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 .
Style
In this action unitalicized bold is used for claim language, while italicized bold is used for emphasis.
Information Disclosure Statement
No IDS has been filed in this application. Included with this office action is a copy of an academic paper authored by several inventors of this application, and having the same name as this application. That document lists various other publications in the field of this application. Applicant is reminded that any documents which are material to patentability should be filed in an IDS.
Applicant Reply
“The claims may be amended by canceling particular claims, by presenting new claims, or by rewriting particular claims as indicated in 37 CFR 1.121(c). The requirements of 37 CFR 1.111(b) must be complied with by pointing out the specific distinctions believed to render the claims patentable over the references in presenting arguments in support of new claims and amendments. . . . The prompt development of a clear issue requires that the replies of the applicant meet the objections to and rejections of the claims. Applicant should also specifically point out the support for any amendments made to the disclosure. See MPEP § 2163.06. . . . An amendment which does not comply with the provisions of 37 CFR 1.121(b), (c), (d), and (h) may be held not fully responsive. See MPEP § 714.” MPEP § 714.02. Generic statements or listing of numerous paragraphs do not “specifically point out the support for” claim amendments. “With respect to newly added or amended claims, applicant should show support in the original disclosure for the new or amended claims. See, e.g., Hyatt v. Dudas, 492 F.3d 1365, 1370, n.4, 83 USPQ2d 1373, 1376, n.4 (Fed. Cir. 2007) (citing MPEP § 2163.04 which provides that a ‘simple statement such as ‘applicant has not pointed out where the new (or amended) claim is supported, nor does there appear to be a written description of the claim limitation ‘___’ in the application as filed’ may be sufficient where the claim is a new or amended claim, the support for the limitation is not apparent, and applicant has not pointed out where the limitation is supported.’)” MPEP § 2163(II)(A).
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 1-2, 8-11, and 17-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) and the claims as a whole, considering all claim elements both individually and in combination, do not amount to significantly more.
Step 1: Is the claim to a process, machine, manufacture, or composition of matter?
All claims are found to be directed to one of the four statutory categories, unless otherwise indicated in this action.
Step 2A Prongs One and Two (Alice Step 1): According to Office guidance, claims that read on math do not recite an abstract idea at step 2A1, when the claims fail to refer to the math by name.1 The MPEP also equates “recit[ing] a judicial exception” with “state[ing]” or “describ[ing]” an abstract idea in the claims.2 Consistent with this guidance, an abstract idea may be first recited in a dependent claim even though the independent claims read on that abstract idea. Claim limitations which recite any of the abstract idea groupings set forth in the manual are found to be directed, as a whole, to an abstract idea unless otherwise indicated.3 The claims do not recite additional elements that integrate the abstract ideas into a practical application.4 To confer patent eligibility to an otherwise abstract idea, claims may recite a specific means or method of solving a specific problem in a technological field.5
Independent Claims
1. A method for modeling sequences of events, the method being implemented by at least one processor, the method comprising: (This reads on implementing the abstract ideas below using generic computer components.) receiving, by the at least one processor and from a plurality of client devices running an interface application and connected to a network, (Sending and receiving data using generic computer components is mere extra-solution activity.) data that corresponds to a plurality of event sequences; (This merely limits the data environment to a particular field of use.) wherein each of the plurality of event sequences involves a network of actors, and wherein the plurality of event sequences are performed asynchronously and at irregular intervals; (This merely limits the data corresponding to a plurality of event sequences to a field of use associated with a particular data environment.) retrieving, by the at least one processor and from a network database, multivariate Hawkes processes parameters based on historical event sequences; (Sending and receiving/retrieving data using generic computer components is mere extra-solution activity.) generating a mixture of multivariate Hawkes processes model based on the plurality of event sequences and the multivariate Hawkes processes parameters, (This reads on both math and on a process.) wherein each of the plurality of event sequences correspond to interactions of an actor node belonging to a cluster with a product indicating an event type, and wherein the plurality of event sequences include events of different types occurring at differing locations with differing number of actors (This merely limits the data corresponding to a plurality of event sequences to a field of use associated with a particular data environment.) and adjusting the mixture of multivariate Hawkes processes model by applying an online learning algorithm to the generated mixture of multivariate Hawkes processes model, (Adjusting the model reads on both math and on a mental process.) wherein the adjusting includes updating a plurality of responsibilities that relates to the plurality of event sequences and updating Hawkes processes parameters among the retrieved multivariate Hawkes process parameters that relate to the plurality of event sequences, (Updating the model reads on both a mathematical operation (e.g. updating the parameters) and on a mental process.) wherein the updated Hawkes processes parameters include intensity parameters, (This merely limits to a field of use associated with a particular data environment.) wherein the applying of the online learning algorithm includes maximizing an evidence lower bound (ELBO) function with respect to a set of responsibility parameters that correspond to the plurality of event sequences, and performing a stochastic gradient update on each respective intensity parameter and each respective impact function that correspond to the plurality of event sequences; (Updating the model reads on both a mathematical operation and on a mental process.) determining, via the adjusted mixture of multivariate Hawkes processes model including the updated