DETAILED ACTION
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed 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and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114.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 4/9/2026 has been entered.
Status of Claims
This is in reply to the claim amendments and remarks of the RCE filed 4/9/2026.
Claims 1, 3-7, 9, and 11-19 have been amended and claims 21-22 have been added new.
Claims 1-7, 9-19, and 21-22 are currently pending and have been examined.
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 .
Response to Amendments
Applicant’s amendments have been fully considered, but do not overcome the previously pending 35 USC 103 and 35 USC 101 rejections.
Response to Arguments
Applicant's arguments have been fully considered but they are not persuasive.
With regard to the limitations of claims 1-7, 9-19, and 21-22, Applicant argues the claims are not directed towards an abstract idea. The Examiner respectfully disagrees. The Examiner has clearly pointed out the limitations directed towards the abstract idea, what the additional elements are and why they do not integrate the abstract idea into a practical application, and why the additional elements and remaining limitations do not amount to significantly more than the abstract idea. The Applicant’s claims are using hidden Markov modeling to analyze spend and subscription predictions through certain user accounts, where analyzing and predicting spend on human user accounts in monitoring and predicting what humans will do for commercial purposes (e.g. Organizing Human Activity). Applicant’s arguments are not persuasive.
The Examiner further notes that Applicant’s claims now recite specific mathematical relationships for performing the predictions and is also rejected as being directed towards Mathematical Relationships. See rejection below. Applicant’s arguments are not persuasive.
The Examiner notes that the hidden Markov model as claimed is being used to implement the abstract idea. The claims now define what the inputs to the HMM are in the form of mathematical relationships (e.g. abstract). Applicant’s arguments are not persuasive.
The Examiner further asserts that any recited machine learning is recited at such a high level of generality that it merely adds the words apply it with the judicial exception (See MPEP 2106.05). Applicant’s arguments are not persuasive.
The Examiner further asserts that the recited allocating a set of computing resources merely adds the words apply it with the judicial exception (See MPEP 2106.05) because there is no specific recitation of what is actually being allocated or how it is being allocated. Applicant’s arguments are not persuasive.
Applicant argues the claims integrate the abstract idea into a practical application. The Examiner respectfully disagrees. Improving the results of the predictions does not improve the functioning of the computer or hardware, but rather narrows the abstract idea, where specifically defining the input values to the HMM (e.g. matrices) narrows the abstract idea specifically. Applicant’s arguments are not persuasive.
The Examiner further asserts that the recited allocating a set of computing resources merely adds the words apply it with the judicial exception (See MPEP 2106.05) because there is no specific recitation of what is actually being allocated or how it is being allocated. For example, what is a computing resource? How is the computing resource being allocated? Applicant’s arguments are not persuasive.
The Examiner points to Page 2 of the McRO-Bascom Memo from December 2016, "The McRO court indicated that it was the incorporation of the particular claimed rules in computer animation "that improved [the] existing technological process", unlike cases such as Alice where a computer was merely used as a tool to perform an existing process." The Applicants’ claims are geared toward making predictions about spend and subscriptions based on user interactions, where these techniques are merely being applied/calculated in a computing environment. Simply applying these known concepts to a specific technical environment (e.g. the computers/Internet) does not account for significantly more than the abstract idea because it does not solve a problem rooted in computer technology nor does it improve the functioning of the computer itself because it is merely making a determination based on rules and/or mathematical relationships (e.g. probability matrix and hidden Markov model) to output to a user. The Applicant’s claimed limitations do not appear to bring about any improvement in the operation or functioning of a computer per se, or to improve computer-related technology by allowing computer performance of a function not previously performable by a computer (see page 2 of the McRo-Bascom memo). The solution appears to be more of a business-driven solution rather than a technical one. In addition, McRO had no evidence that the process previously used by animators is the same as the process required by the claims. The Applicant’s claimed limitations and originally filed specification provide no evidence that the claimed process/functions are any different than what would be done without a computer, where there are no adjustments to the mental process to accommodate implementation by computers. Applicant’s arguments are not persuasive.
With regard to the limitations of claims 1-7, 9-19, and 21-22, Applicant argues that the claims are allowable over 35 USC 103 because the claim amendments overcome the current art rejection. The Examiner respectfully disagrees. Please see the updated rejection below since amendments by Applicant require additional reference to the Examiner’s art rejection.
