DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA .
Continued Examination Under 37 CFR 1.114
A request for continued examination under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's submission filed on 8 June 2026 has been entered.
Introductory Remarks
In response to communications filed on 8 June 2026, claims 1, 9-10, 12-13, and 16-18 are amended per Applicant's request. No claims were cancelled. No claims were withdrawn. No new claims were added. Therefore, claims 1-20 are presently pending in the application, of which claims 1, 13, and 18 are presented in independent form.
The previously raised 103 rejection of the pending claims is withdrawn in view of the amendments to the claims. A new ground(s) of rejection has been issued.
Response to Arguments
Applicant’s arguments filed 8 June 2026 with respect to the rejection of the claims under 35 U.S.C. 103 (see Remarks, p. 8) have been fully considered but are moot because the arguments do not apply to the new references (and thus new combination of references) being used in the current rejection.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
Claims 1-7, 9-12, and 18-20 are rejected under 35 U.S.C. 103 as being unpatentable over Shoemaker et al. (“Shoemaker”) (US 2015/0185995 A1), in view of Harlow et al. (“Harlow”) (US 2018/0357915 A1).
Regarding claim 1: Shoemaker teaches A method for data structure modification, the method comprising:
obtaining first data structures that represent user interactions with first user content by a user of a client device; labeling the first data structures using content features of the first user content; training [machine learning] model using the labeled first data structures to obtain user-specific weights for the user of the client device (Shoemaker, [0022-0023], [0032-0034], [FIG. 1] and [FIG. 2A], where user history 110 such as previous actions 111-114 represented by IACA of the previous actions comprising various fields (i.e., thus, the user history corresponding to the claimed “first data structures” which are “labeled”) are provided to (e.g., read by) machine learning engine 120 to generate predictive models (i.e., the “generation” of predictive models implying “training a…model using the labeled first data structures”). See also Shoemaker, [0035] and [0048], where the machine learning 120 may process user history 110 to produce predictive models 130 (i.e., “training the [machine learning] model”), where models have assigned weights to one or more pieces or fields (i.e., “labels”) of the extracted information about content or action (IACA) of previous (user) actions (see also, e.g., Shoemaker, [0016] and [0022]) (i.e., “user-specific weights”). See Shoemaker, [0029] and [0032-0034], where the information about content includes one or more fields of the content that is structured, e.g., stored in or retrieved from a database, where any fields of the content can be used as information about content, and fields can be different based on content. See Shoemaker, [0046], where IACA associated with action 240 is extracted, e.g., a data type of photo, a timestamp, a location of home, and an identification of a person on the photo being the relative (i.e., “labeling the first data structures using content features of the first user content”));
receiving a second data structure that represents user interactions with second user content by the user; obtaining a predicted [value] for the second data structure based on an output of the trained [machine learning] model using content features of the second user content and the user-specific weights as inputs to the trained [machine learning] model; and modifying the second data structure to include the predicted [value] … (Shoemaker, [0017], where prediction engine 150 identifies and uses one or more predictive models 130 based on the user actions 140 to provide the user with one or more predictions 160 (i.e., “based on an output of the trained [machine learning] model”); recall from Shoemaker in the above limitations with respect to the “using content features of the second user content and the user-specific weights as inputs to the trained [machine learning] model”). See Shoemaker, [0048], where models may have assigned weights to one or more pieces or fields of the extracted information about content or action (IACA) of previous (user) actions (see also Shoemaker, [0016] and [0022]) (i.e., “user-specific weights”).
See Shoemaker, [0049-0050], where one or more predictions are presented to the user, the user may provide input to identify or select one of the predictions, or the predictions may be prioritized, ranked, or reduced (i.e., “obtaining a predicted value for the second data structure”). The final action 180 (i.e., selection) is provided as feedback, e.g., history 110 (i.e., analogous to “modifying the second data structure to include the predicted value”) (see also Shoemaker, [0022], where user history is provided to/read by machine learning engine 120 to generate predictive models, thereby implying that the history 110 is updated, i.e., “modified”)).