intensity parameters and for a particular event sequence from among the plurality of event sequences, a latent cluster of actors that have performed respective actions within the particular event sequence wherein a cluster is composed of different actors at differing times, and wherein the latent cluster of actors are determined based on the updated intensity parameters; (This is a mere instruction to apply the mental/mathematical process limited to a specific data environment associated with the field of use of time series events.) determining, via the adjusted mixture of multivariate Hawkes processes model including the updated intensity parameters and for the particular event sequence from among the plurality of event sequences, at least one causal relationship between at least two events included in the particular event sequence based on the determined latent cluster of actors, (This is a mere instruction to apply the mental/mathematical process limited to a specific data environment associated with the field of use of time series events.) displaying, on a display, latent cluster assignments of the actors to display intensity parameters of the Hawkes processes corresponding to each respective cluster; (Displaying results is mere extra-solution activity, implemented using generic computer components.) and predicting, for the particular event sequence via the adjusted mixture of multivariate Hawkes processes model including the updated intensity parameters and based on the at least one causal relationship between the at least two events included in the particular event sequence, a time of a next event and a type of the next event wherein the online learning algorithm is applied to the generated mixture of multivariate Hawkes processes model (This reads on a mental/mathematical process.) for reducing per-iteration computational complexity of the generated mixture of multivariate Hawkes processes model for increased scalability of the mixture of multivariate Hawkes processes model. (This language merely recites an intended use for the claim, without limiting to a particular structure or requiring steps to be performed. See MPEP §§ 2111.04 and 2103.)
Independent claim 10 is rejected for the reasons given in the rejection of claim 1. The claim also recites “A computing apparatus for modeling sequences of events, the computing apparatus comprising: a processor; a display; a memory; and a communication interface coupled to each of the processor and the memory, wherein the processor is configured to” carry out the operations of claim 1. This is merely an instruction to apply the judicial exception using generic computer components.
Independent claim 19 is rejected for the reasons given in the rejection of claim 1. The claim also recites “A non-transitory computer readable storage medium storing instructions for modeling sequences of events, the storage medium comprising executable code which, when executed by a processor, causes the processor to” carry out the operations of claim 1. This is merely an instruction to apply the judicial exception on a computer.
Step 2B (Alice Step 2): The rejected claims do not recite additional elements that amount to significantly more than the judicial exception.
All additional limitations that do not integrate the claimed judicial exception into a practical application also fail to amount to significantly more, for the reasons given at step 2A2. All limitations found to be extra-solution activity at step 2A2 are found to be WURC, including limitations that read on mere data gathering, data storage, and data input/output/transfer. The independent claims substantially recite “receiving, by the at least one processor and from a plurality of client devices running an interface application and connected to a network,” “retrieving, by the at least one processor and from a network database, multivariate Hawkes processes parameters based on historical event sequences,” and “displaying, on a display, latent cluster assignments of the actors to display intensity parameters of the Hawkes processes corresponding to each respective cluster[.]” Generic data input/output, storage, repetitive processing operations, and generic display of information and have been found to be generic WURC operations that do not transform the abstract idea into patent eligible subject matter, at the Alice step two analysis.6 Other aspects of generic computing have also been found to be WURC.7 Further, the description itself may provide support for a finding that claim elements are WURC. The analysis under § 112(a) as to whether a claim element is “so well-known that it need not be described in detail in the patent specification” is the same as the analysis as to whether the claim element is widely prevalent or in common use.8 Similarly, generic descriptions in the Specification of claimed components and features has been found to support a conclusion that the claimed components were conventional.9 Improvements to the relevant technology may support a finding that the claims include a patent eligible inventive concept. But some mechanism that results in any asserted improvements must be recited in the claim, and the Specification must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing the improvement.10 This applies to the dependent claims below.
Dependent Claims:
2. The method of claim 1, wherein the online learning algorithm comprises an expectation step (E-step) that corresponds to the updating of the plurality of responsibilities that relates to the plurality of event sequences and a maximization step (M-step) that corresponds to the updating of the Hawkes processes parameters that relate to the plurality of event sequences. (This reads on math and on mental processes.)
8. The method of claim 1, further comprising displaying, on the display via a graphical user interface (GUI), a result of the adjusting of the model. (Displaying results is extra-solution activity and WURC as explained in the paragraph outlining step 2B above.)
9. The method of claim 1, wherein the plurality of event sequences includes at least one from among a first event sequence that relates to a banking activity, a second event sequence that relates to a shopping activity, and a third event sequence that relates to a health care activity. (This merely limits to various fields of use.)
Claims 11 and 17-18 are rejected for the reasons given in the rejections of claims 2 and 8-9, respectively.
Claim 20 is rejected for the reasons given in the rejection of claim 2.
All dependent claims are rejected as containing the material of the claims from which they depend.
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 1-2, 8-11, and 17-20 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.
