DETAILED ACTION
Status of the Claims
The following is a Final Office Action in response to amendments and remarks filed 08 July 2026.
Claims 1-2, 6 and 8 have been amended.
Claims 4-5 have been cancelled.
Claims 1-3, 6, and 8 are pending and have been examined.
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 Arguments
Applicant's arguments filed 08 July 2026 have been fully considered but they are not persuasive.
Applicant argues that the double patenting rejection should be withdrawn, however the Examiner respectfully disagrees. Here, and as noted in the rejection previously, the instant claims are rejected as a nonstatutory obviousness-type, and as noted in the updated rejection below, it would have been obvious to modify the instant claims to arrive at the ‘152 patent. As such, the arguments are not persuasive, and the rejection not overcome.
Applicants argue that the 35 U.S.C. 101 rejection under the Alice Corp. vs. CLS Bank Int’l be withdrawn; however the Examiner respectfully disagrees. Applicant argues that the claims are not generic computer functions, however that is not the injury under §101. Here, the amended claims are predicting future behavior and future moods and model creation thereof which is an abstract idea of organizing human activities, and it is the claim construction which only recites additional elements such as processor at such a high level of generality, that it amounts to mere instructions to apply the abstract idea or judicial exception. The inquiry is whether or not the claims are directed towards an abstract idea, and whether or not that abstract idea is then integrated into a practical application (Step 2A prong 2) and whether or not the additional elements amount to significantly more when considered as a whole (Step 2B). Again, and as noted in updated rejection below and previous rejection, the claims are not directed to a practical application of the concept. The claims do not result in improvements to the functioning of a computer or to any other technology or technical field. They do not effect a particular treatment for a disease. They are not applied with or by a particular machine. They do not effect a transformation or reduction of a particular article to a different state or thing. And they are not applied in some other meaningful way beyond generally linking the use of the judicial exception (i.e., predicting a user’s future behavior and future mood) to a particular technological environment (i.e., with the use of generic computing components, neural networks). Here, again as noted in the previous rejection, mere instructions to apply an exception using a generic computer component cannot provide an inventive concept - MPEP 2016.05(f). The claims recitation of the ““first/second network being a deep neural network”” only generally linking the use of the judicial exception to a particular technological environment or field of use – see MPEP 2106.05(h). The claim(s) is/are not patent eligible, the arguments not persuasive, and the rejection not withdrawn.
Applicant next argues that the claims are not well-known and thus amount to an improvement; however, the Examiner respectfully disagrees. As an initial note, the arguments are not compliant under 37 CFR 1.111(b) as they amount to a mere allegation of patent eligibility based upon a bare assertion of improvement. The Examiner respectfully does not find the assertion persuasive because a bare assertion of an improvement without the detail necessary to be apparent is not sufficient to show an improvement (MPEP 2106.04(d)(1) (discussing MPEP 2106.05(a)). That is, the Examiner does not find any evidence that the claimed aspects are any improvement over conventional systems. This argument also appears to be that the claims are patent eligible due to the arguments regarding the prior art rejections; however, the Examiner asserts that subject matter eligibility and novelty/non-obviousness are two separate inquires, neither being a benchmark for the other. See Amdocs Ltd. v. Openet Telecom, Inc., 841 F.3d 1288, 1311 (Fed. Cir. 2016) (“Novelty is the question of whether the claimed invention is new. Inventiveness is the question of whether the claimed matter is invention at all, new or otherwise. The inventiveness inquiry of § 101 should therefore not be confused with the separate novelty inquiry of § 102 or the obviousness inquiry of § 103.”). As such, this argument is not persuasive, and the rejection not overcome.
The Examiner also notes that Applicant’s arguments appear to be whether or not the use of computer or computing components for increased speed and efficiency amounts to significantly more and is an improvement; however the Examiner respectfully disagrees. Nor, in addressing the second step of Alice, does claiming the improved speed or efficiency inherent with applying the abstract idea on a computer provide a sufficient inventive concept. See Bancorp Servs., LLC v. Sun Life Assurance Co. of Can., 687 F.3d 1266, 1278 (Fed. Cir. 2012) (“[T]he fact that the required calculations could be performed more efficiently via a computer does not materially alter the patent eligibility of the claimed subject matter.”); CLS Bank, Int’l v. Alice Corp., 717 F.3d 1269, 1286 (Fed. Cir. 2013) (en banc) aff’d, 134 S. Ct. 2347 (2014) (“[S]imply appending generic computer functionality to lend speed or efficiency to the performance of an otherwise abstract concept does not meaningfully limit claim scope for purposes of patent eligibility.” (citations omitted)). As such, this argument is not persuasive, and the rejection not overcome.