Although Shoemaker does not appear to explicitly state that the “second” data structure, e.g., IACA information, is modified within the user history 110, one of ordinary skill in the art would have been suggested by Shoemaker to have modified Shoemaker to explicitly update the corresponding IACA information in the user history 110 with the motivation of ensuring that the corresponding data is persisted, thereby resulting in improving the system, e.g., accuracy, performance, predictions, etc. (Shoemaker, [0062]).
Shoemaker does not appear to explicitly teach that the machine learning model is a neural network model; that the predicted value pertains to a duration value corresponding to a time interval; [and modifying the second data structure to include the predicted] duration [value], when, based on comparing the predicted duration value with the second data structure, it is determined the second data structure is inconsistent with the predicted duration value.
Harlow teaches the machine learning model is a neural network model (Harlow, [0036], where prediction module 234 may predict activity 232 using a neural network model);
that the predicted value pertains to a duration value corresponding to a time interval (Harlow, [0035-0036], where predicted activity 232 includes patterns of engagement such as duration spent on various tasks or screens, etc.); [and]
[modifying the second data structure to include the predicted] duration [value] when, based on comparing the predicted duration value with the second data structure, it is determined the second data structure is inconsistent with the predicted duration value (Harlow, [0035-0037], where predictive update module 230 updates user preferences 220 based on an activity 232 of user 110, in which update module 236 compares the activity predicted by prediction module 234 with the actual activity 232 of user 110. When the predicted and actual activities do not match, update module 236 may adjust the values of one or more of user parameters 220. Recall from Harlow, [0035-0036] above that the value is based on predicted activity 232 of the user, the activity 232 including duration spent on various tasks or screens (i.e., “predicted duration value”)).
Although Harlow does not appear to explicitly state that a second data structure comprising predicted activities is persisted, one of ordinary skill in the art would have found it obvious to have modified Harlow to have persisted such information within the user parameters 220 with the motivation of persisting a more detailed log of past performance of a user, thereby preserving more rich information for future analysis (see, e.g., Harlow, [0029-0030])).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Shoemaker and Harlow (hereinafter “Shoemaker as modified”) with the motivation of utilizing neural networks, which produce more accurate results despite distortions or less-than-perfect input data.1,2 Furthermore, it would have been obvious to one of ordinary skill in the art to have considered duration as a value with the motivation of assessing a variety of dimensions that may be relevant to predicting user activities, thereby potentially increasing the accuracy of the predictions. Lastly, it would have been obvious to one of ordinary skill in the art to have compared predicted events with actual stored events with the motivation of ensuring that relevant information is updated as needed, e.g., only when inconsistent, thereby conserving processing resources.
Regarding claim 2: Shoemaker as modified teaches The method of claim 1, wherein the user interactions with the first user content and the user interactions with the second user content are active interactions (Shoemaker, [0030], where information about one or more actions taken in association with content includes history usage information. See also Shoemaker, [0061], where actions may include selecting who to share content with, selecting how the content is shared, e.g., selecting what application to share the content. See also, e.g., Shoemaker, [0019], where user actions may include browsing on the Internet).
Regarding claim 3: Shoemaker as modified teaches The method of claim 1, wherein labeling the first data structures comprises labeling the first data structures using an identifier of an author of the first user content (Shoemaker, [0024], where information about content can be metadata including descriptive metadata such as description of a resource for identification and retrieval including author. See also Shoemaker, [0029], where if the content is an email message, and the fields (e.g., To, From, Subject, CC, BCC, body, etc.) can be used as information about content, where IACA fields may comprise any fields and any number of fields (Shoemaker, [0032])).
Regarding claim 4: Shoemaker as modified teaches The method of claim 1, wherein labeling the first data structures comprises labeling the first data structures using a timestamp indicating when the first user content was viewed (Shoemaker, [0024] and [0046], where fields extracted from the information about content associated with an action 240 may include timestamp. See Shoemaker, [0019-0021], [0025], and [0060], where user actions may be browsing (i.e., “view[ing] first user content”) on the Internet, e.g., the time when the webpage is visited (i.e., “viewed”)).