All independent claims substantially recite “displaying, on a display, latent cluster assignments of the actors to display intensity parameters of the Hawkes processes corresponding to each respective cluster[.]” It is not clear what relationships are being displayed. The display of “latent cluster assignments of the actors” sounds like clusters are being displayed, but “of the actors” implies that some label of the group of actors in the cluster is also displayed. More importantly, the language “to display intensity parameters of the Hawkes process corresponding to each cluster” sounds like an x/y chart with clusters on one axis and “display intensity parameters” on the other. But without some way of measuring either, it is not clear how this would work. Ultimately it is not clear what must be on the display based on this language. The Specification does not include a drawing corresponding to the claimed “displaying, on a display.”
All dependent claims are rejected as including the material of the claims from which they depend.
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 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.
Claims 1-2, 9-11, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Xu (A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering, 2017), Xu2 (A Dirichlet Mixture Model of Hawkes Processes for Event Sequence Clustering, 2017), Fang (Online Community Detection for Event Streams on Networks, 2020), Okawa (US 2022/0284313, PCT filed 2019), and Kapil (Stochastic vs Batch Gradient Descent, 2019).
1. A method for modeling sequences of events, the method being implemented by at least one processor, the method comprising: receiving, by the at least one processor (“The source code can be found at https://github.com/HongtengXu/Hawkes-Process-Toolkit.” Xu p. 8. One of ordinary skill in the art would infer from the inclusion of source code, that generic computing components such as a processor and memory would be used to implement the claimed techniques.) and from a plurality of client devices running an interface application and connected to a network, data that corresponds to a plurality of event sequences, wherein each of the plurality of event sequences involves a network of actors, and wherein the plurality of event sequences are performed asynchronously and at irregular intervals; (“Experiments on both synthetic and real-world data show that the clustering method based on our model can learn structural triggering patterns hidden in asynchronous event sequences robustly and achieve superior performance on clustering purity and consistency compared to existing methods.” Xu p. 1. “Typical examples include the viewing records of users in an IPTV system, the electronic health records of patients in hospitals, among many others. All of these data are so-called event sequences, each of which contains a series of events with different types in the continuous time domain, e.g., when and which TV program a user watched, when and which care unit a patient is transferred to. Given a set of event sequences, an important task is learning their clustering structure robustly.” Xu p. 1.) retrieving, by the at least one processor and from a network database, multivariate Hawkes processes parameters based on historical event sequences (Xu teaches “A Hawkes process [13] is a kind of point processes modeling complicated event sequences in which historical events have influences on current and future ones.” Xu P. 2.) generating a mixture of multivariate Hawkes processes model based on the plurality of event sequences and the multivariate Hawkes processes parameters, wherein each of the plurality of event sequences correspond to interactions of an actor node belonging to a cluster with a product indicating an event type, and wherein the plurality of event sequences include events of different types occurring at differing locations with differing number of actors; (“Additionally, the flexibility of our model allows to incorporate the clustering structure event types into learning framework.” Xu Abstract. Note that an event including an actor relates the actor to the cluster of the event. “In many practical situations, we need to deal with a large amount of irregular and asynchronous sequential data observed in continuous time. The applications include the user viewing records in an IPTV system (when and which TV programs are viewed), and the patient records in hospitals (when and what diagnoses and treatments are given), among many others. All of these data can be viewed as event sequences containing multiple event types and modeled via multi-dimensional point processes.” Xu p. 1. Take the previous two examples: clustering IPTV users according to their viewing records is beneficial to the program recommendation system and the ads serving system; clustering patients according to their health records helps hospitals to optimize their medication resources.” Xu P. 1. “To make concrete progress, we propose a Dirichlet Mixture model of Hawkes Processes (DMHP for short) and study its performance on event sequence clustering in depth. In this model, the event sequences belonging to different clusters are modeled via different Hawkes processes.” Xu P. 2. “Given a set of event sequences S . . . contains a series of events ci . . . and their time stamps ti . . . , we model them via a mixture model of Hawkes processes.” Xu P. 2.) and adjusting the mixture of multivariate Hawkes processes model by applying an online learning algorithm to the generated mixture of Hawkes processes model, (The previously cited art does not directly address online learning.
Fang teaches: “A common goal in network modeling is to uncover the latent community structure present among nodes. For many real-world networks, observed connections consist of events arriving as streams, which are then aggregated to form edges, ignoring the temporal dynamic component. A natural way to take account of this temporal dynamic component of interactions is to use point processes as the foundation of the network models for community detection. Computational complexity hampers the scalability of such approaches to large sparse networks. To circumvent this challenge, we propose a fast online variational inference algorithm for learning the community structure underlying dynamic event arrivals on a network using continuous-time point process latent network models.” Fang Abstract.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Fang because it simplifies computational complexity in network modeling where modeling data may arrive in streams.)
