DETAILED ACTION
1. The present application is being examined under the pre-AIA first to invent provisions.
Priority
2. Receipt is acknowledged of certified copies of papers required by 37 CFR 1.55.
Information Disclosure Statement
3. The information disclosure statements (IDS) submitted on 03/04/2024 and 06/03/2024 have been received, entered into the record, and considered. The submission is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statements are being considered by the examiner.
Response to Amendment
4. Receipt of Applicant’s Preliminary Amendment filed on 03/04/2024 is acknowledged. The preliminary amendment includes the amending of claims 3-5 & 7-8.
Claim Rejections - 35 USC § 101
5. 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.
6. Claims (1-9) are rejected under 35 U.S.C. 101 because the claimed invention is directed to non-statutory subject matter because the claimed invention is directed to a judicial exception (i.e., a law of nature, a natural phenomenon, or an abstract idea) without significantly more.
Under the 2019 PEG, when considering subject matter eligibility under 35 U.S.C. § 101, it must be determined whether the claim is directed to one of the four statutory categories of invention, i.e., process, machine, manufacture, or composition of matter (step 1). If the claim does fall within one of the statutory categories, it must then be determined whether the claim is directed to a judicial exception (i.e., law of nature, natural phenomenon, and abstract idea) (step 2A prong 1), and if so, it must additionally be determined whether the claim is integrated into a practical application (step 2A prong 2). If an abstract idea is present in the claim without integration into a practical application, any element or combination of elements in the claim must be sufficient to ensure that the claim amounts to significantly more than the abstract idea itself (step 2B).
In the instant case, claims (1-9) are directed to an information processing device. Thus, each of the claims falls within one of the four statutory categories. However, the claims also fall within the judicial exception of an abstract idea.
Under Step 2A Prong 1, the test is to identify whether the claims are “directed to” a judicial exception. The examiner notes that the claimed invention is directed to an abstract idea in that the instant application is directed to mental processes, specifically deriving nudge data.
The examiner further notes that claims (1-9) recite an information processing device for deriving nudge data which is similar to themes defined above of method of mental processes such as deriving nudge data, and is similar to the abstract idea identified in the 2019 PEG in grouping “c” in that the claims recite certain methods of mental processes such as deriving nudge data. The limitations, substantially comprising the body of the claim, recite a process of deriving nudge data. The examiner notes that the claimed invention derives nudge data. Because the limitations above closely follow the steps in deriving nudge data, and the steps of the claims involve mental processes, the claim recites an abstract idea consistent with the “mental processes” grouping set forth in the 2019 PEG.
Claim 1:
An information processing device comprising: a storage unit configured to store information of a nudge which is a mechanism for prompting a user to voluntarily adopt a desirable behavior;
an acquisition unit configured to acquire nudge intervention information which is information associated with intervention of a nudge; and
an abrasion deriving unit configured to derive nudge abrasion information indicating a degree of decrease in effect of the nudge associated with the nudge intervention information based on the nudge intervention information and to output the derived nudge abrasion information.
These limitations, as drafted, is an apparatus that, under its broadest reasonable interpretation, covers the performance of mental processes specifically deriving nudge data. Deriving nudge data has long before the modern computer was invented, and continues to be predominantly a product of human endeavor. The instant application is directed to deriving nudge data. Moreover, the deriving of nudge abrasion information can be performed by a human via their mind and/or pen & paper. Because the limitations above closely follow the steps of deriving nudge data, and the steps involved human judgments, observations and evaluations that can be practically or reasonably performed in the human mind and/or pen & paper, the claim recites an abstract idea consistent with the “mental process” grouping set forth in the 2019 PEG.
The mere nominal recitation of generic computing components such as an “information processing device”, “storage unit”, “acquisition unit”, and “abrasion deriving unit” do not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea.
If the claims are directed toward the judicial exception of an abstract idea, it must then be determined under Step 2A Prong 2 whether the judicial exception is integrated into a practical application. The Examiner notes that considerations under Step 2A Prong 2 comprise most the consideration previously evaluated in the context of Step 2B. The Examiner submits that the considerations discussed previously determined that the claim does not recite “significantly more” at Step 2B would be evaluated the same under Step 2A Prong 1 and result in the determination that the claim does not integrate the abstract idea into a practical application.
The instant application fails to integrate the judicial exception into a practical application because the instant application merely recites words “apply it” (or an equivalent) with the judicial exception or merely includes instructions to implement an abstract idea. The instant application is directed to an apparatus instructing the reader to implement the identified apparatus of mental processes of deriving nudge data. The elements of the claim do not themselves amount to an improvement to the computer, to a technology or another technical field. Moreover, the storage of nudge information is a data storage operation that is an insignificant data storage operation that does not integrate the abstract idea into a practical application. Furthermore, the acquiring of nudge intervention information is a data gathering operation that is an insignificant data gathering operation that does not integrate the abstract idea into a practical application. Additionally, the output of nudge abrasion information is a data output operation that is an insignificant data output operation that does not integrate the abstract idea into a practical application.