Applicant's arguments fail to comply with 37 CFR 1.111(b) because they amount to a general allegation that the claims define a patentable invention without specifically pointing out how the language of the claims patentably distinguishes them from the references.
Applicant's arguments do not comply with 37 CFR 1.111(c) because they do not clearly point out the patentable novelty which he or she thinks the claims present in view of the state of the art disclosed by the references cited or the objections made. Further, they do not show how the amendments avoid such references or objections.
Applicant’s remarks regarding the Zadeh reference have been fully considered but are not persuasive. Applicant appears to be arguing the intended use of the Zedah reference, however, a recitation of the intended use of the claimed invention must result in a structural (or methodical) difference between the claimed invention and the prior art in order to patentably distinguish the claimed invention from the prior art. If the prior art structure (or methodical) is capable of performing the intended use, then it meets the claim. As such, this argument is not persuasive, and the rejection not overcome.
Applicant’s remaining arguments with respect to the prior art have been fully considered and addressed in the updated rejection below, as necessitated by amendments.
In response to arguments in reference to any depending claims that have not been individually addressed, all rejections made towards these dependent claims are maintained due to a lack of reply by the Applicants in regards to distinctly and specifically pointing out the supposed errors in the Examiner's prior office action (37 CFR 1.111). The Examiner asserts that the Applicants only argue that the dependent claims should be allowable because the independent claims are unobvious and patentable over the prior art.
Double Patenting
The nonstatutory double patenting rejection is based on a judicially created doctrine grounded in public policy (a policy reflected in the statute) so as to prevent the unjustified or improper timewise extension of the “right to exclude” granted by a patent and to prevent possible harassment by multiple assignees. A nonstatutory double patenting rejection is appropriate where the conflicting claims are not identical, but at least one examined application claim is not patentably distinct from the reference claim(s) because the examined application claim is either anticipated by, or would have been obvious over, the reference claim(s). See, e.g., In re Berg, 140 F.3d 1428, 46 USPQ2d 1226 (Fed. Cir. 1998); In re Goodman, 11 F.3d 1046, 29 USPQ2d 2010 (Fed. Cir. 1993); In re Longi, 759 F.2d 887, 225 USPQ 645 (Fed. Cir. 1985); In re Van Ornum, 686 F.2d 937, 214 USPQ 761 (CCPA 1982); In re Vogel, 422 F.2d 438, 164 USPQ 619 (CCPA 1970); In re Thorington, 418 F.2d 528, 163 USPQ 644 (CCPA 1969).
A timely filed terminal disclaimer in compliance with 37 CFR 1.321(c) or 1.321(d) may be used to overcome an actual or provisional rejection based on nonstatutory double patenting provided the reference application or patent either is shown to be commonly owned with the examined application, or claims an invention made as a result of activities undertaken within the scope of a joint research agreement. See MPEP § 717.02 for applications subject to examination under the first inventor to file provisions of the AIA as explained in MPEP § 2159. See MPEP § 2146 et seq. for applications not subject to examination under the first inventor to file provisions of the AIA . A terminal disclaimer must be signed in compliance with 37 CFR 1.321(b).
The filing of a terminal disclaimer by itself is not a complete reply to a nonstatutory double patenting (NSDP) rejection. A complete reply requires that the terminal disclaimer be accompanied by a reply requesting reconsideration of the prior Office action. Even where the NSDP rejection is provisional the reply must be complete. See MPEP § 804, subsection I.B.1. For a reply to a non-final Office action, see 37 CFR 1.111(a). For a reply to final Office action, see 37 CFR 1.113(c). A request for reconsideration while not provided for in 37 CFR 1.113(c) may be filed after final for consideration. See MPEP §§ 706.07(e) and 714.13.
