DETAILED ACTION
This communication is in response to the application and preliminary amendment filed 3/13/24 in which claims 1, 5-13, 15, 29, 31, and 39 were amended, and claims 16-28, 30, 32-34, 37-38, and 40 were canceled. Claims 1-15, 29, 31, 35-36, and 39 are pending.
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Information Disclosure Statement
The information disclosure statement (IDS) submitted on 3/13/24 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
The information disclosure statement (IDS) submitted on 9/27/24 is in compliance with the provisions of 37 CFR 1.97. Accordingly, the information disclosure statement is being considered by the examiner.
Allowable Subject Matter
Claims 31, 35-36, and 39 are allowed.
The following is a statement of reasons for the indication of allowable subject matter: the prior art does not disclose initializing a deep Q neural network to be used for learning associations between actions and rewards, wherein actions includes, for each event, a recommended notification type and associated predicted emotional state of the user. Relevant prior art includes the following references:
Mehrotra, Abhinav, et al. "MyTraces: Investigating correlation and causation between users’ emotional states and mobile phone interaction." Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 1.3 (2017): 1-21.
Rachuri, Kiran K., et al. "EmotionSense: a mobile phones based adaptive platform for experimental social psychology research." Proceedings of the 12th ACM international conference on Ubiquitous computing. 2010.
US 2023/0113072 A1 – Method, system, and medium for affective music recommendation and composition (see ¶ 25 (“A further embodiment is directed to a method for training a machine learning model to predict human affective responses to musical features, comprising: presenting a listener with music having a set of musical features; obtaining affective response data from the listener indicating the listener's affective response to presentation of the music; labelling the musical features of the music with the affective response data to generate labelled musical feature data; and using the labelled musical feature data as training data to train the machine learning model to predict the affective response data based on the musical feature data.”))
US 2021/0344560 A1 – Adapting a device to a user based on user emotional state (see Abstract (“A change in the emotional state of a user of a device is detected, and in response to this change a reason for the change in user emotional state is determined. This determination is made based on both the current user emotional state and context data for the device or user. The device then adapts to the user based on the current emotional state and the reason for the change in user emotional state. This adaptation of the computing device refers to an alteration of the operation of the computing device with a goal of increasing the likelihood of the user being in a good emotional state (e.g., happy, relaxed) and reducing the likelihood of the user being in a bad emotional state (e.g., sad, angry).”)).
Claim Rejections - 35 USC § 112
The following is a quotation of 35 U.S.C. 112(b):
(b) CONCLUSION.—The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the inventor or a joint inventor regards as the invention.
The following is a quotation of 35 U.S.C. 112 (pre-AIA ), second paragraph:
The specification shall conclude with one or more claims particularly pointing out and distinctly claiming the subject matter which the applicant regards as his invention.
Claims 7 and 9 are rejected under 35 U.S.C. 112(b) or 35 U.S.C. 112 (pre-AIA ), second paragraph, as being indefinite for failing to particularly point out and distinctly claim the subject matter which the inventor or a joint inventor (or for applications subject to pre-AIA 35 U.S.C. 112, the applicant), regards as the invention.
Claim 7 recites the limitation "the context types" in line 1. There is insufficient antecedent basis for this limitation in the claim.
Claim 9 recites the limitation "the emotional state" in line 1. There is insufficient antecedent basis for this limitation in the claim.
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 1-15 and 29 are rejected under 35 U.S.C. 101 because the claimed invention is directed to an abstract idea without significantly more.
Claim 1
A computer-implemented method for determining, using a machine learning (ML) model, extended reality (XR) notification types for delivering notification of an event to a user, the method comprising:
receiving user information, wherein the user information includes user characteristics and relationships data;
receiving event information, wherein the event information includes event type data;
determining, using a machine learning (ML) model, recommended notification types for delivering notification of the event to the user and, for each recommended notification type, predicted emotional state information including a predicted emotional state of the user and a rating;
receiving local preferences information for the user, wherein the local preferences information includes one or more of local preferences for different notification types, different event types, and different wanted emotional states;
selecting the notification type for delivering the notification of the event to the user by comparing, for each recommended notification type, the predicted emotional state information and the local preferences information; and
delivering the notification of the event to the user using the selected notification type.