Here, the claim elements entirely comprise the abstract idea, leaving little if any aspects of the claim for further consideration under Step 2A Prong 2. In short, the claims have failed to integrate a practical application (see at least 84 Fed. Reg. (4) at 55). Under the 2019 PEG, this supports the conclusion that the claim is directed to an abstract idea, and the analysis proceeds to Step 2B.
While many considerations in Step 2A need not be reevaluated in Step 2B because the outcome will be the same. Here, on the basis of the additional elements other than the abstract idea, considered individually and in combination as discussed above, the Examiner respectfully submits that the claim 1 does not contain any additional elements that individually or as an ordered combination amount to an inventive concept and the claims are ineligible.
With respect to the dependent claims do not recite anything that is found to render the abstract idea as being transformed into a patent eligible invention. The dependent claims are merely reciting further embellishments of the abstract idea and do not claim anything that amounts to significantly more than the abstract idea itself.
With respect to the dependent claims, they have been considered and are not found to be reciting anything that amounts to being significantly more than the abstract idea. Claims 2-9 are directed to further embellishments of the central theme of the abstract idea in that the claims are directed to further embellishments of the deriving nudge data of the steps of claim 1 and do not amount to significantly more.
Specifically, claim 2 recites the deriving of nudge intervention information which can be performed by the human mind and/or pen & paper and does not amount to significantly more.
Furthermore, claim 3 recites the deriving of defined nudge abrasion information which can be performed by the human mind and/or pen & paper and does not amount to significantly more. Furthermore, the acquiring of user information is a data gathering operation that is an insignificant data gathering operation that does not integrate the abstract idea into a practical application.
Additionally, claim 4 recites the deriving of defined nudge abrasion information which can be performed by the human mind and/or pen & paper and does not amount to significantly more. Furthermore, the acquiring of environment information is a data gathering operation that is an insignificant data gathering operation that does not integrate the abstract idea into a practical application.
Moreover, claim 5 recites the updating of nudge information which can be performed by the human mind and/or pen & paper and does not amount to significantly more. Moreover, the mere nominal recitation of generic computing components such as an “update unit” does not take the claim out of certain methods of mental processes grouping. Therefore, the limitation is directed to an abstract idea. Moreover, the storage of updated nudge information is a data storage operation that is an insignificant data storage operation that does not integrate the abstract idea into a practical application.
Furthermore, claim 6 recites the deriving of defined nudge abrasion information and the subsequent updating of nudges which can be performed by the human mind and/or pen & paper and does not amount to significantly more. Moreover, the storage of nudge types is a data storage operation that is an insignificant data storage operation that does not integrate the abstract idea into a practical application.
Additionally, claim 7 recites the deriving of defined nudge recovery information which can be performed by the human mind and/or pen & paper and does not amount to significantly more. Additionally, the mere nominal recitation of generic computing components such as a “recovery deriving unit” does not take the claim out of certain methods of mental processes grouping.
Moreover, claim 8 recites the estimating of a defined probability, selecting a nudge recommendation, and generation of a defined abrasion feature quantity which can be performed by the human mind and/or pen & paper and does not amount to significantly more. Moreover, the mere nominal recitation of generic computing components such as a “behavior modification estimating unit”, “nudge selecting unit”, and “abrasion feature generating unit” do not take the claim out of certain methods of mental processes grouping
Furthermore, claim 9 recites the generation of a defined model and estimation of a defined probability which can be performed by the human mind and/or pen & paper and does not amount to significantly more.
Claim Rejections - 35 USC § 112
7. 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.
8. 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.
9. Claim 8 recites the limitation "wherein the storage unit stores information of " in Page 08. There is insufficient antecedent basis for this limitation in the claim as the plurality of types of nudges no “plurality of types of nudges” is claimed earlier in the claim or in parent independent claim 1.
Dependent claim 9 is rejected for incorporating the deficiencies of dependent claim 8.
Claim Rejections - 35 USC § 102
10. 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.
11. The following is a quotation of the appropriate paragraphs of 35 U.S.C. 102 that form the basis for the rejections under this section made in this Office action:
A person shall be entitled to a patent unless –
(a)(1) the claimed invention was patented, described in a printed publication, or in public use, on sale, or otherwise available to the public before the effective filing date of the claimed invention.