The USPTO Internet website contains terminal disclaimer forms which may be used. Please visit www.uspto.gov/patent/patents-forms. The actual filing date of the application in which the form is filed determines what form (e.g., PTO/SB/25, PTO/SB/26, PTO/AIA /25, or PTO/AIA /26) should be used. A web-based eTerminal Disclaimer may be filled out completely online using web-screens. An eTerminal Disclaimer that meets all requirements is auto-processed and approved immediately upon submission. For more information about eTerminal Disclaimers, refer to www.uspto.gov/patents/apply/applying-online/eterminal-disclaimer.
Claims 1-3, 6 and 8 are rejected on the ground of nonstatutory double patenting as being unpatentable over claims 1-4 and 6 of U.S. Patent No. 17/926,611 (Now US Patent No. 12,551,152, hereinafter the ‘152 patent). Although the claims at issue are not identical, they are not patentably distinct from each other because some features of the claims at issue in this instant application are broader than those in the issued patent. Here, specifically, instant claim 1 is rejected on the ground of nonstatutory obviousness-type double patenting as being unpatentable over claim 1 of the ‘152 Patent. The claims differ in that instant claims 1, 6, and 8 recites extracting and predicting future moods and behavior, claim 2 recites constructing and updating of a model for predicting future mood and behavior whereas claim 1 of the ‘152 Patent recites the training of forecasting models for future mood behavior. The portion of the specification in the ‘152 Patent that supports the recited extracting and predicting aspects includes an embodiment that would anticipate instant claims 1, 2, 6, and 8 herein. Instant claim 1, 2, 6, and 8 cannot be considered patentably distinct over claim 1 of the ‘152 Patent when there is a specifically disclosed embodiment that supports claim 1 of that patent and falls within the scope of claim 1 herein because it would have been obvious to one having ordinary skill in the art to modify the method of claim 1 by selecting a specifically disclosed embodiment that supports that claim, i.e., the extracting, predicting, constructing steps. One having ordinary skill in the art would have been motivated to do this because that embodiment is disclosed as being a preferred embodiment within claim 1. Instant independent claims 8 and 15 are rejected under the same rationale, mutatis mutandis.
Dependent claims 2, 4, and 6 of the ‘512 Patent recite substantially similar subject matter as the instant claims 3-5. See MPEP § 804(II)(B)(2). In such a situation, it is per se obvious to claim a broader invention. Therefore, the claims must be rejected under double patenting as anticipated by the issued patent.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-3, 6 and 8 is/are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more. The claims are directed to a process (an act, or series of acts or steps), a machine (a concrete thing, consisting of parts, or of certain devices and combination of devices), and a manufacture (an article produced from raw or prepared materials by giving these materials new forms, qualities, properties, or combinations, whether by hand labor or by machinery). Thus, each of the claims falls within one of the four statutory categories (Step 1). The claims recite a method (process) and apparatus, however, the claim(s) recite(s) predicting future behavior and future moods and model creation thereof which is an abstract idea of organizing human activities.