Regarding claim 5: Shoemaker as modified teaches The method of claim 1, wherein labeling the first data structures comprises, for each of the first data structures, creating a training data structure that includes one or more labels from the first user content as first fields and portions of a corresponding first data structure as second fields (Shoemaker, [0022], where prediction generator 135 may trigger machine learning 120 to process history 110. See Shoemaker, [0016], where the model generation portion uses machine learning 120 to analyze previous actions of a user, the behavior and/or habits of the user, and data associated with the user (collectively referred to as user history 110), to generate predictive models 130 associated with the user (i.e., implying that the history 110 is a form of “training data”, i.e., corresponding to the claimed “training data structure”). See Shoemaker, [0046], where IACA associated with action 240 is extracted, e.g., a data type of photo, a timestamp, a location of home, and an identification of a person on the photo being the relative (i.e., “first fields…second fields”)).
Regarding claim 6: Shoemaker as modified teaches The method of claim 1, wherein the user interactions with the first user content include a passive behavior of the user (Shoemaker, [0025] and [0030], where information about content includes any data recorded or generated about the content, including the location of the user when the webpage is visited, information about the network, system, devices, operating systems, applications, software, etc. used to perform the actions, date, time, location when the actions are performed, etc.).
Regarding claim 7: Shoemaker as modified teaches The method of claim 1, wherein the content features of the first user content include one or more of portions of the first user content that are viewed by the user, user identifiers for other users associated with the user interactions, and timestamps associated with the user interactions (Shoemaker, [0023-0025] and [0029-0030], where information about content includes any data recorded or generated about the content, including time when the action was performed, as well as description of a resource such as file name, title, etc., as well as the URL of the webpage visited by the user. If the content is email, then the IACA fields may include the subject and body that can be used as information about content).
Regarding claim 9: Shoemaker as modified teaches The method of claim 1, wherein obtaining the predicted duration value comprises:
configuring the trained neural network model using the user-specific weights (Shoemaker, [0022], where user history 110 (e.g., previous actions, behavior, habits, data associated with a user) includes previous actions 111-114 provided to (e.g., read by) machine learning engine 120 to generate predictive models (i.e., the “generation” of predictive models implying “training a…model using the…first data structures”). See also Shoemaker, [0035], where the machine learning 120 may process user history 110 to produce predictive models 130. See Shoemaker, [0048], where models have assigned weights to one or more pieces or fields (i.e., “labels”) of the extracted information about content or action (IACA) of previous actions (see also, e.g., Shoemaker, [0022]). See Harlow in claim 1 above with respect to the machine learning model being a “neural network” model and the value being a “duration value” as claimed); and
providing the content features to input nodes of the trained neural network model (Shoemaker, [0017], where prediction engine 150 identifies and uses one or more predictive models 130 based on the user actions 140 to provide the user with one or more predictions 160, which may include one or more action options. Recall from Shoemaker, [0029] and [0032-0034], where the information about content includes one or more fields of the content that is structured, e.g., stored in or retrieved from a database, where any fields of the content can be used as information about content, and fields can be different based on content. See Shoemaker, [0046], where IACA associated with action 240 is extracted, e.g., a data type of photo, a timestamp, a location of home, and an identification of a person on the photo being the relative. See Harlow in claim 1 above with respect to the machine learning model being a “neural network” model).
Regarding claim 10: Shoemaker as modified teaches The method of claim 1, wherein modifying the second data structure comprises updating a field of the second data structure to include the predicted duration value (Harlow, [0035-0037], where predictive update module 230 updates user preferences 220 based on an activity 232 of user 110, in which update module 236 compares the activity predicted by prediction module 234 with the actual activity 232 of user 110. When the predicted and actual activities do not match, update module 236 may adjust the values of one or more of user parameters 220 (i.e., “updating a field of the second data structure”). See Harlow in claim 1 above with respect to the second data structure being updated to include the “predicted duration value”).
Regarding claim 11: Shoemaker as modified teaches The method of claim 1, wherein receiving the second data structure comprises receiving the second data structure from a signal service that generates the second data structure based on the user interactions with the second user content by the user (Shoemaker, [0016], where history 110 can be the history of one or more applications, websites, services of different providers, etc., the user has given permissions or consents to gather the user’s history and user actions. See Shoemaker, [0027], where data recorded or generated about the content may be device-generated, where devices may communicate with a cellular network (Shoemaker, [0072])).
Although Shoemaker does not appear to explicitly state that the data is generated “from a signal service” as claimed, one of ordinary skill in the art would have been suggested by Shoemaker’s disclosure such that the cellular network performs this generation with the motivation of offloading computing resources to be performed by another device instead of the user’s device (which may, for example, require more resource consumption).