12. Claims 1-3, 5, and 7 are rejected under 35 U.S.C. 102(a)(1) as being anticipated by Dalecke et al. (Article entitled “Designing Dynamic and Personalized Nudges”, dated 03 July 2020).
13. Regarding claim 1, Dalecke teaches an information processing device comprising:
A) a storage unit configured to store information of a nudge which is a mechanism for prompting a user to voluntarily adopt a desirable behavior (Page 142, Section 4, Page 142, Section 4.1, Page 145, Section 4.3.5, Page 146, Section 4.3.5);
B) an acquisition unit configured to acquire nudge intervention information which is information associated with intervention of a nudge (Page 145, Section 4.3.5, Page 146, Section 4.3.5); and
C) an abrasion deriving unit configured to derive nudge abrasion information indicating a degree of decrease in effect of the nudge associated with the nudge intervention information based on the nudge intervention information and to output the derived nudge abrasion information (Page 145, Section 4.3.5, Page 146, Section 4.3.5).
The examiner notes that Dalecke teaches “a storage unit configured to store information of a nudge which is a mechanism for prompting a user to voluntarily adopt a desirable behavior” as “Designing a digital nudge is about selecting a target activity and presenting practical and motivational information so that the activity suggested by the nudge can be considered favorable by the user” (Page 142, Section 4), “A nudge can include different types of data, such as text, images (e.g., of a point of interest, a scenery or road/path conditions), position or directions on a map, link to online information, video and audio. Some components (e.g., text) can be predefined and selected based on relevance, while others (e.g., directions on a map) must be created when the nudge is to be presented. Predefined text can often be combined with current data, for example, “Using the car instead of will cost you ", “Use to get to " or “This takes you to "” (Page 142, Section 4.1), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed” (Page 145, Section 4.3.5), and “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5). The examiner further notes that the storage of every nudge teaches the claimed storage of information of a nudge. The examiner further notes that Dalecke teaches “an acquisition unit configured to acquire nudge intervention information which is information associated with intervention of a nudge” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed” (Page 145, Section 4.3.5) and “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5). The examiner further notes that the storage of whether a nudge has been followed (i.e. the undefined claimed nudge intervention information in the broadest reasonable interpretation) entails acquiring such intervention information in the first place. The examiner further notes that Dalecke teaches “an abrasion deriving unit configured to derive nudge abrasion information indicating a degree of decrease in effect of the nudge associated with the nudge intervention information based on the nudge intervention information and to output the derived nudge abrasion information” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5) and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that the calculation of an acceptvalue (i.e. the claimed nudge abrasion information in the broadest reasonable interpretation) is based off of whether a user followed a nudge (i.e. the claimed nudge intervention information) and is indicative of a degree of decrease in the effect of that nudge. Such a calculated acceptvalue is “output” for the subsequent calculation of the weight information.
Regarding claim 2, Dalecke further teaches an information processing device comprising:
A) wherein the nudge intervention information includes at least information indicating whether the nudge has been viewed by the user in a predetermined period and information indicating whether the user has adopted a behavior promoted by the nudge (Page 144, Section 4.3, Page 145, Section 4.3.5, Page 146, Section 4.3.5).
The examiner notes that Dalecke teaches “wherein the nudge intervention information includes at least information indicating whether the nudge has been viewed by the user in a predetermined period and information indicating whether the user has adopted a behavior promoted by the nudge” as “The user’s previous reactions to nudges also give information on what activity is best for the specific nudge. If the user has ignored nudges for walking a number of times, it may be better to nudge for something different, e.g., using the bus” (Page 144, Section 4.3), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed” (Page 145, Section 4.3.5), “Acceptance values and weights must be related to a time period. The system can calculate the values over the whole period the user has been nudged, but also for shorter periods” (Page 146, Section 4.3.5), and “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5). The examiner further notes that nudge intervention information (such as accepting, rejecting, and/or ignoring) can be tracked for specific time periods (i.e. the claimed predetermined period). Moreover, the storage of user acceptance/rejection/ignoring of a nudge teaches the claimed information indicating whether the user has adopted a behavior promoted by the nudge.
Regarding claim 3, Dalecke further teaches an information processing device comprising:
A) wherein the acquisition unit additionally acquires user information which is associated with attributes of the user and which affects the decrease in effect of the nudge (Page 143, Section 4.2, Page 145, Section 4.3.5, Page 146, Section 4.3.5, Table 4); and
B) wherein the abrasion deriving unit derives the nudge abrasion information in additional consideration of the user information (Page 143, Section 4.2, Page 145, Section 4.3.5, Page 146, Section 4.3.5, Table 4).