Applicant argues the matrices are not taught by the cited prior art. The Examiner respectfully disagrees. The Examiner asserts Dhama et al. teach generating, based on the set of input features, a hidden Markov model including an initial state matrix, a transition matrix, and an emission matrix (See Figure 7A, Figure 7B, Paragraph 0037 – “utilize a deep Markov model which is trained based, at least in part, on past customer spending features of the cardholder. In one embodiment, the deep Markov model is a hidden Markov model with a variational inference architecture”, Paragraph 0070, Paragraph 0072 – “The transitions between the hidden states are also stochastic processes and are governed by a transition probability matrix”, Paragraph 0074, Paragraph 0086, Paragraph 0090, Paragraph 0096, Paragraph 0100 – “The transmission network model 308 takes a sequence of a set of values of transition probability values of each of the hidden states as input and outputs the next set of values of the next transition probability values for the hidden states”, Paragraph 0102 – “The emission network model 310 takes a set of values of emission probabilities of each of the amount bands (i.e., hidden states) as an input and generates a set of mean and standard deviations of the customer spending features”, and Paragraph 0121), where Applicant’s claims recite generic use of the matrices and does not specifically define what the matrices actually entail. Everything is the claims is recited at a high level, where the Examiner recommends specifically defining how these generic features (e.g. HMM and matrices) are being implemented to determine the predictions. Applicant’s arguments are not persuasive.
The Examiner again strongly recommends rolling up claim 3 into the independent claims to overcome the prior art 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 1-7, 9-19, and 21-22 are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter;
When considering subject matter eligibility under 35 U.S.C. 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter. If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea), and if so, it must additionally be determined whether the claim is a patent-eligible application of the exception. If an abstract idea is present in the claim, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself.
In the instant case (Step 1), claims 9-15 are directed toward a process, claims 16-19 and 22 are directed toward a product, and claims 1-7 and 21 are directed toward a system; which are statutory categories of invention.
Additionally (Step 2A Prong One), the independent claims are directed toward a system, comprising: a memory component; and one or more processing devices coupled to the memory component, the one or more processing devices configured to perform operations comprising: generating a set of input features based on user account data associated with a user account; generating, based on the set of input features, a hidden Markov model including an initial state matrix, a transition matrix, and an emission matrix; generating, based on the initial state matrix, the transition matrix, and the emission matrix of the hidden Markov model, a predicted subscription probability matrix including probability values representing potential account interactions between the user account and a set of computing applications; modifying one or more probability values of the predicted subscription probability matrix to form a modified predicted subscription probability matrix; determining a predicted account interaction metric for the user account based on the modified predicted subscription probability matrix; and allocating a set of computing resources for the set of computing applications based on the predicted account interaction metric for the user account (Organizing Human Activity and Mathematical Relationships), which are considered to be abstract ideas (See MPEP 2106.05). The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Organizing Human Activity because the claimed limitations are analyzing input data to determine a predicted subscription probability matrix for analyzing how human users will interact with different computing applications, which is analyzing how humans interact for commercial purposes. The steps/functions disclosed above and in the independent claims are directed toward the abstract idea of Mathematical Relationships because the claimed limitations are analyzing input data to determine a predicted subscription probability matrix for analyzing how human users will interact with different computing applications, with specific recitation of a hidden Markov model using matrices, which is math.
Dependent claims 2-7, 10-15, 17-19, and 21-22 further narrow the abstract idea identified in the independent claims, where any additional elements introduced are discussed below
Step 2A Prong Two: In this application, even if not directed toward the abstract idea, the independent claims additionally recite “a system, comprising: a memory component; and one or more processing devices coupled to the memory component, the one or more processing devices to perform operations comprising; allocating a set of computing resources for the set of computing applications (claim 1)”; “processing circuitry executing a feature generation module; by the processing circuitry executing an account interaction prediction module; by the processing circuitry executing a transformer encoder module; allocating, by the processing circuitry executing an account management system, a set of computing resources for the set of computing applications (claim 9)”; “non-transitory computer-readable medium storing executable instructions, which when executed by one or more processing devices, cause the one or more processing devices to perform operations; allocating a set of computing resources for the set of computing applications (claim 16)”, which are additional elements that do no integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed structure merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05) and are recited at such a high level of generality. These limitations can also be viewed as nothing more than an attempt to generally link the use of the judicial exception to the technological environment of a computer. Even when viewed in combination, the additional elements in the claims do no more than use the computer components as a tool. There is no change to the computer or other technology that is recited in the claim, and thus the claims do not improve computer functionality or other technology.