wherein the adjusting includes updating a plurality of responsibilities that relates to the plurality of event sequences and updating Hawkes processes parameters among the retrieved multivariate Hawkes process parameters that relate to the plurality of event sequences, (Xu teaches “Our model, however, aims at finding the clustering structure across different sequences. The intensity of each event is generated via a single Hawkes process, while the likelihood of an event sequence is a mixture of likelihood functions from different Hawkes processes.” Xu P. 3. “We propose a variational Bayesian inference algorithm to learn the DMHP model in a nested Expectation-Maximization (EM) framework.” Xu P. 2. “Our algorithm is in a nested EM framework, where the outer iteration corresponds to the loop of E-step and M-step and the inner iteration corresponds to the inner EM in the M-step.” Xu P. 5. See also Xu section 4.1-4.2 describing the E-step used to “updating responsibility” and the M-step a used to update parameters.) wherein the updated Hawkes processes parameters include intensity parameters, (See Xu P. 2 Equation 1 and associated description.) wherein the applying of the online learning algorithm includes maximizing an evidence lower bound (ELBO) function with respect to a set of responsibility parameters that correspond to the plurality of event sequences, (Fang teaches “As seen in (8), it is difficult to compute this likelihood directly, which requires summation over exponentially many terms. An alternative approach is by using variational inference (Hoffman et al., 2013) methods to optimize the evidence lower bound (ELBO) instead of the log likelihood.” Fang P. 8.) and performing a stochastic gradient update on each respective intensity parameter and each respective impact function that correspond to the plurality of event sequences; (Xu teaches “Hawkes processes have a particular form of intensity: [Equation 1] where uc is the endogenous intensity capturing the peer influence. The decay in the influence of historical type-c0 events on the subsequent type-c events is captured via the so-called impact function -cc0 (t), which is nonnegative.” Xu page 2 (describing a Hawkes process.)
The previously cited art does not expressly teach use of stochastic gradient updates on intensity parameters and on impact functions.
Okawa teaches: “In the Hawkes process, an “intensity function” representing the occurrence probability of the event is assumed to have a self-exciting property. In other words, in the Hawkes process, a phenomenon whereby, when an event occurs, the occurrence probability of an event of the same type increases, or in other words, the value of the intensity function jumps, is modeled.” Okawa ¶23. “The magnitude of the effect of the event is expressed by parameters of the intensity function. The parameters of the intensity function are normally estimated from data using the maximum likelihood method or the like. The magnitude of the effect of the event is believed to vary according to the event type and external factors.” Okawa ¶24. “Analytical solutions or approximate solutions can be acquired for a large number of trigger functions h(.Math.), k(.Math.) from the integral included in the above formula. During learning, a set of the parameters of Ψ(.Math.), ϕ(.Math.). Hand the parameters of the trigger function h(.Math.), k(.Math.) with which to minimize the likelihood L is estimated. Any method may be used to optimize the parameters. The likelihood L in the above formula can be differentiated for all of the parameters and can therefore be optimized using a gradient method, for example. A backpropagation method can be applied as is likewise when a neural network is assumed as Ψ, ϕ.” Okawa ¶52. The claimed “impact function” reads on the intensity function of Okawa.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Okawa because gradients leverage the statistical nature of models to improve the models in the direction of an optimum.
Okawa does not state that the gradient descent taught in the reference is stochastic.
Kapil teaches “Advantages of Stochastic Gradient Descent 1. It is easier to fit into memory due to a single training sample being processed by the network 2. It is computationally fast as only one sample is processed at a time 3. For larger datasets it can converge faster as it causes updates to the parameters more frequently 4. Due to frequent updates the steps taken towards the minima of the loss function have oscillations which can help getting out of local minimums of the loss function (in case the computed position turns out to be the local minimum.)” Kapil PP. 3-4.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Kapil because modifying the gradient descent using in Okawa because it requires less resources and is computationally faster ordinary gradient descent.) determining, via the adjusted mixture of multivariate Hawkes processes model including the updated intensity parameters and for a particular event sequence from among the plurality of event sequences, a latent cluster of actors that have performed respective actions within the particular event sequence wherein a cluster is composed of different actors at differing times, and wherein the latent cluster of actors are determined based on the updated intensity parameters; (“The proposed model generates the event sequences with different clusters from the Hawkes processes with different parameters, and uses a Dirichlet distribution as the prior distribution of the clusters.” Xu P. 1, abstract. “In many practical situations, we need to deal with a huge amount of irregular and asynchronous sequential data. Typical examples include the viewing records of users in an IPTV system, the electronic health records of patients in hospitals, among many others. All of these data are so-called event sequences, each of which contains a series of events with different types in the continuous time domain, e.g., when and which TV program a user watched, when and which care unit a patient is transferred to. Given a set of event sequences, an important task is learning their clustering structure robustly. Event sequence clustering is meaningful for many practical applications. Take the previous two examples: clustering IPTV users according to their viewing records is beneficial to the program recommendation system and the ads serving system; clustering patients according to their health records helps hospitals to optimize their medication resources.” Xu P. 1. Hawkes processes have a particular form of intensity: [Equation 1] uc is the exogenous base intensity independent of the history while . . . the endogenous intensity capturing the peer influence. The decay in the influence of historical type-c0 events on the subsequent type-c events is captured via the so-called impact function -cc0 (t), which is nonnegative.” Xu P. 2.
The previously cited art does not expressly teach that the clustering is “latent.”