The examiner notes that Dalecke teaches “wherein the acquisition unit additionally acquires user information which is associated with attributes of the user and which affects the decrease in effect of the nudge” as “Generating personalized nudges for each user requires a user profile that identifies interests, behavior, and other characteristics of the user. If the user has been nudged for a period, it may be possible to determine which nudges work best for this particular user. This knowledge is based on the nudging history, reflected in the user profile, and is used to design a nudge tailored to the specific user. Tailoring of nudges also heavily relies on knowledge of the user (such as capabilities and opportunities), and on analysis of the user situation. User profile information can be collected explicitly, through direct user participation, or implicitly, through automatic monitoring of user activities [8, 9]. Explicit information gathering means that the users themselves provide information through, for example specification of interests, and positive or negative responses to nudges. Implicit information is obtained by continuously monitoring user activity, behavior and reactions to nudges. Table 4 presents some of the information that can be included in a user profile sup porting personalized nudging… When the user is new to a nudging system, historical nudging information has not been collected, and the demographic filtering is important for designing a nudge. When nudging history is available, demographic filtering will be less important, and nudge design may mainly be based on the user’s nudging history, tracked activity and preferences” (Page 143, Section 4.2), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5), and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that a user profile (See example depicted in Table 4) teaches the claimed user information that is associated with attributes of the user. Moreover, the calculation of an acceptvalue (i.e. the claimed nudge abrasion information) is based off of whether a user accepted/rejected a nudge (i.e. nudging history (which is stored in the user profile)) and is indicative of a degree of decrease in the effect of that nudge. The examiner further notes that Dalecke teaches “wherein the abrasion deriving unit derives the nudge abrasion information in additional consideration of the user information” as “Generating personalized nudges for each user requires a user profile that identifies interests, behavior, and other characteristics of the user. If the user has been nudged for a period, it may be possible to determine which nudges work best for this particular user. This knowledge is based on the nudging history, reflected in the user profile, and is used to design a nudge tailored to the specific user. Tailoring of nudges also heavily relies on knowledge of the user (such as capabilities and opportunities), and on analysis of the user situation. User profile information can be collected explicitly, through direct user participation, or implicitly, through automatic monitoring of user activities [8, 9]. Explicit information gathering means that the users themselves provide information through, for example specification of interests, and positive or negative responses to nudges. Implicit information is obtained by continuously monitoring user activity, behavior and reactions to nudges. Table 4 presents some of the information that can be included in a user profile sup porting personalized nudging… When the user is new to a nudging system, historical nudging information has not been collected, and the demographic filtering is important for designing a nudge. When nudging history is available, demographic filtering will be less important, and nudge design may mainly be based on the user’s nudging history, tracked activity and preferences” (Page 143, Section 4.2), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5), and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that a user profile (See example depicted in Table 4) teaches the claimed user information that is associated with attributes of the user. Moreover, the calculation of an acceptvalue (i.e. the claimed nudge abrasion information) is based off of whether a user accepted/rejected a nudge (i.e. nudging history (which is stored in the user profile)) and is indicative of a degree of decrease in the effect of that nudge.
Regarding claim 5, Dalecke further teaches an information processing device comprising:
A) an update unit configured to update information of the nudge associated with the nudge intervention information and stored in the storage unit based on the nudge abrasion information (Page 145, Section 4.3.5, Page 146, Section 4.3.5).
The examiner notes that Dalecke teaches “an update unit configured to update information of the nudge associated with the nudge intervention information and stored in the storage unit based on the nudge abrasion information” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed” (Page 145, Section 4.3.5), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5), “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5), and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that the nudge information is “updated” based on the calculated nudge abrasion information for future nudge suggestions.
Regarding claim 7, Dalecke further teaches an information processing device comprising:
A) a recovery deriving unit configured to derive nudge recovery information indicating a degree of recovery in effect of the nudge associated with the nudge intervention information based on the nudge intervention information (Page 145, Section 4.3.5);
B) wherein the update unit updates information of the nudge associated with the nudge intervention information in additional consideration of the nudge recovery information (Page 145, Section 4.3.5, Page 146, Section 4.3.5).
The examiner notes that Dalecke teaches “a recovery deriving unit configured to derive nudge recovery information indicating a degree of recovery in effect of the nudge associated with the nudge intervention information based on the nudge intervention information” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5). The examiner further notes that the calculated weight value teaches the undefined claimed recovery information in the broadest reasonable interpretation. The examiner notes that Dalecke teaches “wherein the update unit updates information of the nudge associated with the nudge intervention information in additional consideration of the nudge recovery information” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed” (Page 145, Section 4.3.5), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5), “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5), and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that the nudge information is “updated” based on the calculated nudge abrasion information and weight values (i.e. the undefined recovery in the broadest reasonable interpretation) for future nudge suggestions.
Claim Rejections - 35 USC § 103
14. 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.
15. 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.