In addition, dependent claims 2-7, 10-15, 17-19, and 21-22 further narrow the abstract idea and dependent claims 12 and 21 additionally recite “by the processing circuitry executing a price mapper module (claim 12)”; “using an initial state neural network; using a transition neural network (claim 21)” which do not account for additional elements that integrate the judicial exception (e.g. abstract idea) into a practical application because the claimed limitation is recited at such a high level of generality it merely adds the words to apply it with the judicial exception and mere instructions to implement an abstract idea on a computer (See MPEP 2106.05).
Step 2B: When analyzing the additional element(s) and/or combination of elements in the claim(s) other than the abstract idea per se the claim limitations amount(s) to no more than: a general link of the use of an abstract idea to a particular technological environment and merely amounts to the application or instructions to apply the abstract idea on a computer (See MPEP 2106.05). Further, Method; System; and Product Independent claims 1, 9, and 16 recite “a system, comprising: a memory component; and one or more processing devices coupled to the memory component, the one or more processing devices to perform operations comprising; allocating a set of computing resources for the set of computing applications (claim 1)”; “processing circuitry executing a feature generation module; by the processing circuitry executing an account interaction prediction module; by the processing circuitry executing a transformer encoder module; allocating, by the processing circuitry executing an account management system, a set of computing resources for the set of computing applications (claim 9)”; “non-transitory computer-readable medium storing executable instructions, which when executed by one or more processing devices, cause the one or more processing devices to perform operations; allocating a set of computing resources for the set of computing applications (claim 16)”; however, these elements merely facilitate the claimed functions at a high level of generality and they perform conventional functions and are considered to be general purpose computer components which is supported by Applicant’s specification in Paragraphs 0196-0199 and Figures 12 and 19. The Applicant’s claimed additional elements are mere instructions to implement the abstract idea on a general purpose computer and generally link of the use of an abstract idea to a particular technological environment. When viewed as a whole, these additional claim element(s) do not provide meaningful limitation(s) to transform the abstract idea into a patent eligible application of the abstract idea such that the claim(s) amounts to significantly more than the abstract idea itself.
In addition, claims 2-7, 10-15, 17-19, and 21-22 further narrow the abstract idea identified in the independent claims. The Examiner notes that the dependent claims merely further define the data being analyzed and how the data is being analyzed. Similarly, claims 12 and 21 additionally recite “by the processing circuitry executing a price mapper module (claim 12)”; “using an initial state neural network; using a transition neural network (claim 21)” which do not account for additional elements that amount to significantly more than the abstract idea because the claimed price mapper module merely amounts to the application or instructions to apply the abstract idea on a computer and does not move beyond a general link of the use of an abstract idea to a particular technological environment (See MPEP 2106.05). The additional limitations of the independent and dependent claim(s) when considered individually and as an ordered combination do not amount to significantly more than the abstract idea. The examiner has considered the dependent claims in a full analysis including the additional limitations individually and in combination as analyzed in the independent claim(s). Therefore, the claim(s) are rejected under 35 U.S.C. 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries set forth in Graham v. John Deere Co., 383 U.S. 1, 148 USPQ 459 (1966), that are applied 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.
Claims 1-2, 4-7, 9-10, 12-17, 19, and 21-22 is/are rejected under 35 U.S.C. 103 as being unpatentable over Dhama et al. (US 2022/0335429 A1) in view of Kim et al. (US 2024/0325929 A1).