Xu2 teaches “Take the previous two examples: clustering IPTV users according to their viewing records is beneficial to the program recommendation system and the ads serving system; clustering patients according to their health records helps hospitals to optimize their medication resources.” Xu2 P. 1. “Here u . . . and A . . . are parameters of Hawkes processes . . .[.] Denote the latent variables indicating the labels of clusters as matrix Z[.]” Xu2 P. 3. “Focusing on the task of clustering event sequences, we investigate the sample complexity of our DMHP model and its learning algorithm. In particular, we want to show that the clustering method based on our model requires fewer samples than existing methods to identify clusters successfully.” Xu2 p. 5. “Taking the parameter as a representation of the clustering center, we can calculate the distance between two clusters as d = ||theta1 – theta2||. Assume that N1 < N2, we denote the first cluster as “minor” cluster, whose sample percentage is PI1 = N1/(N1+N2). Applying our DMHP model and its learning algorithm to the data generated with different d’s and pi1’s, we can calculate the F1 scores of the minor cluster w.r.t. {d, pi}. The high F1 score means that the minor cluster is identified with high accuracy. Fig. 2 visualizes the maps of F1 scores generated via different methods w.r.t. the number of events per sequence. We can find that the F1 score obtained via our DMHP-based method is close to 1 in most situations. Its identifiable area (yellow part) is much larger than that of the MMHP+DPGMM method consistently w.r.t. the number of events per sequence. The unidentifiable cases happen only in the following two situations: the parameters of different clusters are nearly equal (i.e., d [Wingdings font/0xE0] 0); or the minor cluster is extremely small (i.e., pi1 [Wingdings font/0xE0] 0). The enlarged version of Fig. 2 is given in the supplementary file.” Xu2 p. 6. “The principle is simple: because random sampling does not change the clustering structure of data, a clustering method with high consistency should preserve the pairwise relationships of samples in different trials.” Xu2 p. 7.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of to combine the teaching of Xu2 because using latent clustering can result in training of accurate models using less data. See sections of Xu2 pp.5-7 cited above.) determining, via the adjusted mixture of multivariate Hawkes processes model including the updated intensity parameters and for the particular event sequence from among the plurality of event sequences, at least one causal relationship between at least two events included in the particular event sequence based on the determined latent cluster of actors; (“Experiments on both synthetic and real-world data show that the clustering method based on our model can learn structural triggering patterns hidden in asynchronous event sequences robustly” Xu Abstract. “A Hawkes process [13] is a kind of point processes modeling complicated event sequences in which historical events have influences on current and future ones. It can also be viewed as a cascade of non-homogeneous Poisson processes [8, 34]. . . . Hawkes processes have a particular form of intensity: [Equation 1] uc is the exogenous base intensity independent of the history while . . . the endogenous intensity capturing the peer influence. The decay in the influence of historical type-c0 events on the subsequent type-c events is captured via the so-called impact function -cc0 (t), which is nonnegative.” Xu P. 2.) displaying, on a display, latent cluster assignments of the actors to display intensity parameters of the Hawkes processes corresponding to each respective cluster; and (See Xu Fig. 2 and Section 4.3 on page 6 of Xu. See also Xu2 Figs. 3b, 3c and Section 5.2.) predicting, for the particular event sequence via the adjusted mixture of multivariate Hawkes processes model including the updated intensity parameters and based on the at least one causal relationship between the at least two events included in the particular event sequence, a time of a next event and a type of the next event (“The proposed model generates the event sequences with different clusters from the Hawkes processes with different parameters, and uses a Dirichlet distribution as the prior distribution of the clusters.” Xu P. 1, abstract. “In many practical situations, we need to deal with a huge amount of irregular and asynchronous sequential data. Typical examples include the viewing records of users in an IPTV system, the electronic health records of patients in hospitals, among many others. All of these data are so-called event sequences, each of which contains a series of events with different types in the continuous time domain, e.g., when and which TV program a user watched, when and which care unit a patient is transferred to. Given a set of event sequences, an important task is learning their clustering structure robustly. Event sequence clustering is meaningful for many practical applications. Take the previous two examples: clustering IPTV users according to their viewing records is beneficial to the program recommendation system and the ads serving system; clustering patients according to their health records helps hospitals to optimize their medication resources.” Xu P. 1.) wherein the online learning algorithm is applied to the generated mixture of multivariate Hawkes processes model (This is obvious over the combination of references. As indicated in the rejection above, Xu teaches a mixture of multivariate Hawkes processes model. The secondary reference, Fang, teaches the application of online learning to Hawkes processes. See Fang, Abstract cited above. The motivation to combine the teachings above applies to application of an online algorithm to the Hawkes process of Xu.) for reducing per-iteration computational complexity of the generated mixture of multivariate Hawkes processes model for increased scalability of the mixture of multivariate Hawkes processes model. (This language merely recites an intended use for the claim, without limiting to a particular structure or requiring steps to be performed. See MPEP § 2111.04 and 2103.)