The limitations of:
In claims 1, 6, and 8 “extract a feature based on past behavior series data of a prediction target and output behavior feature data; predict a future behavior series of the prediction target based on the behavior feature data using a trained behavior series prediction model for predicting a behavior series the future behavior series of the prediction target including a first mean value and a first variance value, the trained behavior series prediction model including a first network, the first network being a deep neural network; predict a future mood series of the prediction target based on the behavior feature data and past mood series data of the prediction target using a trained mood series prediction model for predicting a mood series, the trained mood series prediction model including a second network, the first network being a deep neural network, wherein the processor is configured to, by using the first network:(i) acquire a first feature vector from the behavior feature data;(ii) perform nonlinear transformation of the first feature vector; and(iii) acquire the first mean value and the first variance value based on the transformed first feature vector, wherein the processor is configured to, by using the second network:(i) acquire a second feature vector from the behavior feature data;(ii) perform nonlinear transformation of the second feature vector;(iii) calculate a third feature vector considered an importance of the transformed second feature vector,(iv) acquire a fourth feature vector from the behavior feature data;(v) acquire a first value and a second value based on the third feature vector and the fourth feature vector, the first value indicating a degree of positivity or negativity of emotion, the second value indicating a degree of excitement of emotion, wherein the trained behavior series prediction model includes parameters, and wherein the parameters are acquired by calculating a negative log likelihood for output behavior series data using a second mean value and a second variance value obtained by the first network, the second mean value and the second variance value are calculated before predicting the future behavior series of the prediction target”
In claim 2 “extract a feature based on behavior series data of a learning target and output behavior feature data; construct a behavior series prediction model for predicting a behavior series, the constructed behavior series prediction model including a first network, the first network being a deep neural network; calculate a negative log likelihood for output behavior series data using a first mean value and a first variance value obtained by the first network; update parameters of the constructed behavior series prediction model based on the negative log likelihood; construct a mood series prediction model for predicting a mood series the constructed mood series prediction model including a second network, the second network being a deep neural network; update parameters of the constructed mood series prediction model based on the behavior feature data and past mood series data of the learning target;....by using the first network:(i) acquire a first feature vector from the behavior feature data;(ii) perform nonlinear transformation of the first feature vector; and(iii) acquire the first mean value and the first variance value based on the transformed first feature vector, and wherein the processor is configured to, by using the second network:(i) acquire a second feature vector from the behavior feature data;(ii) perform nonlinear transformation of the second feature vector;(iii) calculate a third feature vector considered an importance of the transformed second feature vector,(iv) acquire a fourth feature vector from the behavior feature data;(v) acquire a first value and a second value based on the third feature vector and the fourth feature vector, the first value indicating a degree of positivity or negativity of emotion, the second value indicating a degree of excitement of emotion”
...as drafted, is a process that, under its broadest reasonable interpretation, covers organizing human activities--fundamental economic principles or practices (including hedging, insurance, mitigating risk); commercial or legal interactions (including agreements in the form of contracts; legal obligations; advertising, marketing or sales activities or behaviors; business relations); managing personal behavior or relationships or interactions between people (including social activities, teaching, and following rules or instructions) but for the recitation of generic computer components (Step 2A Prong 1). That is, other than reciting “a prediction apparatus comprising: a memory: and a processor coupled to the memory and configured to;,” (or “a learning apparatus comprising: a memory: and a processor coupled to the memory and configured to;” in claim 2 or “A prediction method which is executed by a prediction apparatus” in claim 6 or “A non-transitory computer-readable recording medium storing a program for causing a computer to perform” in claim 8) nothing in the claim element precludes the step from the methods of organizing human interactions grouping. For example, but for the “a prediction apparatus comprising: a memory: and a processor coupled to the memory and configured to;,” (or “a learning apparatus comprising: a memory: and a processor coupled to the memory and configured to;” in claim 2 or “A prediction method which is executed by a prediction apparatus” in claim 6 or “A non-transitory computer-readable recording medium storing a program for causing a computer to perform” in claim 8) language, “extract/extracting, “predict/predicting,” “predict/predicting,” ”acquire” “perform,” “calculate” “extract,” “construct,” and “update,” in the context of this claim encompasses the user predicting other user’s behaviors and moods which is business relation/fundamental economic practice/commercial or legal interaction/managing personal behavior with the use of some mathematical concepts (vectors, values). If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation as one of the methods of organizing human activities, while some of the limitations may be based on mathematical concepts, but for the recitation of generic computer components, then it falls within the “Certain Methods of Organizing Human Activities” grouping of abstract ideas. Accordingly, the claim(s) recite(s) an abstract idea (Step 2A, Prong One: YES).
This judicial exception is not integrated into a practical application (Step 2A Prong Two). In particular, the claim only recites one additional element – using a processor or apparatus to perform the steps. The processor or apparatus in the steps is recited at a high-level of generality such that it amounts no more than mere instructions to apply the exception using a generic computer component. Specifically the claims amount to nothing more than an instruction to apply the abstract idea using a generic computer or invoking computers as tools by adding the words “apply it” (or an equivalent) with the judicial exception, or mere instructions to implement an abstract idea on a computer, or merely uses a computer as a tool to perform an abstract idea - see MPEP 2106.04(d)(I) discussing MPEP 2106.05(f). The recitation of “first/second network being a deep neural network” in the limitations also merely indicates a field of use or technological environment in which the judicial exception is performed. Although the additional element “deep neural network” limits the identified judicial exceptions, this type of limitation merely confines the use of the abstract idea to a particular technological environment (neural networks) and thus fails to add an inventive concept to the claims. See MPEP 2106.05(h). Accordingly, the combination of these additional elements does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. The claim is directed to an abstract idea, even when considered as a whole (Step 2A Prong Two: NO).