Step 1: YES. Claim 1 (and its dependent claims) are directed to a process and, therefore, fall under a statutory category.
Step 2A Prong 1: YES. As indicated by the highlighted portions, claim 1 recites determining recommended notification types for delivering event notification to a user and a predicted emotional state of the user and rating for each recommended notification type. As drafted, this process may be performed mentally by a user by evaluation, opinion, and judgment and, therefore, falls under the Mental Processes grouping of abstract ideas. Further, the limitation of selecting a notification types for delivering the event notification to the user by comparing the predicted emotional state and local preferences for each recommended notification type may also be performed by evaluation, opinion, and judgment and, therefore, also falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2/Step 2B: NO. Receiving user and event information are data gathering steps. Similarly, receiving local preferences information for the user is also a data gathering step. The type or source of data does not cause a data gathering step to integrate the judicial exception into a practical application. Further, delivering the event notification to the user is a data outputting step. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.” “Using a machine learning (ML) model)…using a machine learning (ML) model” are mere instructions to apply the exception using generic computer components.
Accordingly, claim 1 is ineligible.
Claim 2
The method according to claim 1, further comprising: wherein the ML model comprises a graph neural network (GNN).
Step 2A Prong 2: NO. Recitation of the ML model as a graph neural network is mere instruction to apply the exception.
Accordingly, claim 2 is ineligible.
Claim 3
The method according to claim 2, further comprising:
collecting data including user information, notification information and context information, wherein the user information includes user characteristics and relationships data, the notification information includes notification types and relationships data, and the context information includes context types and relationships data;
building, using the user characteristics and relationships data, a user-to-user dependency graph representing associations between users; [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
generating, using the user-to-user dependency graph, first user embeddings1; [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
building, using the context types and relationships data and the notification types and relationships data, a context-to-notification dependency graph representing associations between contexts and notifications; [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
generating, using the context-to-notification dependency graph, first notification embeddings2 and context embeddings3; [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
building, using the first notification embeddings and the first user embeddings, a notification-to-user dependency graph representing associations between users and notifications; [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
generating, using the notification-to-user dependency graph, second notification embeddings and second user embeddings; [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
combining the generated first and second user embeddings, first and second notification embeddings and context embeddings; and [Step 2A Prong 1: YES. Mental Process capable of being performed by evaluation, opinion, and judgment]
training the GNN using the combined embeddings to predict recommended notification types for delivering notifications of events to users and, for each recommended notification type for each user, predicted emotional state information including a predicted emotional state of the user and a rating.
Step 2A Prong 1: YES. As indicated by the highlighted portions, claim 2 recites a mental process.
Step 2A Prong 2/Step 2B: NO. The limitation of collecting data is data gathering which is considered insignificant extra-solution activity. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.” The limitation of training a GNN using the combined embedding to predict recommended notification types is mere instruction to apply the exception.
Accordingly, claim 3 is ineligible.
Claim 4
The method according to claim 3, further comprising:
receiving user rating information for the notification delivered to the user, wherein the user rating information includes actual emotional state information for the user; and
using the received user rating information for retraining the GNN.
Step 2A Prong 2/Step 2B: NO. Receiving information is insignificant extra-solution activity under 2A. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.” Retraining the GNN using the received information is mere instruction to apply the exception.
Accordingly, claim 4 is ineligible.
Claim 5
5. The method according to claim 1, wherein the user characteristics and relationships data includes one or more of: age, gender, education, interests, friend status, and social networks status.
Step 2A Prong 2/Step 2B: NO. Receiving information is insignificant extra-solution activity under 2A. The type of information does not cause the data gathering activity to integrate the judicial exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 5 is ineligible.
6. The method according to claim 1, wherein the notification types and relationships data includes one or more of: visual, auditory, tactile, smell, taste, and receiving device type.
Step 2A Prong 2/Step 2B: NO. Receiving information is insignificant extra-solution activity under 2A. The type of information does not cause the data gathering activity to integrate the judicial exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 6 is ineligible.