16. This application currently names joint inventors. In considering patentability of the claims the examiner presumes that the subject matter of the various claims was commonly owned as of the effective filing date of the claimed invention(s) absent any evidence to the contrary. Applicant is advised of the obligation under 37 CFR 1.56 to point out the inventor and effective filing dates of each claim that was not commonly owned as of the effective filing date of the later invention in order for the examiner to consider the applicability of 35 U.S.C. 102(b)(2)(C) for any potential 35 U.S.C. 102(a)(2) prior art against the later invention.
17. Claim 4 is rejected under 35 U.S.C. 103 as being unpatentable over Dalecke et al. (Article entitled “Designing Dynamic and Personalized Nudges”, dated 03 July 2020) as applied to claims 1-3, 5, and 7 above, in view of Gutta et al. (U.S. PGPUB 2002/0142722).
15. Regarding claim 4, Dalecke further teaches an information processing device comprising:
A) wherein the acquisition unit additionally acquires environment information indicating an external environment state of the user in a predetermined period (Page 146, Section 4.3.5).
The examiner notes that Dalecke teaches “wherein the acquisition unit additionally acquires environment information indicating an external environment state of the user in a predetermined period” as “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5). The examiner further notes that stored situation information includes weather information (i.e. the claimed environment information) that corresponds to time data (i.e. the claimed predetermined period in the broadest reasonable interpretation).
Dalecke does not explicitly teach:
B) wherein the abrasion deriving unit derives the nudge abrasion information in additional consideration of the environment information.
Gutta, however, teaches “wherein the abrasion deriving unit derives the nudge abrasion information in additional consideration of the environment information” as “When recommending an item, the disclosed recommender retrieves the user preferences and evaluates the current environmental conditions. A recommendation score can be generated for each available item based on the user's demonstrated preferences under similar environmental conditions, such as in the same or a similar geographic area or under similar weather conditions” (Abstract) and “The environmental factors can be emphasized in the recommendation score, for example, by allowing a user to assign a weight each feature that is utilized to compute the overall score. In a further variation, the rules can be ordered in accordance with the number of environmental factors appearing in the conditions of each rule, or as selected by the user” (Paragraph 26).
The examiner further notes that although Dalecke clearly teaches weather (i.e. environment) information that is stored alongside nudges as well as nudge abrasion information that is a calculated score, there is no explicit teaching that such a score is based off of the stored weather information. Nevertheless, Gutta teaches the concept of a recommendation (i.e. nudge) score that is based off of environment data. The combination would result in the nudge abrasion information score of Dalecke to be based off of its weather (i.e. environment) information.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Gutta’s would have allowed Dalecke’s to provide a method for generating recommendation scores based off of environmental factors, as noted by Gutta (Paragraph 7).
18. Claim 6 is rejected under 35 U.S.C. 103 as being unpatentable over Dalecke et al. (Article entitled “Designing Dynamic and Personalized Nudges”, dated 03 July 2020) as applied to claims 1-3, 5, and 7 above, in view of Von der Weth et al. (Article entitled “Helping Users Tackle Algorithmic Threats on Social Media: A Multimedia Research Agenda”, dated 16 October 2020).
19. Regarding claim 6, Dalecke further teaches an information processing device comprising:
A) wherein the storage unit stores information of a plurality of types of nudges (Page 146, Section 4.3.5);
B) wherein the abrasion deriving unit derives the nudge abrasion information for each of the plurality of types of nudges (Page 145, Section 4.3.5, Page 146, Section 4.3.5); and
C) wherein the update unit updates information of one of the nudges in consideration of both the nudge abrasion information of the one nudge included in the plurality of types of nudges (Page 145, Section 4.3.5, Page 146, Section 4.3).
The examiner notes that Dalecke teaches “wherein the storage unit stores information of a plurality of types of nudges” as “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5). The examiner further notes that the storage of nudge types teaches the claimed storing of information of a plurality of types of nudges. The examiner further notes that Dalecke teaches “wherein the abrasion deriving unit derives the nudge abrasion information for each of the plurality of types of nudges” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5) and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that the calculation of an acceptvalue (i.e. the claimed nudge abrasion information in the broadest reasonable interpretation) is for each nudge type C. The examiner further notes that Dalecke teaches “wherein the update unit updates information of one of the nudges in consideration of both the nudge abrasion information of the one nudge included in the plurality of types of nudges” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed” (Page 145, Section 4.3.5), “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5), “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5), and “A high acceptance value combined with a high nudge frequency for activity C, indicates that the user has reacted positively to many nudges for C, and that nudging for C can possibly be stopped for a period or the nudge frequency can be reduced. On the other end, a very low acceptance value with a medium or high nudge frequency, indicates that the user is not currently willing to use this mode of transportation. The next nudges should probably suggest a different activity” (Page 146, Section 4.3.5). The examiner further notes that the nudge information is “updated” based on the calculated nudge abrasion information and weight values (i.e. the undefined recovery in the broadest reasonable interpretation) for future nudge suggestions.