The claim does not include a combination of additional elements that are sufficient to amount to significantly more than the judicial exception (Step 2B). As discussed above with respect to integration of the abstract idea into a practical application (Step 2A Prong 2), the combination of additional elements of using a processor or apparatus to perform the steps amounts to no more than mere instructions to apply the exception using a generic computer component and merely limiting the technical environment. Mere instructions to apply an exception using a generic computer component and merely limiting the technical environment cannot provide an inventive concept. Therefore, when considering the additional elements alone, and in combination, there is no inventive concept in the claim. As such, the claim(s) is/are not patent eligible, even when considered as a whole (Step 2B: NO).
Claim 3 recite(s) the additional limitation(s) further introducing mathematical concepts which is not an inventive concept that meaningfully limits the abstract idea. Again, as discussed with respect to claims 1, 2, 6, and 8, the claims are simply limitations which are no more than mere instructions to apply the exception using a computer or with computing components. Accordingly, the additional element(s) does not integrate the abstract idea into a practical application because it does not impose any meaningful limits on practicing the abstract idea. Even when considered as a whole, the claims do not integrate the judicial exception into a practical application at Step 2A or provide an inventive concept in Step 2B.
Claims 1-3, 6 and 8 are therefore not eligible subject matter, even when considered as a whole.
Claim Rejections - 35 USC § 103
In the event the determination of the status of the application as subject to AIA 35 U.S.C. 102 and 103 (or as subject to pre-AIA 35 U.S.C. 102 and 103) is incorrect, any correction of the statutory basis (i.e., changing from AIA to pre-AIA ) for the rejection will not be considered a new ground of rejection if the prior art relied upon, and the rationale supporting the rejection, would be the same under either status.
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claim(s) 1-3, 6 and 8 is/are rejected under 35 U.S.C. 103 as being unpatentable over Wildberger (US PG Pub. 2018/0047065) and further in view of Zadeh et al. (US PG Pub. 2014/0201126).
As per claims 1, 6, and 8, Wildberger discloses a prediction apparatus comprising: a memory: and a processor coupled to the memory and configured to; and method and a non-transitory computer-readable recording medium storing a program for causing a computer to perform (processor, memory, Wildberger ¶97-¶98 and Fig. 3; computer readable media, ¶2; computer implemented systems, ¶4):
extract a feature based on past behavior series data of a prediction target and output behavior feature data (client history, Wildberger ¶28, ¶30, ¶50; expected behavior, desires, mood, ¶50; wearable information, data from wearable device sensed, ¶52, ¶57, and ¶59; digital footprint, tracking user’s digital traces acquired over time, ¶48; history/schedule, ¶75);
predict a future behavior series of the prediction target based on the behavior feature data using a trained behavior series prediction model for predicting a behavior series the future behavior series of the prediction target including a first mean value and a first variance value, the trained behavior series prediction model including a first network, the first network being a deep neural network (predictions may be generated through the use of weighted comparisons to identify differences or similarities between historical patterns for the individual, identified population-level patterns (e.g., for the general population as a whole or a selected demographic segment), among others. Quantified metrics may be utilized to generate predictions, including quantified metrics associated with at least one of: the client's expected behavior, the client's expected desires, and the client's expected moods. Machine-learning, trained neural networks, and hidden Markov models, among others, can be utilized to identify relationships and weightings thereof based on a sufficiently large or trained data set, Wildberger ¶74; The contextual factors are prone to change, and accordingly, in some embodiments, the contextual factors are periodically or continuously monitored such that the system and its predictions are able to be responsive to just-in-time changes. In some embodiments, the platform includes a data encoding processor configured to extract, data representations (e.g., vectors, variables, scores) based on the client's expected behavior (e.g., going to the gym), desires (e.g., desires coffee in the morning), or moods (e.g., in a rush, angry), and these representations may be stored (e.g., appended) onto the digital profile with a corresponding timestamp, ¶50) (Examiner interprets the ability to use neural networks as also including the use of deep neural networks);
