P.M.]. If att empts to reach the examiner by telephone are unsuccessful, the examiner’s supervi sor, Apu Mofiz can be reached on [571-272-4080] . The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300. Information regarding the status of an application may be obtained from the Patent Application Information Retrieval (PAIR) system. Status information for published applications may be obtained from either Private PAIR or Public PAIR. Status information for unpublished applications is available through Private PAIR only. For more information about the PAIR system, see http://pair-direct.uspto.gov. Should you have questions on access to the Private PAIR system, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative or access to the au tomated information system, contact [ 800-786-9199 (IN USA OR CANADA) or 571-272-1000 ] . Hung Le 03 / 28 /202 6 /HUNG D LE/ Primary Examiner, Art Unit 2161