Regarding claim 12: Shoemaker as modified teaches The method of claim 1, wherein the method further comprises processing a telemetry log for the client device that represents the user interactions with the first user content (Shoemaker, [0030], where history usage information includes usage records or usage logs from, e.g., a device (see, e.g., Shoemaker, [0027])); and
wherein obtaining the predicted duration value comprises cross-verifying the telemetry log with the output of the trained neural network model (Shoemaker, [0018] and [0050], where the final action 180 (e.g., the user’s selection) is provided as feedback to the history 110 (i.e., “telemetry log”; see, e.g., Shoemaker, [0030], where history usage information may be stored as usage records or usage logs), and machine learning 120 may incorporate or account for the user’s feedback in making changes to one or more models already generated. See Harlow in claim 1 above with respect to the value being a “duration value” and the machine learning model being a “neural network” model).
Regarding claim 18: Claim 18 recites substantially the same claim limitations as claim 1, and is rejected for the same reasons.
Note that Shoemaker teaches A system for processing data structures that represent user interactions, the system comprising: at least one processor; memory storing instructions that, when executed by the at least one processor, cause the system to perform a set of operations, the set of operations comprising [the claimed steps] (Shoemaker, [0063], [0068], and [0075], where the disclosed systems may be implemented by processor(s) by loading computer executable instructions stored on a medium onto one or more of the processors, such media including signals).
Regarding claim 19: Claim 19 recites substantially the same claim limitations as claim 7, and is rejected for the same reasons.
Regarding claim 20: Claim 20 recites substantially the same claim limitations as claim 12, and is rejected for the same reasons.
Claim 8 is rejected under 35 U.S.C. 103 as being unpatentable over Shoemaker et al. (“Shoemaker”) (US 2015/0185995 A1), in view of Harlow et al. (“Harlow”) (US 2018/0357915 A1), in further view of Wheatley et al. (“Wheatley”) (US 2015/0181289 A1).
Regarding claim 8: Shoemaker as modified teaches The method of claim 7, but does not appear to explicitly teach wherein the second data structure is modified to have changed timestamps.
Wheatley teaches wherein the second data structure is modified to have changed timestamps (Wheatley, [0040], where the application may compare the time of an inconsistent activity to the time associated with events of the user. This implies that the data structure may have timestamps stored. Although Wheatley does not appear to explicitly state that the second data structure is “modified” to have changed timestamps, one of ordinary skill in the art would have found it obvious to have modified Wheatley to have such changed timestamps with the motivation of being able to quickly pinpoint certain activities/events, rather than looking at a range of activities/events, i.e., faster processing).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Shoemaker as modified and Wheatley with the motivation of being able to accurately determine whether activities are consistent/inconsistent.
Claims 13-17 are rejected under 35 U.S.C. 103 as being unpatentable over Shoemaker et al. (“Shoemaker”) (US 2015/0185995 A1), in view of Hu et al. (“Hu”) (US 2021/0231449 A1), in further view of Copper (“Copper”) (US 2019/0340533 A1).
Regarding claim 13: Shoemaker teaches A method for data structure modification, the method comprising:
processing a telemetry log representing first user interactions with first user content by a user of a client device to identify the first user interactions (Shoemaker, [0030], where history usage information includes usage records or usage logs from, e.g., a device (see, e.g., Shoemaker, [0027]));
generating [predictions] that correspond[] to the identified first user interactions based on an output of a trained [machine learning] model using content features of the first user content and user-specific weights as inputs to the trained [machine learning] model … (Shoemaker, [0022], where user history 110 (e.g., previous actions, behavior, habits, data associated with a user) (i.e., analogous to the claimed “telemetry log”, as the user history was derived from logs; see Shoemaker, [0027] and [0030] above) includes previous actions 111-114 provided to (e.g., read by) machine learning engine 120 to generate predictive models (i.e., the “generation” of predictive models implying a “trained [machine learning] model using the…first data structures”). See also Shoemaker, [0017] and [0035], where the prediction engine 150 / machine learning model 120 may process user actions 140 / user history 110, to produce predictive models 130, thereby providing the user with one or more predictions 160, which may include one or more action options. See Shoemaker, [0029] and [0032-0034], where the information about content includes one or more fields of the content that is structured, e.g., stored in or retrieved from a database, where any fields of the content can be used as information about content, and fields can be different based on content. See Shoemaker, [0046], where IACA associated with action 240 is extracted, e.g., a data type of photo, a timestamp, a location of home, and an identification of a person on the photo being the relative. See also Shoemaker, [0048], where models may have assigned weights to one or more pieces or fields of the extracted information about content or action (IACA) of previous actions (see also Shoemaker, [0022]) (i.e., “user-specific weights”)) … .