7. The method according to claim 1, wherein the context types and relationships data includes one or more of: alarm, meeting, weather change, advertisement, activity type, indoor, outdoor, spatial information, physical distance, and geographical location.
Step 2A Prong 2/Step 2B: NO. Receiving information is insignificant extra-solution activity under 2A. The type of information does not cause the data gathering activity to integrate the judicial exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 7 is ineligible.
Claim 8
The method according to claim 1, wherein the event type data includes one or more of: alarm, weather change, new email, new voicemail, new message, news, announcement, and advertisement.
Step 2A Prong 2/Step 2B: NO. Receiving information is insignificant extra-solution activity under 2A. The type of information does not cause the data gathering activity to integrate the judicial exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 8 is ineligible.
Claim 9
The method according to claim 1, wherein the emotional state of the user corresponds to one or more of: angry, tense, excited, elated, happy, relaxed, calm, exhausted, tired, sad, a measure of valence, and a measure of arousal.
Step 2A Prong 1: YES. Determining the emotional state of the user is a mental process.
Accordingly, claim 9 is ineligible.
Claim 10
The method according to claim 1, wherein the local preferences information for the user is based on one or more of: different levels of attentiveness the user is experiencing and different emotional states of the user that the user has deprioritized.
Step 2A Prong 2/Step 2B: NO. Receiving information is insignificant extra-solution activity under 2A. The type of information does not cause the data gathering activity to integrate the judicial exception into a practical application. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.”
Accordingly, claim 10 is ineligible.
Claim 11
The method according to claim 1, wherein selecting the notification type for delivering the notification of the event to the user by comparing, for each recommended notification type, the predicted emotional state information and the local preferences information comprises:
selecting the notification type with the highest rating.
Step 2A Prong 1: YES. Selecting a notification type with the highest rating encompasses a Mental Process.
Accordingly, claim 11 is ineligible.
Claim 12
The method according to claim 1, wherein selecting the notification type for delivering the notification of the event to the user by comparing, for each recommended notification type, the predicted emotional state information and the local preferences information comprises:
comparing, for each recommended notification type, the predicted emotional state information and the local preferences information using cosine similarity according to:
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and
selecting the notification type that is most similar to the local preferences information.
Step 2A Prong 1: YES. Selecting the notification type by comparing the predicted emotional state information and the local preferences information using cosine similarity encompasses a Mental Process. Alternatively, it recites a mathematical calculation and, therefore, also falls under the Mathematical Concepts grouping of abstract ideas.
Accordingly, claim 12 is ineligible.
Claim 13
The method according to claim 3, wherein each user, each notification type, and each context type correspond to a node and each of the user characteristics and relationship data, each of the notification types and relationship data, and each of the context types and relationship data correspond to a set of features for each node, and
training the GNN using the combined embeddings comprises:
applying a linear transformation to the features for each node; and
aggregating the features only for the nodes that are related.
Step 2A Prong 1: YES. Applying a linear transformation to the features for each node encompasses a mathematical calculation and, therefore, falls under the Mathematical Concepts grouping of abstract ideas. Aggregating the features only for the nodes that are related encompasses a mental process.
Step 2A Prong 2/Step 2B: NO. Describing each user, each notification type, and each context type and each of the user characteristics and relationship data, each of the notification types and relationship data, and each of the context types and relationship data as corresponding to a set of features for each node, is mere instruction to apply the exception.
Accordingly, claim 13 is ineligible.
Claim 14
The method according to claim 13, wherein applying the linear transformation to the features for each node and aggregating the features only for the nodes that are related corresponds to a multilayer perceptron (MLP).
Step 2A Prong 2/Step 2B: NO. Describing the linear transformation and the aggregation of the features of nodes that are related as corresponding to an MLP is mere instruction to apply the exception using generic computer components recited at a high level of generality.
Accordingly, claim 14 is ineligible.