predict a future mood series of the prediction target based on the behavior feature data and past mood series data of the prediction target using a trained mood series prediction model for predicting a mood series (predictions may be generated through the use of weighted comparisons to identify differences or similarities between historical patterns for the individual, identified population-level patterns (e.g., for the general population as a whole or a selected demographic segment), among others. Quantified metrics may be utilized to generate predictions, including quantified metrics associated with at least one of: the client's expected behavior, the client's expected desires, and the client's expected moods. Machine-learning, trained neural networks, and hidden Markov models, among others, can be utilized to identify relationships and weightings thereof based on a sufficiently large or trained data set, Wildberger ¶74), the trained mood series prediction model including a second network, the first network being a deep neural network (using trained neural networks, Wildberger ¶74),
wherein the processor is configured to, by using the first network (using trained neural networks, Wildberger ¶74):
acquire a first feature vector from the behavior feature data (multi-dimensional vectors, Wildberger ¶46);
perform nonlinear transformation of the first feature vector (identify non-linear relationships by appending weighted linkages between different data elements, Wildberger ¶52-¶53); and
acquire the first mean value and the first variance value based on the transformed first feature vector (For example, a prediction engine may be configured to identify correlations, co-variances, linear relationships, non-linear relationships, lagged relationships (e.g., cyclical factors), that are indicative of potential sources of causation (e.g., if a meeting is booked in Mississauga, the client needs transportation) or inference (e.g., there is a 90% chance of a coffee purchase in the morning). These relationships can be established by appending weighted linkages between different data elements, or comparing identified relationships against baselines defined by processing aggregate data from other data profiles and deviations therefrom (e.g., a consistent difference from a mean may be indicative of a trait of the user). A potential data structure includes representations of adjacency matrices, and weighted edge lists, whereby the adjacency matrices, and weighted edge lists are continuously updated to represent newly identified relationships, Wildberger ¶52-¶53),
wherein the processor is configured to, by using the second network (using trained neural networks, Wildberger ¶74):
acquire a second feature vector from the behavior feature data (multi-dimensional vectors, Wildberger ¶46);
perform nonlinear transformation of the second feature vector (identify non-linear relationships by appending weighted linkages between different data elements, Wildberger ¶52-¶53);
calculate a third feature vector considered an importance of the transformed second feature vector (In some embodiments, the platform includes a data encoding processor configured to extract, data representations (e.g., vectors, variables, scores) based on the client's expected behavior (e.g., going to the gym), desires (e.g., desires coffee in the morning), or moods (e.g., in a rush, angry), and these representations may be stored (e.g., appended) onto the digital profile with a corresponding timestamp, Wildberger ¶50),
acquire a fourth feature vector from the behavior feature data (multi-dimensional vectors, Wildberger ¶46);
acquire a first value and a second value based on the third feature vector and the fourth feature vector, the first value indicating a degree of positivity or negativity of emotion, the second value indicating a degree of excitement of emotion (data representations (e.g., vectors, variables, scores) based on the client's expected behavior (e.g., going to the gym), desires (e.g., desires coffee in the morning), or moods (e.g., in a rush, angry), and these representations may be stored (e.g., appended) onto the digital profile with a corresponding timestamp, Wildberger ¶50) (Examiner notes the scores with respect to the desires and moods as the first and second values with degree of positivity or negativity and excitement of emotion, as discussed in the interview 10 June 2026),
wherein the trained behavior series prediction model includes parameters (historical patterns, trained data set, Wildberger ¶74), and
Wildberger does not expressly disclose wherein the parameters are acquired by calculating a negative log likelihood for output behavior series data using a second mean value and a second variance value obtained by the first network, the second mean value and the second variance value are calculated before predicting the future behavior series of the prediction target.
However, Zadeh teaches wherein the parameters are acquired by calculating a negative log likelihood for output behavior series data using a second mean value and a second variance value obtained by the first network, the second mean value and the second variance value are calculated before predicting the future behavior series of the prediction target. (Boltzmann machine, negative log probability for the state, Zadeh ¶1723).