Dalecke does not explicitly teach:
C) and the nudge abrasion information of nudges similar to the one nudge.
Von der Weth, however, teaches “and the nudge abrasion information of nudges similar to the one nudge” as “if the same or similar nudges gets repeatedly ignored, the platform may no longer display such nudges in the future” (Page 4430, Section 3.3).
The examiner further notes that although Dalecke clearly updates a nudge based off of its own abrasion information as well as the storage of abrasion information of similar nudges (See Table 5), there is no explicit teaching of the use of abrasion information of similar nudges. Nevertheless, Von der Weth teaches the concept of the use of ignored similar nudges (i.e. abrasion information of similar nudges) as a basis for not displaying a nudge in the future (i.e. “updating” nudge information of a nudge in the broadest reasonable interpretation). The combination would result in the updating of the nudge of Dalecke to also use abrasion information of similar nudges.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Von der Weth’s would have allowed Dalecke’s to provide a method for automatically configuring and personalizing nudges, as noted by Von der Weth (Page 4430, Section 3.3).
20. Claims 8-9 are rejected under 35 U.S.C. 103 as being unpatentable over Dalecke et al. (Article entitled “Designing Dynamic and Personalized Nudges”, dated 03 July 2020) as applied to claims 1-3, 5, and 7 above, in view of Ranganathan et al. (U.S. PGPUB 2021/0390875), and further in view of Bojja et al. (U.S. PGPUB 2017/0185581).
21. Regarding claim 8, Dalecke further teaches an information processing device comprising:
C) wherein the storage unit stores information of the plurality of types of nudges (Page 146, Section 4.3.5);
D) wherein the abrasion deriving unit includes an abrasion feature generating unit configured to generate an abrasion feature quantity indicating the nudge abrasion information based on the nudge intervention information for each of the plurality of types of nudges (Page 145, Section 4.3.5);
E) based on the abrasion feature quantities of the plurality of types of nudges (Page 145, Section 4.3.5).
The examiner notes that Dalecke teaches “wherein the storage unit stores information of the plurality of types of nudges” “When storing information about previous nudges, each nudge is represented by the triple (N,S,R), where N represents the selected nudge components (i.e., activity, nudge types, content and presentation), S represents the situation when the nudge was given (e.g., destination, time of day, weather), and R represents the user reaction to the nudge, i.e., accept or reject” (Page 146, Section 4.3.5). The examiner further notes that the storage of nudge types teaches the claimed storing of information of a plurality of types of nudges. The examiner further notes that Dalecke teaches “based on the abrasion feature quantities of the plurality of types of nudges” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5). The examiner further notes that the calculated nudge frequency for each nudge type teaches the claimed undefined abrasion feature quantity in the broadest reasonable interpretation.
Dalecke does not explicitly teach:
A) a behavioral modification estimating unit configured to estimate a behavioral modification probability which is a probability that the user adopts a behavior promoted by the nudge; and
B) a nudge selecting unit configured to select a nudge to be recommended to a user based on the behavioral modification probability;
E) wherein the behavioral modification estimating unit estimates the behavioral modification probability for each of the plurality of types of nudges.