Regarding Claim 1: Dhama et al. teach a system, comprising: a memory component; and one or more processing devices coupled to the memory component, the one or more processing devices to configured perform operations comprising (See Figures 1-2):
generating a set of input features based on user account data associated with a user account (See Paragraph 0096 – “The bands of probable amount balance are created based on the inputs provided by the administrators and/or merchants”, Paragraph 0100 – “The transmission network model 308 takes a sequence of a set of values of transition probability values of each of the hidden states as input and outputs the next set of values of the next transition probability values for the hidden states”, and Paragraph 0126);
generating, based on the set of input features, a hidden Markov model including an initial state matrix, a transition matrix, and an emission matrix (See Figure 7A, Figure 7B, Paragraph 0037 – “utilize a deep Markov model which is trained based, at least in part, on past customer spending features of the cardholder. In one embodiment, the deep Markov model is a hidden Markov model with a variational inference architecture”, Paragraph 0070, Paragraph 0072 – “The transitions between the hidden states are also stochastic processes and are governed by a transition probability matrix”, Paragraph 0074, Paragraph 0086, Paragraph 0090, Paragraph 0096, Paragraph 0100 – “The transmission network model 308 takes a sequence of a set of values of transition probability values of each of the hidden states as input and outputs the next set of values of the next transition probability values for the hidden states”, Paragraph 0102 – “The emission network model 310 takes a set of values of emission probabilities of each of the amount bands (i.e., hidden states) as an input and generates a set of mean and standard deviations of the customer spending features”, and Paragraph 0121);
generating, based on the initial state matrix, the transition matrix, and the emission matrix of the hidden Markov model, a predicted “spend” probability matrix including probability values representing potential account interactions between the user account and a set of computing applications (See Paragraph 0072 – “a transition probability matrix”, Paragraph 0086 – “The server system predicts a hidden state of the DMM and estimates a current emission probability associated with the hidden state. Here, the current emission probability indicates a successful transaction. A likelihood score of being the card-on-file payment transaction getting approved within the next particular time window (e.g., 6 hours) is determined based on the current emission probability value and the payment amount (i.e., $29)”, Paragraph 0098 – “first define types of probability distributions for the hidden states and the customer feature vectors (i.e., observation vector) and attempt to estimate parameters of the probability distributions using a neural network”, Paragraph 0102, and Paragraph 0127 – “the emission network model takes emission probabilities of each hidden state as an input and generates means and standard deviation (S.D.) corresponding to each customer spending features underlying in each hidden state as an output”);
modifying one or more probability values of the predicted “spend” probability matrix to form a modified predicted “spend” probability matrix (See Paragraph 0072 – “a transition probability matrix”, Paragraph 0075 – “update the hidden state associated with the cardholder 120 with every transaction at the merchants”, Paragraph 0086 – “The server system predicts a hidden state of the DMM and estimates a current emission probability associated with the hidden state. Here, the current emission probability indicates a successful transaction. A likelihood score of being the card-on-file payment transaction getting approved within the next particular time window (e.g., 6 hours) is determined based on the current emission probability value and the payment amount (i.e., $29)”, Paragraph 0102, Paragraph 0103 – “the training engine 222 is configured to update/refresh the DMM for the cardholder 120 based on the customer spending on a timely basis”, and Paragraph 0127 – “the emission network model takes emission probabilities of each hidden state as an input and generates means and standard deviation (S.D.) corresponding to each customer spending features underlying in each hidden state as an output”);
determining a predicted account interaction metric for the user account based on the modified predicted “spend” probability matrix (See Paragraph 0080 – “the prediction engine 224 is configured to determine a current emission probability associated with the hidden state”, Paragraph 0086 – “The server system predicts a hidden state of the DMM and estimates a current emission probability associated with the hidden state”, Paragraph 0113, and Paragraph 0128);
and allocating a set of computing resources for the set of computing applications based on the predicted account interaction metric for the user account (See Figure 6, Figure 8, Paragraph 0031, Paragraph 0106, Paragraph 0132 – “allocation and usage of the components of the user device”).
Dhama et al. do not specifically disclose a subscription prediction. However, Kim et al. further teach subscription prediction (See Paragraph 0009, Paragraph 0045 – “The acquisition module 120 may acquire subscription information, status information, and action information of each user playing the mobile F2P game based on event log data in the DB 200, and train the prediction model 300 or predict the LTV of the user based thereon. In addition, if necessary for training the prediction model 300, the acquisition module 120 may additionally acquire the LTV of each user”, Paragraph 0060 – “the prediction module 150 may input data on subscription information of a predetermined user”, and claim 1).
The teachings of Dhama et al. and Kim et al. are related because both are analyzing customers to make determinations. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the teachings of Dhama et al. to incorporate the subscription predictions of Kim et al. in order to determine if there are ways to improve subscription models to increase profit.