Both the Wildberger and Zadeh references are analogous in that both are directed towards/concerned with analytics and prediction modeling. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Zadeh’s fuzzy logic techniques in Wildberger’s system to improve the system and method with reasonable expectation that this would result in a more accurate behavior and mood prediction modelling system.
The motivation being that there is a need for improving performance or efficiency in future models (Zadeh ¶2216-¶2217).
As per claim 2, Wildberger discloses a learning apparatus comprising: a memory: and a processor coupled to the memory and configured to (processor, memory, Wildberger ¶97-¶98 and Fig. 3):
extract a feature based on behavior series data of a learning target and output behavior feature data (client history, Wildberger ¶28, ¶30, ¶50; expected behavior, desires, mood, ¶50; wearable information, data from wearable device sensed, ¶52, ¶57, and ¶59; digital footprint, tracking user’s digital traces acquired over time, ¶48; history/schedule, ¶75);
construct a behavior series prediction model for predicting a behavior series, the constructed behavior series prediction model including a first network, the first network being a deep neural network (For example, predictions may be generated through the use of weighted comparisons to identify differences or similarities between historical patterns for the individual, identified population-level patterns (e.g., for the general population as a whole or a selected demographic segment), among others. Quantified metrics may be utilized to generate predictions, including quantified metrics associated with at least one of: the client's expected behavior, the client's expected desires, and the client's expected moods. Machine-learning, trained neural networks, and hidden Markov models, among others, can be utilized to identify relationships and weightings thereof based on a sufficiently large or trained data set, Wildberger ¶74) (Examiner interprets the ability to use neural networks as also including the use of deep neural networks);
construct a mood series prediction model for predicting a mood series the constructed mood series prediction model including a second network, the second network being a deep neural network (The contextual factors are prone to change, and accordingly, in some embodiments, the contextual factors are periodically or continuously monitored such that the system and its predictions are able to be responsive to just-in-time changes. In some embodiments, the platform includes a data encoding processor configured to extract, data representations (e.g., vectors, variables, scores) based on the client's expected behavior (e.g., going to the gym), desires (e.g., desires coffee in the morning), or moods (e.g., in a rush, angry), and these representations may be stored (e.g., appended) onto the digital profile with a corresponding timestamp, Wildberger ¶50; using neural networks, ¶74): and
update parameters of the constructed mood series prediction model based on the behavior feature data and past mood series data of the learning target (predictions may be generated through the use of weighted comparisons to identify differences or similarities between historical patterns for the individual, identified population-level patterns (e.g., for the general population as a whole or a selected demographic segment), among others. Quantified metrics may be utilized to generate predictions, including quantified metrics associated with at least one of: the client's expected behavior, the client's expected desires, and the client's expected moods. Machine-learning, trained neural networks, and hidden Markov models, among others, can be utilized to identify relationships and weightings thereof based on a sufficiently large or trained data set, Wildberger ¶74; digital profile continuously updated, ¶4-¶6);
wherein the processor is configured to, by using the first network (using trained neural networks, Wildberger ¶74):
acquire a first feature vector from the behavior feature data (multi-dimensional vectors, Wildberger ¶46);
perform nonlinear transformation of the first feature vector (identify non-linear relationships by appending weighted linkages between different data elements, Wildberger ¶52-¶53); and
acquire the first mean value and the first variance value based on the transformed first feature vector (For example, a prediction engine may be configured to identify correlations, co-variances, linear relationships, non-linear relationships, lagged relationships (e.g., cyclical factors), that are indicative of potential sources of causation (e.g., if a meeting is booked in Mississauga, the client needs transportation) or inference (e.g., there is a 90% chance of a coffee purchase in the morning). These relationships can be established by appending weighted linkages between different data elements, or comparing identified relationships against baselines defined by processing aggregate data from other data profiles and deviations therefrom (e.g., a consistent difference from a mean may be indicative of a trait of the user). A potential data structure includes representations of adjacency matrices, and weighted edge lists, whereby the adjacency matrices, and weighted edge lists are continuously updated to represent newly identified relationships, Wildberger ¶52-¶53), and