Ranganathan, however, teaches “a behavioral modification estimating unit configured to estimate a behavioral modification probability which is a probability that the user adopts a behavior promoted by the nudge” as “In accordance with the disclosed principles, the trained classification model may calculate the probability that certain users will engage with one or more specific nudges or a combination of nudges” (Paragraph 35), “a nudge selecting unit configured to select a nudge to be recommended to a user based on the behavioral modification probability” as “In accordance with the disclosed principles, the trained classification model may calculate the probability that certain users will engage with one or more specific nudges or a combination of nudges. Accordingly, at step 308, the trained model may be used to create an evaluation matrix having the behavioral principles on one axis and another axis with possible nudge interactions (i.e., how the users may engage with or interact with the nudge) such as opening the nudge, clicking through the nudge, creation of a savings goal, etc. The intersection of the axes can be considered cells or matrix entries, which will be filled with the probabilities derived from the behavioral classification model's confidence levels (referred to herein as “behavior-based nudge probabilities”). In one or more embodiments, user individual and or financial data may be entered into the previously trained behavioral classification model, which may be run multiple times (e.g., one time for each behavioral principle used by the process 300) with the model's results (i.e., behavior-based nudge probabilities) being entered into the appropriate matrix cells” (Paragraph 35) and “At step 310, the process 300 may apply a policy for selecting the behavioral nudge or nudges that maximizes the target. For example, in accordance with the disclosed principles, the evaluation matrix may indicate that some users are triggered more by peer effects while others by loss aversion. As such, a nudge based on peer effects may be output to the user, providing a personalized nudge determined to be appropriate for the user” (Paragraph 37), and “wherein the behavioral modification estimating unit estimates the behavioral modification probability for each of the plurality of types of nudges” as “In accordance with the disclosed principles, the trained classification model may calculate the probability that certain users will engage with one or more specific nudges or a combination of nudges. Accordingly, at step 308, the trained model may be used to create an evaluation matrix having the behavioral principles on one axis and another axis with possible nudge interactions (i.e., how the users may engage with or interact with the nudge) such as opening the nudge, clicking through the nudge, creation of a savings goal, etc. The intersection of the axes can be considered cells or matrix entries, which will be filled with the probabilities derived from the behavioral classification model's confidence levels (referred to herein as “behavior-based nudge probabilities”). In one or more embodiments, user individual and or financial data may be entered into the previously trained behavioral classification model, which may be run multiple times (e.g., one time for each behavioral principle used by the process 300) with the model's results (i.e., behavior-based nudge probabilities) being entered into the appropriate matrix cells” (Paragraph 35) and “At step 310, the process 300 may apply a policy for selecting the behavioral nudge or nudges that maximizes the target. For example, in accordance with the disclosed principles, the evaluation matrix may indicate that some users are triggered more by peer effects while others by loss aversion. As such, a nudge based on peer effects may be output to the user, providing a personalized nudge determined to be appropriate for the user” (Paragraph 37).
The examiner further notes that Ranganathan teaches the concept of calculating probabilities of nudges that are then used for selecting such nudges. The combination would result in selecting the nudges of Dalecke via the use of such probabilities which would be based on the abrasion features quantities of Dalecke.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Ranganathan’s would have allowed Dalecke’s to provide a method for increasing user activity, as noted by Ranganathan (Paragraph 9).
Dalecke and Ranganathan do not explicitly teach:
F) wherein the nudge selecting unit selects a nudge with the highest behavioral modification probability as the nudge to be recommended to the user.
Bojja, however, teaches “wherein the nudge selecting unit selects a nudge with the highest behavioral modification probability as the nudge to be recommended to the user” as “the systems and methods use one or more emoji detection methods and classifiers to determine probabilities or confidence scores for emoji. The confidence scores represent a likelihood that a user will want to insert the emoji into a particular content or replace the particular content (or a portion thereof) with the emoji. For example, emoji having the highest confidence scores can be suggested to the user for possible insertion into a text message” (Paragraph 6).
The examiner further notes that although Ranganathan clearly teaches the calculation of probabilities for nudges, there is no selection of such nudges based off of a highest probability. Nevertheless, Bojja teaches the concept of selecting a recommendation based off of a highest confidence score (i.e. probability) for an activity of a user inserting a specific emoji. The combination would result in selecting the highest scored nudges of Ranganathan.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Bojja’s would have allowed Dalecke’s and Ranganathan’s to provide a method for reducing the time in selecting an activity, as noted by Bojja (Paragraph 4).
Regarding claim 9, Dalecke further teaches an information processing device comprising:
A) the abrasion feature quantities (Page 145, Section 4.3.5).
The examiner notes that Dalecke teaches “based on the abrasion feature quantities of the plurality of types of nudges” as “To have an indication of the usefulness of nudging, we store information about every nudge given to a person and register whether or not the suggestion given in the nudge was followed. To reflect the willingness of the user to follow nudges, we calculate an acceptance value with respect to both activity and nudge type. The activity acceptance value reflects how many times the user followed a nudge where the specific activity were suggested, while the nudge type acceptance value reflects how many times the user followed a nudge where the specific nudge type were used as motivation. Acceptance value for an activity (or nudge type)C is described in Formula 1, where Accept(C) represents the number of times a nudge for C is accepted, while Nudge(C) represents the number of times a nudge for C was given. We also register nudge frequency for each activity (or nudge type) C, reflecting the relative amount of nudges the user has received for C. Nudge frequency is described in Formula 2, where NudgeAll represents the total number of nudges given to the user…A combined weight is calculated as a combination of AcceptValue and NudgeFreq as shown in Formula 3. Two variables, x and y, determine the relative importance of the two values” (Page 145, Section 4.3.5). The examiner further notes that the calculated nudge frequency for each nudge type teaches the claimed undefined abrasion feature quantity in the broadest reasonable interpretation.