Regarding Claim 2: Dhama et al. in view of Kim et al. teach the limitations of claim 1. Dhama et al. do not specifically disclose the following. However, Kim et al. further teach wherein the predicted account interaction metric comprises a lifetime value (LTV) associated with the user account over a defined time period (See Paragraph 0009, Paragraph 0045 – “The acquisition module 120 may acquire subscription information, status information, and action information of each user playing the mobile F2P game based on event log data in the DB 200, and train the prediction model 300 or predict the LTV of the user based thereon. In addition, if necessary for training the prediction model 300, the acquisition module 120 may additionally acquire the LTV of each user”, Paragraph 0060 – “the prediction module 150 may input data on subscription information of a predetermined user”, and claim 1).
The teachings of Dhama et al. and Kim et al. are related because both are analyzing customers to make determinations. Therefore, it would have been obvious to one of ordinary skill in the art at the effective filing date of the claimed invention to have modified the teachings of Dhama et al. to incorporate the subscription predictions of Kim et al. in order to determine if there are ways to improve subscription models to increase profit.
Regarding Claim 4: Dhama et al. in view of Kim et al. teach the limitations of claim 1. Dhama et al. further teach wherein the one or more processing devices are configured to perform operations comprising: generating an initial price map including pricing values for a set of products or services provided by the set of computing applications; generating a set of price adjustments to the initial price map based on the set of input features; and modifying the initial price map to form a final price map for the user account based on the initial price map and the set of price adjustments (See Paragraphs 0090-0097 – “train the deep Markov model (DMM) for each cardholder based, at least in part, on associated customer feature vectors in past time … create, or design, a plurality of hidden states for each cardholder in a hidden space vector of the DMM using a hidden state generation model 306. In an embodiment, each hidden state in the plurality of hidden states is associated with a band of probable amount balance available in the payment account associated with the cardholder 120. The bands of probable amount balance are created based on the inputs provided by the administrators and/or merchants … The vocabulary of the plurality of hidden states has been designed in such a way that the DMM outputs a probability corresponding to each amount band so that when the merchant requests for a particular amount for a specific cardholder, a probability score can be provided to the merchant”).
Regarding Claim 5: Dhama et al. in view of Kim et al. teach the limitations of claim 1. Dhama et al. further teach the one or more processing devices to perform operations comprising generating an initial state matrix including a plurality of initial state values corresponding to the plurality of hidden states of the hidden Markov model for the user account based on the set of input features (See Paragraph 0072 – “a transition probability matrix”, Paragraph 0076 – “learn initial latent state probabilities, emissions probability distributions, and transition probability distributions from a set of training data”, Paragraph 0086 – “The server system predicts a hidden state of the DMM and estimates a current emission probability associated with the hidden state”, Paragraph 0095, Paragraph 0102, and Paragraph 0127).
Regarding Claim 6: Dhama et al. in view of Kim et al. teach the limitations of claim 1. Dhama et al. further teach the one or more processing devices to perform operations including generating a transition matrix comprising a plurality of transition values corresponding to a plurality of hidden states of a hidden Markov model for the user account based on the set of input features (See Paragraph 0072 – “a transition probability matrix”, Paragraph 0075 – “update the hidden state associated with the cardholder 120 with every transaction at the merchants”, Paragraph 0086 – “The server system predicts a hidden state of the DMM and estimates a current emission probability associated with the hidden state. Here, the current emission probability indicates a successful transaction. A likelihood score of being the card-on-file payment transaction getting approved within the next particular time window (e.g., 6 hours) is determined based on the current emission probability value and the payment amount (i.e., $29)”, Paragraph 0102, Paragraph 0103 – “the training engine 222 is configured to update/refresh the DMM for the cardholder 120 based on the customer spending on a timely basis”, and Paragraph 0127 – “the emission network model takes emission probabilities of each hidden state as an input and generates means and standard deviation (S.D.) corresponding to each customer spending features underlying in each hidden state as an output”).
Regarding Claim 7: Dhama et al. in view of Kim et al. teach the limitations of claim 1. Dhama et al. further teach the one or more processing devices to perform operations including generating an emission matrix comprising a plurality of emission values corresponding to the plurality of hidden states of the hidden Markov model for the user account based on the set of input features (See Paragraph 0080 – “the prediction engine 224 is configured to determine a current emission probability associated with the hidden state”, Paragraph 0086 – “The server system predicts a hidden state of the DMM and estimates a current emission probability associated with the hidden state”, Paragraph 0113, and Paragraph 0128).