wherein the processor is configured to, by using the second network (using trained neural networks, Wildberger ¶74):
acquire a second feature vector from the behavior feature data (multi-dimensional vectors, Wildberger ¶46);
perform nonlinear transformation of the second feature vector (identify non-linear relationships by appending weighted linkages between different data elements, Wildberger ¶52-¶53);
calculate a third feature vector considered an importance of the transformed second feature vector (In some embodiments, the platform includes a data encoding processor configured to extract, data representations (e.g., vectors, variables, scores) based on the client's expected behavior (e.g., going to the gym), desires (e.g., desires coffee in the morning), or moods (e.g., in a rush, angry), and these representations may be stored (e.g., appended) onto the digital profile with a corresponding timestamp, Wildberger ¶50),
acquire a fourth feature vector from the behavior feature data (multi-dimensional vectors, Wildberger ¶46);
acquire a first value and a second value based on the third feature vector and the fourth feature vector, the first value indicating a degree of positivity or negativity of emotion, the second value indicating a degree of excitement of emotion (data representations (e.g., vectors, variables, scores) based on the client's expected behavior (e.g., going to the gym), desires (e.g., desires coffee in the morning), or moods (e.g., in a rush, angry), and these representations may be stored (e.g., appended) onto the digital profile with a corresponding timestamp, Wildberger ¶50) (Examiner notes the scores with respect to the desires and moods as the first and second values with degree of positivity or negativity and excitement of emotion, as discussed in the interview 10 June 2026)
Wildberger does not expressly disclose calculate a negative log likelihood for output behavior series data using a first mean value and a first variance value obtained by the first network; update parameters of the constructed behavior series prediction model based on the negative log likelihood.
However, Zadeh teaches calculate a negative log likelihood for output behavior series data using a first mean value and a first variance value obtained by the first network; update parameters of the constructed behavior series prediction model based on the negative log likelihood. (Boltzmann machine, negative log probability for the state, Zadeh ¶1723).
Both the Wildberger and Zadeh references are analogous in that both are directed towards/concerned with analytics and prediction modeling. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Zadeh’s fuzzy logic techniques in Wildberger’s system to improve the system and method with reasonable expectation that this would result in a more accurate behavior and mood prediction modelling system.
The motivation being that there is a need for improving performance or efficiency in future models (Zadeh ¶2216-¶2217).
As per claim 3, Wildberger and Zadeh disclose as shown above with respect to claim 2. Zadeh further teaches the processor is configured to update the parameters based on behavior feature data obtained by adding noise to future behavior feature data extracted as data for learning (noise introduced as training, Zadeh ¶1737).
Both the Wildberger and Zadeh references are analogous in that both are directed towards/concerned with analytics and prediction modeling. Before the effective filing date of the claimed invention, it would have been obvious to a person of ordinary skill in the art to use Zadeh’s fuzzy logic techniques in Wildberger’s system to improve the system and method with reasonable expectation that this would result in a more accurate behavior and mood prediction modelling system.
The motivation being that there is a need for improving performance or efficiency in future models (Zadeh ¶2216-¶2217).
Conclusion
THIS ACTION IS MADE FINAL. Applicant is reminded of the extension of time policy as set forth in 37 CFR 1.136(a).
A shortened statutory period for reply to this final action is set to expire THREE MONTHS from the mailing date of this action. In the event a first reply is filed within TWO MONTHS of the mailing date of this final action and the advisory action is not mailed until after the end of the THREE-MONTH shortened statutory period, then the shortened statutory period will expire on the date the advisory action is mailed, and any nonprovisional extension fee (37 CFR 1.17(a)) pursuant to 37 CFR 1.136(a) will be calculated from the mailing date of the advisory action. In no event, however, will the statutory period for reply expire later than SIX MONTHS from the mailing date of this final action.
Any inquiry concerning this communication or earlier communications from the Examiner should be directed to ANDREW B WHITAKER whose telephone number is (571)270-7563. The examiner can normally be reached on M-F, 8am-5pm, EST.
If attempts to reach the examiner by telephone are unsuccessful, the Examiner’s supervisor, Lynda Jasmin can be reached on (571) 272-6782. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/ANDREW B WHITAKER/Primary Examiner, Art Unit 3629
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