2. The method of claim 1, wherein the online learning algorithm comprises an expectation step (E-step) that corresponds to the updating of the plurality of responsibilities that relates to the plurality of event sequences and a maximization step (M-step) that corresponds to the updating of the Hawkes processes parameters that relate to the plurality of event sequences. (“We propose a variational Bayesian inference algorithm to learn the DMHP model in a nested Expectation-Maximization (EM) framework.” Xu P. 2. “Our algorithm is in a nested EM framework, where the outer iteration corresponds to the loop of E-step and M-step and the inner iteration corresponds to the inner EM in the M-step.” Zu P. 5. See also Xu section 4.1-4.2 describing the E-step used to “updating responsibilities” and the M-step a used to update parameters.)
9. The method of claim 1, wherein the plurality of event sequences includes at least one from among a first event sequence that relates to a banking activity, a second event sequence that relates to a shopping activity, and a third event sequence that relates to a health care activity. (Xu teaches “We focus on the clustering problem of the event sequences obeying Hawkes processes because Hawkes processes have been proven to be useful for describing real-world data in many applications, e.g., financial analysis [1], social network analysis [3, 51], system analysis [22], and e-health [30, 42].” Xu P. 2.)
Claims 10-11 and 18 are rejected for the reasons given in the rejections of claims 1-2 and 9, respectively.
Claims 19-20 are rejected for the reasons given in the rejections of claims 1-2, respectively.
Claims 8 and 17 are rejected under 35 U.S.C. 103 as being unpatentable over Xu, Xu2, Fang, Okawa, Kapil, and Adams (Benefits of the Graphical User Interface, 2018)
8. The method of claim 1, further comprising displaying, on the display via a graphical user interface (GUI), a result of the adjusting of the mixture of multivariate Hawkes processes model. (The earliest support for this claim language appears in the utility application filed 03/24/2022. No mention of a GUI is found in the provisional.
The previously cited art does not discuss a GUI.
Adams teaches “The graphical user interface (GUI; sometimes pronounced “gooey”) is used by most commercially popular computer operating systems and software programs today. It's the kind of interface that allows users to manipulate elements on the screen using a mouse, a stylus, or even a finger. This kind of interface allows word processing or web design programs, for example, to offer WYSIWYG (what you see is what you get) options. . . . Before GUI systems became popular, command line interface (CLI) systems were the norm. On these systems, users had to input commands using lines of coded text. The commands ranged from simple instructions for accessing files or directories to far more complicated commands that required many lines of code. . . . As you might imagine, GUI systems have made computers far more user-friendly than CLI systems.” Adams P. 1-2.
It would have been obvious to one of ordinary skill in the art before the effective filing date to combine the teaching of Adams because representing data using a graphical user interface it allows intuitive manipulation of information and does not require the user to learn command line operations.
Claim 17 is rejected for the reasons given in the rejection of claim 8.
Response to Arguments
Applicant's arguments filed 03/26/2026 have been fully considered but they are not persuasive.
Rejections under § 101:
Pages 12-15 of the Applicant Remarks consist of a copy of claim 1, and various characterizations of recent SME Guidance from the Office. But the guidance discussed in the Remarks is not clearly applied to the claim language.
Applicant asserts that “time-series modeling . . . was unable to perform an accurate prediction for event sequencing involving a network of different actors/entities, which were asynchronously performed at irregular intervals.” Rem. 16. Per Applicant, Hawkes processes are also problematic since “the multivariate Hawkes processes was . . . incapable of computationally modeling latent structure of network actors in real-world settings” accurately, and such Hawkes processes were “limited to learning a single dependence pattern for all the sequence of events.” Rem. 16. See also Spec. ¶¶4-5. Applicant also asserts that using “multiple iterations of conventional modeling at differing times and with differing sequence of events” solved the above problems, but was infeasible because it required excessive computational resources. Rem. 16. This last a problem, the only problem directly tied to the computer itself, does not appear to have been described in the original Specification.
The Remarks here confuse a shortcoming of classes of models with a specific technological problem with a given type of model. Deficient performance, alone, may indicate a technological problem. Deficient performance may be the result of a technological problem. But deficient performance is not, itself, a technological problem. This determination is not dispositive, because an inventive concept may lie in a technological improvement, without any specific technological problem.
The Remarks also assert a technological improvement. See Rem. 16-17. Specifically, the Remarks recite almost a page of language very similar to that of amended claim 1 before asserting – with no explanation whatsoever – that “features of amended claim 1 allow for determination of [a] causal relationship between the at least two events included in the particular event sequence and accurately performing a prediction based on the determined relationship for event sequencing involving a network of different actors/entities, which were asynchronously performed at irregular intervals and include multiple impact patterns, without requiring multiple modeling iterations for more efficient utilization of computing resources[.]” Rem. 17. As evidence for this assertion, the Remarks direct the reader to “the ordinary artisan [who] would reasonably understand” the techniques of claim 1 to result in this asserted benefit. See Rem. 17. An improvement to accuracy in the field of computer modeling or machine learning, or a reduction in system requirements required for utilizing a computer model may constitute an inventive concept. But no evidence of any accuracy improvement or evidence of the recently asserted decrease in computing requirements is found in the record. Citation to one of ordinary skill in the art is unconvincing. Without any evidence or any clearly articulated connection between the claimed techniques and a technological improvement, there is no sufficient basis for a determination that the claimed invention is directed to a technological improvement.