Dalecke does not explicitly teach:
A) wherein the behavioral modification estimating unit generates an estimation model for estimating the behavioral modification probability for each of the plurality of types of nudges based on user information which is information associated with attributes of a user; and
B) wherein the behavioral modification estimating unit estimates the behavioral modification probability for each of the plurality of types of nudges based on the estimation model.
Ranganathan, however, teaches “wherein the behavioral modification estimating unit generates an estimation model for estimating the behavioral modification probability for each of the plurality of types of nudges based on user information which is information associated with attributes of a user” as “In accordance with the disclosed principles, the trained classification model may calculate the probability that certain users will engage with one or more specific nudges or a combination of nudges. Accordingly, at step 308, the trained model may be used to create an evaluation matrix having the behavioral principles on one axis and another axis with possible nudge interactions (i.e., how the users may engage with or interact with the nudge) such as opening the nudge, clicking through the nudge, creation of a savings goal, etc. The intersection of the axes can be considered cells or matrix entries, which will be filled with the probabilities derived from the behavioral classification model's confidence levels (referred to herein as “behavior-based nudge probabilities”). In one or more embodiments, user individual and or financial data may be entered into the previously trained behavioral classification model, which may be run multiple times (e.g., one time for each behavioral principle used by the process 300) with the model's results (i.e., behavior-based nudge probabilities) being entered into the appropriate matrix cells” (Paragraph 35) and “At step 310, the process 300 may apply a policy for selecting the behavioral nudge or nudges that maximizes the target. For example, in accordance with the disclosed principles, the evaluation matrix may indicate that some users are triggered more by peer effects while others by loss aversion. As such, a nudge based on peer effects may be output to the user, providing a personalized nudge determined to be appropriate for the user” (Paragraph 37) and “wherein the behavioral modification estimating unit estimates the behavioral modification probability for each of the plurality of types of nudges based on the estimation model” as “In accordance with the disclosed principles, the trained classification model may calculate the probability that certain users will engage with one or more specific nudges or a combination of nudges. Accordingly, at step 308, the trained model may be used to create an evaluation matrix having the behavioral principles on one axis and another axis with possible nudge interactions (i.e., how the users may engage with or interact with the nudge) such as opening the nudge, clicking through the nudge, creation of a savings goal, etc. The intersection of the axes can be considered cells or matrix entries, which will be filled with the probabilities derived from the behavioral classification model's confidence levels (referred to herein as “behavior-based nudge probabilities”). In one or more embodiments, user individual and or financial data may be entered into the previously trained behavioral classification model, which may be run multiple times (e.g., one time for each behavioral principle used by the process 300) with the model's results (i.e., behavior-based nudge probabilities) being entered into the appropriate matrix cells” (Paragraph 35) and “At step 310, the process 300 may apply a policy for selecting the behavioral nudge or nudges that maximizes the target. For example, in accordance with the disclosed principles, the evaluation matrix may indicate that some users are triggered more by peer effects while others by loss aversion. As such, a nudge based on peer effects may be output to the user, providing a personalized nudge determined to be appropriate for the user” (Paragraph 37).
The examiner further notes that Ranganathan teaches the concept of calculating probabilities of nudges that are then used for selecting such nudges based off of a generated model (i.e. the claimed estimation model). The combination would result in selecting the nudges of Dalecke via the use of such probabilities which would be based on the abrasion features quantities of Dalecke.
It would have been obvious to one of ordinary skill in the art before the effective filing date of instant invention to combine the teachings of the cited references because teaching Ranganathan’s would have allowed Dalecke’s to provide a method for increasing user activity, as noted by Ranganathan (Paragraph 9).
Conclusion
22. The prior art made of record and not relied upon is considered pertinent to applicant's disclosure.
U.S. PGPUB 2019/0236676 issued to Achan et al. on 01 August 2019. The subject matter disclosed therein is pertinent to that of claims 1-9 (e.g., methods to nudge users).
U.S. PGPUB 2014/0164951 issued to Gupta on 12 June 2014. The subject matter disclosed therein is pertinent to that of claims 1-9 (e.g., methods to nudge users).
U.S. PGPUB 2022/0318839 issued to Dickison on 06 October 2022. The subject matter disclosed therein is pertinent to that of claims 1-9 (e.g., methods to nudge users).
Contact Information
23. Any inquiry concerning this communication or earlier communications from the examiner should be directed to Mahesh Dwivedi whose telephone number is (571) 272-2731. The examiner can normally be reached on Monday to Friday 8:20 am – 4:40 pm.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Charles Rones can be reached (571) 272-4085. The fax number for the organization where this application or proceeding is assigned is (571) 273-8300.
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Mahesh Dwivedi
Primary Examiner
Art Unit 2168
July 01, 2026
/MAHESH H DWIVEDI/Primary Examiner, Art Unit 2168
Prosecution Insights