Shoemaker does not appear to explicitly teach that the machine learning model is a neural network model; [generating a] predicted data structure, including mapping entries within the telemetry log to fields within the predicted data structure, and populating the fields within the predicted data structure with data based on the mapped entries within the telemetry log, wherein the predicted data structure comprises a plurality of fields including a duration field indicating a time interval; identifying discrepancies between the predicted data structure and first data structures corresponding to the first user interactions of the user of the client device; and updating, using the predicted data structure, the first data structures based on the identified discrepancies.
Hu teaches that the machine learning model is a neural network model (Hu, [0034-0035], where an LSTM network is trained under a downstream prediction task, i.e., for generating a predicted fixed-length embedding vector. See Hu, [0027], where LSTM networks are artificial recurrent neural networks (RNN) architectures used in the field of deep learning);
[generating a] predicted data structure, including mapping entries within the telemetry log to fields within the predicted data structure, and populating the fields within the predicted data structure with data based on the mapped entries within the telemetry log, wherein the predicted data structure comprises a plurality of fields (Hu, [0034-0037] and [0044], where the system predicts a user embedding (i.e., “predicted data structure”) based on a fixed-length embedding vector converted from a variable-length user behavior matrix from, e.g., a raw activity log (see, e.g., Hu, [FIG. 5] and [0043]) (i.e., “within the telemetry log”), where the user embedding is calculated based on user historical behaviors, the predicted user embedding having a prediction value and a corresponding observation value. See Hu, [0027], where a user behavior record is transformed, enabling the collected data to be used as observation, and thereby estimating the modeling to minimize the loss between the target (i.e., observation) and the prediction (i.e., predicted value). Note that “fixed-length embedding” implies “a plurality of fields” (see, e.g., Hu, [0034], in also discussing a matrix of multiple dimensions, which is then converted into a corresponding fixed-length embedding).
Thus, although Hu does not appear to explicitly state that the predicted values are mapped to an observation, Hu discloses minimizing the loss between the target and the prediction. Therefore, one of ordinary skill in the art would have been suggested to have mapped each of the vector dimensions between the target and predicted values with the motivation of being able to leverage previous history as input for predicting similarly future events (i.e., using the same dimensions)); [and]
identifying [an error] between the predicted data structure and first data structures corresponding to the first user interactions of the user of the client device; and updating, using the predicted data structure, the first data structures based on the identified [error] (Hu, [0044], where the system predicts a user embedding based on a fixed-length embedding vector converted from a variable-length user behavior matrix, and then compares the target behavior (user embedding vector) to the actual behavior to determine the loss (error) in the prediction. See also Hu, [Claims 8-9] and [Claims 14-15], where the system determines an error between the predicted target and actual user behavior, and updates the user behavior data/vector (i.e., “first data structures”) based on the error)).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Shoemaker and Hu (hereinafter “Shoemaker as modified”) with the motivation of (1) utilizing a neural network to enable deep learning of user characteristics represented by embedding (Hu, [0027]), (2) estimating the modeling to minimize the loss between the target and the prediction, thus taking any data as a target and leveraging previous history as an input, thereby not requiring any annotation or labeling, with the potential to be self-learning all from the data (Hu, [0027]), which improves convenience, reduces reliance on (good) datasets, and potentially reduces manual entry errors from labeling; and (3) improving service online computation due to compact user modeling (Hu, [0048]).
Shoemaker as modified does not appear to explicitly teach that discrepancies are identified, i.e., [identifying] discrepancies [between the predicted data structure and the first data structures].