The claims appear to be directed to a combination of mental and mathematical processes carried out on a conventional computer. Claiming the implementation of a mental or mathematical process using conventional computer components is not technological. In contrast, claims directed to an improved technique of computer modelling, machine learning, or some other technological field may be directed to an inventive concept. Since the Remarks do not clearly relate specific operations in the claims to a specific improvement in any of these fields, there is a lack of evidence showing this claim to be directed to a technological improvement.
It is noted here, that patent eligible techniques which improve the accuracy of a model may include math. This is noted because the Specification includes various mathematical operations, none of which are claimed, and because claiming math tends to be avoided. If the combination of math and other specific techniques leads to an improvement in the model accuracy, the inclusion of math in the claims may actually be a way forward.
Rejections under § 103:
The Remarks assert that the combination of Xu and Fang fail to teach the claimed “adjusting the mixture of multivariate Hawkes processes by applying on online learning algorithm to the generated mixture of Hawkes processes[.]” Rem. 18-19. Creation and use of a mixture of multivariate Hawkes processes is taught in Xu, as indicated in the rejection. The rejection cites Fang as teaching online adjustment of models, and a motivation to modify Xu. As best understood, Applicant’s position is that Fang fails to teach a Hawkes process and therefore, cannot teach adjusting of a mixture of multivariate Hawkes processes. Rem. 19. This is true, but fails to address what the combination of references would suggest to one of ordinary skill. Specifically, one of ordinary skill in the art would be motivated to adjust the multivariate Hawkes processes taught in Xu, in view of Fang’s teaching of using online learning for the reason cited in the rejection above.
Applicant asserts that Xu fails to teach claim language that is new to this action and juxtaposes claim language with characterizations of the teachings of the prior art. But no specific distinction between the teaching of Xu and the claimed operations are articulated in the Remarks.
No other specific arguments are submitted.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to PAUL M KNIGHT whose telephone number is (571) 272-8646. The examiner can normally be reached Monday - Friday 9-5 ET.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Michelle Bechtold can be reached on (571. The fax phone number for the organization where this application or proceeding is assigned is (571) 273-8300.
Information regarding the status of published or unpublished applications may be obtained from Patent Center. Unpublished application information in Patent Center is available to registered users. To file and manage patent submissions in Patent Center, visit: https://patentcenter.uspto.gov. Visit https://www.uspto.gov/patents/apply/patent-center for more information about Patent Center and https://www.uspto.gov/patents/docx for information about filing in DOCX format. For additional questions, contact the Electronic Business Center (EBC) at 866-217-9197 (toll-free). If you would like assistance from a USPTO Customer Service Representative, call 800-786-9199 (IN USA OR CANADA) or 571-272-1000.
PAUL M. KNIGHT
/PAUL M KNIGHT/Examiner, Art Unit 2148
1 This distinction between claims which read on math and claims which recite an abstract idea is based on official USPTO Guidance. The 2019 Subject Matter Eligibility (SME) Examples instructs examiners that a claim reciting “training the neural network” where the background describes training as “using stochastic learning with backpropagation which is a type of machine learning algorithm that uses the gradient of a mathematical loss function to adjust the weights of the network” “does not recite any mathematical relationships, formulas, or calculations.” See 2019 SME Example 39, PP. 8-9 (emphasis added). In this example, the plain meaning of “training the neural network” read in light of the disclosure reads on backpropagation using the gradient of a mathematical loss function. See MPEP § 2111.01. In contrast, the 2024 SME Examples instructs examiners that a claim reciting “training, by the computer, the ANN . . . wherein the selected training algorithm includes a backpropagation algorithm and a gradient descent algorithm” does recite an abstract idea because “[t]he plain meaning of [backpropagation algorithm and gradient descent algorithm] are optimization algorithms, which compute neural network parameters using a series of mathematical calculations.” 2024 PEG Example 47, PP. 4-6. The Memorandum of August 4, 2025; Reminders on evaluating subject matter eligibility of claims under 35 U.S.C. 101, P. 3 also directs examiners that “training the neural network” recited in Example 39 merely “involve[s] . . . mathematical concepts” and contrasts claim 2 of example 47 as “referring to [specific] mathematical calculations by name[.]” (Emphasis added.)
2 “For instance, the claims in Diehr . . . clearly stated a mathematical equation . . . and the claims in Mayo . . . clearly stated laws of nature . . . such that the claims ‘set forth’ an identifiable judicial exception. Alternatively, the claims in Alice Corp. . . . described the concept of intermediated settlement without ever explicitly using the words ‘intermediated’ or ‘settlement.’” MPEP § 2106.04(II)(A).
3 “By grouping the abstract ideas, the examiners’ focus has been shifted from relying on individual cases to generally applying the wide body of case law spanning all technologies and claim types. . . . If the identified limitation(s) falls within at least one of the groupings of abstract ideas, it is reasonable to conclude that the claim recites an abstract idea in Step 2A Prong One.” MPEP § 2106.04(a). See also MPEP 2104(a)(2).