Claim 15
A central computing device for determining, using a machine learning (ML) model, extended reality (XR) notification types for delivering notification of an event to a user, comprising:
a memory; and
processing circuitry coupled to the memory, wherein the processing circuitry is configured to:
receive user information, wherein the user information includes user characteristics and relationships data;
receive event information, wherein the event information includes event type data;
determine, using a machine learning (ML) model, recommended notification types for delivering notification of the event to the user and, for each recommended notification type, predicted emotional state information including a predicted emotional state of the user and a rating;
receive local preferences information for the user, wherein the local preferences information includes one or more of local preferences for different notification types, different event types, and different wanted emotional states;
select the notification type for delivering the notification of the event to the user by comparing, for each recommended notification type, the predicted emotional state information and the local preferences information; and
deliver the notification of the event to the user using the selected notification type.
Step 1: YES. Claim 15 (and its dependent claims) is directed to a system and, therefore, fall under a statutory category.
Step 2A Prong 1: YES. As indicated by the highlighted portions, claim 1 recites determining recommended notification types for delivering event notification to a user and a predicted emotional state of the user and rating for each recommended notification type. As drafted, this process may be performed mentally by a user by evaluation, opinion, and judgment and, therefore, falls under the Mental Processes grouping of abstract ideas. Further, the limitation of selecting a notification types for delivering the event notification to the user by comparing the predicted emotional state and local preferences for each recommended notification type may also be performed by evaluation, opinion, and judgment and, therefore, also falls under the Mental Processes grouping of abstract ideas.
Step 2A Prong 2/Step 2B: NO. Receiving user and event information are data gathering steps. Similarly, receiving local preferences information for the user is also a data gathering step. The type or source of data does not cause a data gathering step to integrate the judicial exception into a practical application. Further, delivering the event notification to the user is a data outputting step. Under 2B this insignificant extra solution activity is well understood routine and conventional activity. See “Receiving or transmitting data over a network, e.g., using the Internet to gather data, Symantec, 838 F.2d at 1321, 120 USPQ2d at 1362.” “Using a machine learning (ML) model)…using a machine learning (ML) model” are mere instructions to apply the exception using generic computer components. Similarly, “central computing device…using a machine learning (ML) model…a memory; and processing circuitry coupled to the memory, wherein the processing circuitry is configured to” is mere instruction to apply the exception.
Accordingly, claim 15 is ineligible.
Claim 29
A computer program product comprising a non-transitory computer readable medium storing a computer program comprising instructions which, when executed by processing circuity of a device, causes the device to perform the method of claim 1.
Step 2A Prong 2/Step 2B: NO. The additional elements of “a non-transitory computer readable medium storing a computer program comprising instructions…processing circuitry of a device” are mere instruction to apply the exception.
Accordingly, claim 29 is ineligible.
Conclusion
Any inquiry concerning this communication or earlier communications from the examiner should be directed to SHAHID KHAN whose telephone number is (571)270-0419. The examiner can normally be reached M-F, 9-5 est.
Examiner interviews are available via telephone, in-person, and video conferencing using a USPTO supplied web-based collaboration tool. To schedule an interview, applicant is encouraged to use the USPTO Automated Interview Request (AIR) at http://www.uspto.gov/interviewpractice.
If attempts to reach the examiner by telephone are unsuccessful, the examiner’s supervisor, Usmaan Saeed can be reached at (571)272-4046. The fax phone number for the organization where this application or proceeding is assigned is 571-273-8300.
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/SHAHID K KHAN/Primary Examiner, Art Unit 2146
1 For example, in the case of user embeddings the input is a graph where every node contains a set of features that represent the user (features can be age, sex and others) and the edges between the nodes (users) determine whether the users are related or not. Using such an input the graph embedding (user embedding in this case) is produced by applying a linear transformation (such as an MLP) to the features of every node and then aggregating these features only for the nodes that are related. Spec. ¶ 78.
2 In the case of notification embeddings, for example, the input features are the features of each notification (such as the type of the notification (audio/visual) and others) and the edges are the relations between the notifications i.e., if they are coming from the same source. Spec. ¶ 79.
3 In the case of context embeddings, for example, the input features are the features of each context (indoor/outdoor, activity type) and the edges are the relationship between contexts i.e., if we consider spatial contexts the physical distance between the geographical location of each context. Spec. ¶ 80.
Prosecution Insights