Copper teaches [identifying] discrepancies [between the predicted data structure and the first data structures] (Copper, [0033-0039] and [0067], where the system generates replacement values or invalid or missing values in historical data records used for training a primary (machine learning) model or in new data records introduced to the computing system for processing by a primary (machine learning) model after the model is placed in service, where the replacement model data structure includes information previously placed in the field status data structure 440).
It would have been obvious to one of ordinary skill in the art before the effective filing date of the claimed invention to have combined the teachings of Shoemaker as modified and Copper (hereinafter “Shoemaker as modified”) with the motivation of changing specific elements/attributes for more fine-grained tuning of user-related data, thereby potentially leading to more accurate results in the future.
Although Shoemaker as modified does not appear to explicitly teach the plurality of fields in the predicted data structure “including a duration field indicating a time interval” as claimed, the claimed invention does not distinguish over the prior art because the differences in the claim limitations and the prior art’s disclosure are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The various steps of claim 13 would have been performed the same regardless of the specific data involved (i.e., the predicted data structure comprising a duration field indicating a time interval, or some other data). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 1385, 217 USPQ2d 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art to have referred to Shoemaker as modified’s teachings in making the claimed invention, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention over the prior art.
Regarding claim 14: Shoemaker as modified teaches The method of claim 13, wherein:
the identified discrepancies include an inconsistent field of an existing data structure; and updating the first data structures comprises updating the inconsistent field with a predicted value from the predicted data structure (Copper, [0033-0039] and [0067], where the system generates replacement values or invalid or missing values in historical data records used for training a primary (machine learning) model or in new data records introduced to the computing system for processing by a primary (machine learning) model after the model is placed in service, where the replacement model data structure includes information previously placed in the field status data structure 440).
Regarding claim 15: Shoemaker as modified teaches The method of claim 13, wherein:
the identified discrepancies include a missing data structure; and updating the first data structures comprises storing the predicted data structure with the first data structures (Copper, [0033-0039] and [0067], where the system generates replacement values or invalid or missing values in historical data records used for training a primary (machine learning) model or in new data records introduced to the computing system for processing by a primary (machine learning) model after the model is placed in service, where the replacement model data structure includes information previously placed in the field status data structure 440).
Regarding claim 16: Shoemaker as modified teaches The method of claim 13, wherein:
the first user interactions include reading an email by the user; the first user content includes the email; and the predicted data structure indicates a time period for reading the email (Shoemaker, [0029], where content may be an email message, and the fields can be used as information about content. See also Shoemaker, [0019], where actions include browsing on the Internet. See Shoemaker, [0024-0025], where information about content may be metadata, including timestamps, as well as a time when a webpage is visited by a user).
Although Shoemaker does not appear to explicitly state that the action pertains to reading an email by the user as claimed, or that the fields include a time period for reading the email as claimed, the claimed invention does not distinguish over the prior art because the differences in the claim limitations and the prior art’s disclosure are only found in the nonfunctional descriptive material and are not functionally involved in the steps recited. The various steps of claim 13 would have been performed the same regardless of the specific data involved (i.e., reading an email with a duration field indicating a time period for reading that email as claimed, or some other data). Thus, this descriptive material will not distinguish the claimed invention from the prior art in terms of patentability. See In re Gulack, 703 F.2d 1381, 1385, 217 USPQ2d 401, 404 (Fed. Cir. 1983); In re Lowry, 32 F.3d 1579, 32 USPQ2d 1031 (Fed. Cir. 1994).
Therefore, it would have been obvious to a person of ordinary skill in the art to have referred to Shoemaker’s teachings in making the claimed invention, because such data does not functionally relate to the steps in the method claimed and because the subjective interpretation of the data does not patentably distinguish the claimed invention over the prior art.
Regarding claim 17: Claim 17 recites substantially the same claim limitations as claim 7, and is rejected for the same reasons.
Conclusion
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/IRENE BAKER/Primary Examiner, Art Unit 2154
27 June 2026
1 Frazier et al. US 2002/0120435 A1 at [0002] (“Advantages of artificial neural networks include their ability to learn and their ability to produce relatively more accurate results (than those produced by standard computer systems) despite distortions in input data”).
2 Grayson et al. US 5,111,531 A at [Background] (“The potential advantage of neural nets is that, unlike classification logic, they can work with less than perfect input information”).
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