4 Step 2A prongs one and two are evaluated individually, consistent with the framework in the MPEP. Evaluation of relationships between abstract ideas and additional elements in one location promotes clarity of the record.
5 “In short, first the specification should be evaluated to determine if the disclosure provides sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it 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. That is, the claim includes the components or steps of the invention that provide the improvement described in the specification. . . . It should be noted that while this consideration is often referred to in an abbreviated manner as the ‘improvements consideration,’ the word ‘improvements’ in the context of this consideration is limited to improvements to the functioning of a computer or any other technology/technical field, whether in Step 2A Prong Two or in Step 2B.” MPEP 2106.04(d)(1). See also Koninklijke KPN N.V. v. Gemalto M2M GmbH, 942 F.3d 1143, 1150-1152 (Fed. Cir. 2019).
6 See MPEP § 2106.05(d)(II) listing operations including “receiving or transmitting data,” “storing and retrieving data in memory,” and “performing repetitive calculations” as WURC. “The claims at issue do not require any nonconventional computer, network, or display components, or even a non-conventional and non-generic arrangement of known, conventional pieces, but merely call for performance of the claimed information collection, analysis, and display functions on a set of generic computer components and display devices.” Elec. Power Grp., LLC v. Alstom S.A., 830 F.3d 1350, 1355 (Fed. Cir. 2016) (emphasis added, internal quotes omitted).
7 “But ‘[f]or the role of a computer in a computer-implemented invention to be deemed meaningful in the context of this analysis, it must involve more than performance of 'well-understood, routine, [and] conventional activities previously known to the industry.’ Content Extraction, 776 F.3d at 1347-48 (quoting Alice, 134 S. Ct at 2359). Here, the server simply receives data, ‘extract[s] classification information . . . from the received data,’ and ‘stor[es] the digital images . . . taking into consideration the classification information.’ See ‘295 patent, col. 10 ll. 1-17 (Claim 17). . . . These steps fall squarely within our precedent finding generic computer components insufficient to add an inventive concept to an otherwise abstract idea. Alice, 134 S. Ct. at 2360 (‘Nearly every computer will include a 'communications controller' and a 'data storage unit' capable of performing the basic calculation, storage, and transmission functions required by the method claims.’); Content Extraction, 776 F.3d at 1345, 1348 (‘storing information’ into memory, and using a computer to ‘translate the shapes on a physical page into typeface characters,’ insufficient confer patent eligibility); Mortg. Grader, 811 F.3d at 1324-25 (generic computer components such as an ‘interface,’ ‘network,’ and ‘database,’ fail to satisfy the inventive concept requirement); Intellectual Ventures I, 792 F.3d at 1368 (a ‘database’ and ‘a communication medium’ ‘are all generic computer elements’); BuySAFE v. Google, Inc., 765 F.3d 1350, 1355 (Fed. Cir. 2014) (‘That a computer receives and sends the information over a network—with no further specification—is not even arguably inventive.’).” TLI Commc'ns LLC v. AV Auto., LLC, 823 F.3d 607, 614 (Fed. Cir. 2016), Emphasis Added.
8 “The analysis as to whether an element (or combination of elements) is widely prevalent or in common use is the same as the analysis under 35 U.S.C. 112(a) as to whether an element is so well-known that it need not be described in detail in the patent specification. See Genetic Techs. Ltd. v. Merial LLC, 818 F.3d 1369, 1377, 118 USPQ2d 1541, 1546 (Fed. Cir. 2016) (supporting the position that amplification was well-understood, routine, conventional for purposes of subject matter eligibility by observing that the patentee expressly argued during prosecution of the application that amplification was a technique readily practiced by those skilled in the art to overcome the rejection of the claim under 35 U.S.C. 112, first paragraph)[.]” MPEP § 2106.05(d)(I).
9 “Similarly, claim elements or combinations of claim elements that are routine, conventional or well-understood cannot transform the claims. (Citing BSG Tech LLC v. BuySeasons, Inc., 899 F.3d 1281, 1290-1291 (Fed. Cir. 2018)). When the patent's specification ‘describes the components and features listed in the claims generically,’ it ‘support[s] the conclusion that these components and features are conventional.’ Weisner v. Google LLC, 51 F.4th 1073, 1083-84 (Fed. Cir. 2022); see also Beteiro, LLC v. DraftKings Inc., 104 F.4th 1350, 1357-58 (Fed. Cir. 2024).” Broadband iTV, Inc. v. Amazon.com, Inc., 113 F.4th 1359 (Fed. Cir. 2024)
10 “If it is asserted that the invention improves upon conventional functioning of a computer, or upon conventional technology or technological processes, a technical explanation as to how to implement the invention should be present in the specification. That is, the disclosure must provide sufficient details such that one of ordinary skill in the art would recognize the claimed invention as providing an improvement. The specification need not explicitly set forth the improvement, but it 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.” MPEP § 2106.05(a).
Prosecution Insights