Regarding Claims 9-10, 12-17, and 19: Claims 9-10, 12-17, and 19 recite limitations already addressed by the rejections of claims 1-2 and 4-7 above; therefore the same rejections apply.
Regarding Claim 21: Dhama et al. in view of Kim et al. teach the limitations of claim 1. Dhama et al. further teach wherein the initial state matrix is generated using an initial state neural network, the transition matrix is generated using a transition neural network, and the emission matrix is generated using an emission neural network (See Figure 7C, Paragraphs 0037-0038, Paragraph 0043, Paragraph 0080, Paragraph 0098, Paragraph 0113, and Paragraph 0122).
Regarding Claim 22: Dhama et al. in view of Kim et al. teach the limitations of claim 16. Dhama et al. further teach wherein the hidden Markov model includes an ensemble of multiple binary-classification hidden Markov sub-models that share the initial state matrix and the transition matrix (See Paragraph 0071, Paragraph 0090, Paragraph 0099, Paragraph 0113, and Paragraph 0122).
Allowable over 35 USC 103
Claims 3, 11, and 18 are allowable over the prior art, but remain rejected under §101 for the reasons set forth above. Dependent claims 3, 11, and 18 disclose a system, product, and method for analyzing input data to determine a predicted subscription probability matrix for analyzing how human users will interact with different computing applications by generating a predicted subscription probability matrix that is concatenated with a hidden Markov model to concatenate hidden states and multiplied by an emission matrix to form a modified predicted subscription probability matrix.
Regarding a possible 103 rejection: The closest prior art of record is:
Dhama et al. (US 2022/0335429 A1) – which discloses using hidden Markov model to analyze customer interactions.
Kim et al. (US 2024/0325929 A1) – which discloses analyzing subscription probabilities to determine LTV’s for customers.
Urdiales et al. (US 2023/0418793 A1) – which discloses a plurality of different analysis methods using machine learning.
The prior art of record neither teaches nor suggests all particulars of the limitations as recited in claims 3, 11, and 18, such as analyzing input data to determine a predicted subscription probability matrix for analyzing how human users will interact with different computing applications by generating a predicted subscription probability matrix that is concatenated with a hidden Markov model to concatenate hidden states and multiplied by an emission matrix to form a modified predicted subscription probability matrix. While individual features may be known per se, there is no teaching or suggestion absent applicants’ own disclosure to combine these features other than with impermissible hindsight and the combination/arrangement of features are not found in analogous art. Specifically the claimed “a system, comprising: a memory component; and one or more processing devices coupled to the memory component, the one or more processing devices configured to perform operations comprising: generating a set of input features based on user account data associated with a user account; generating, based on the set of input features, a hidden Markov model including an initial state matrix, a transition matrix, and an emission matrix; generating, based on the initial state matrix, the transition matrix, and the emission matrix of the hidden Markov model, a predicted subscription probability matrix including probability values representing potential account interactions between the user account and a set of computing applications; modifying one or more probability values of the predicted subscription probability matrix to form a modified predicted subscription probability matrix; determining a predicted account interaction metric for the user account based on the modified predicted subscription probability matrix; and allocating a set of computing resources for the set of computing applications based on the predicted account interaction metric for the user account … concatenating the predicted subscription probability matrix with a concatenated hidden states matrix; generating a concatenated hidden states residual matrix; adding the concatenated hidden states matrix and the concatenated hidden states residual matrix to form a modified concatenated hidden states matrix; and multiplying the modified concatenated hidden states matrix and an emission matrix to form the modified predicted subscription probability matrix (as required by claims 3, 11, and 18)”, thus rendering claims 3, 11, and 18 as allowable over the prior art.
Conclusion
The prior art made of record, but not relied upon is considered pertinent to Applicant's disclosure is listed on the attached PTO-892 and should be taken into account / considered by the Applicant upon reviewing this office action.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to MATTHEW D HENRY whose telephone number is (571)270-0504. The examiner can normally be reached on Monday-Thursday 9AM-5PM.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, BRIAN EPSTEIN can be reached on (571)-270-5389. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/MATTHEW D HENRY/Primary Examiner, Art Unit 3625
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